WO2023114723A2 - Biomarkers for dendritic cells - Google Patents

Abstract

The present disclosure generally relates to, inter alia, methods for characterizing dendritic cells as well as methods for identifying a dendritic cell as either an inflammatory or a tolerogenic dendritic cell.

Description

BIOMARKERS FOR DENDRITIC CELLS

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Patent Application 63/289,104, filed December 13, 2021, which is incorporated herein by reference in entirety and for all purposes.

FIELD

[0002] The present disclosure relates generally to methods for characterizing dendritic cells as well as methods for identifying a dendritic cell as either an inflammatory or a tolerogenic dendritic cell.

BACKGROUND

[0003] Dendritic cells (DC) bridge innate and adaptive immunity through recognition and processing of pathogen and danger-associated signals for orchestrating cytokine-mediated inflammatory responses and priming antigen-specific T cell activation (Qian and Cao, 2017). DC activation and maturation is a highly coordinated response associated with phenotypic and morphologic changes, which enable functional specialization for mounting protective immunity or tolerance to self-antigens (Dalod et al., 2014). The DC maturation process results in upregulation of major histocompatibility complexes (MHC), costimulatory molecules (CD86, CD80, CD40, ICOSL) trafficking receptors (CCR7) and secretion of proinflammatory cytokines (Raker et al., 2015). Emerging research has identified adaptations in cellular metabolism that are central to accommodate energy demands associated with functional changes in transcriptional and biosynthetic pathways necessary for DC survival, migration, effective T cell priming capacity (Thomaz et al., 2018).

[0004] Similar to the Warburg effect in cancer cells, a profound shift from oxidative phosphorylation (OXPHOS) to aerobic glycolysis upon Toll-like receptor (TLR) activation was shown to be central metabolic rewiring in murine bone marrow-derived DCs (BMDCs) (Krawczyk et al., 2010). This immediate-early glycolytic increase within minutes of TLR- stimulus, is controlled by the TBKl-IKKs-Akt signaling axis to activate the rate-limiting glycolytic enzyme hexokinase 2 (HK2), which is essential for supporting de novo synthesis of fatty acids for ER and Golgi expansion (Everts et al., 2014). While TLR-activated DCs become more dependent on extracellular glucose, it was demonstrated that intracellular glycogen stores support the early glycolytic flux and immune functions (Thwe et al., 2017). While the early stages of BMDC activation maintained increased OXPHOS, the onset of sustained glycolytic reprogramming induced iNOS-dependent generation of nitric oxide (NO) from arginine, which blocks mitochondrial electron transport and respiration (Everts et al., 2012). BMDC switch to glycolysis and lactic acid fermentation as a rapid source of ATP and further engage pentose phosphate pathway (PPP) for increased nucleotide biosynthesis and NADPH for generation of reactive oxygen species (ROS) (Kelly and O’Neill, 2015). Together these complex pathways program murine DC’s ability to process and present antigens for proper activation of adaptive immune branches.

[0005] While glycolytic metabolism is a hallmark of murine BMDC activation, this phenomenon does not directly translate to human DC (Amiel et al., 2012; Kelly and O’Neill, 2015; Malinarich et al., 2015; Wculek et al., 2019) and recent evidence suggests that contextspecific metabolic reprogramming governs changes in immature, steady-state, inflammatory activation and initiation of immune tolerance in different microenvironmental and pathophysiological settings (Thomaz et al., 2018; Wculek et al., 2019). Furthermore, diverse metabolic programs and mitochondrial reprogramming underlie cellular fate and function of distinct DC subtypes (Basit et al., 2018). Metabolic differences associated with deregulated OXPHOS, glycolysis and fatty acid oxidation (FAO) programs were also shown to influence anti-inflammatory phenotype of tolerogenic DCs (Sim et al., 2016), which maintain immune tolerance by inhibiting effector and autoreactive T cells, and polarizing development of regulatory T cell (Treg) responses (Ritprajak et al., 2019).

[0006] While providing invaluable insights to the field of immuno-metabolism, the technical limitations of bulk cellular measurements often used to measure metabolic respiration by means of metabolite tracing and/or oxygen consumption (OCR) and extracellular flux analyses (ECAR), are not able to adequately capture the newly-appreciated phenotypic and functional and diversity associated with the heterogeneous nature of in vitro DC culture systems (Helft et al., 2015; Sander et al., 2017). As key regulators of immune homeostasis, monocyte-derived DCs (moDC) have been critical resource for diverse cell therapy applications including priming anti -turn or T- cell responses as cancer vaccines (Santos and Butterfield, 2018), or in the opposing role as tolerogenic (tol-moDC) promoting immune suppression for organ transplantation and autoimmune disease treatment (Marin et al., 2018). Emergence of single-cell approaches using RNA sequencing and high-dimensional mass (cytometry by time of flight, CyTOF) and fluorescent cytometry-based techniques enables robust estimation of immuno-metabolic states of individual cells in the context of heterogeneous cell populations.

[0007] The disclosure provided here provides solutions to the problems existing with previous attempts to characterize different states of dendritic cells.

SUMMARY

[0008] The present disclosure generally relates to, among other things, methods for characterizing a dendritic cell in a subpopulation of dendritic cells as either inflammatory or tolerogenic. As described herein, functional metabolic states and the underlying metabolic protein regulome was mapped with simultaneous immune characterization of inflammatory and tolerogenic monocyte-derived DC differentiation. Novel single-cell energetic metabolism by profiling translation inhibition (SCENITH) (Arguello et al., 2020) and CyTOF-based single-cell metabolic regulome profiling (scMEP) (Hartmann et al., 2021) were coupled to integrate functional measurements with quantifying metabolite transporters and enzymes across major cellular metabolic axes, respectively.

[0009] In one aspect, provided herein is a method of characterizing a dendritic cell in a subpopulation of dendritic cells in a biological sample. The method involves determining two or more of: (a) a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample; (b) an immune profile of the dendritic cell in the sub-population of dendritic cells and a reference biological sample; (c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells, and then characterizing the differentiation state of the dendritic cell.

[0010] In some embodiments, the metabolic profile is determined by measuring mitochondrial dependence, glycolytic capacity, and FAAO. In some embodiments, the method further comprises measuring expression levels of ENO1, GAPDH, LDHA, GLUT1, PFKFB4, MCT1, ATP5A, CS, IDH2, PPARy, CytC, SDHA, CD98, and CD36.

[0011] In some embodiments, the immune profile is determined by measuring expression levels of HLA-DR, CD86, CD206, PD-L1, CD14, CD141, ILT3, and CDlc. [0012] In some embodiments, the sub-population of dendritic cells is monocyte-derived. [0013] In some embodiments, the reference sample comprises CD14+ monocytes.

[0014] In some embodiments, the method further comprises calculating a metabolic score for the dendritic cell in the subpopulation of dendritic cells and a reference biological sample. In some embodiments, the metabolic score comprises a glycolytic score, an oxidative phosphorylation (OXPHOS) score, a fatty acid oxidation (FAO) score, an amino acid (AA) score, a pentose phosphate pathway (PPP) score, and/or a glutathione biosynthesis (GSH) score. In some embodiments, the score is calculated using a method comprising linear regression analysis between scMEP median metabolic marker expression and log-transformed median normalized SCENITH parameters.

[0015] In some embodiments, the differentiation state of the dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased protein synthesis, (ii) increased mitochondrial dependence, (iii) moderate FAAO, (iv) decreased expression levels of ENO 1, GAPDH, LDHA, and (v) increased expression levels of GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2.

[0016] In some embodiments, the differentiation state of the dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the immune profile of the dendritic cell is determined to have one or more of (i) increased expression levels of HLA-DR, CD86, CD206, and PD-L1, and (ii) decreased expression levels of CD14.

[0017] In some embodiments, the differentiation state of the dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the dendritic cell has a decreased ratio of phosphorylated mTOR to phosphorylated AMPK.

[0018] In some embodiments, the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased glycolysis, (ii) increased oxidative phosphorylation, (iii) increased expression levels of MCT1, PFKFB4, LDHA, Cytc, SDHA, CD98, and (iv) decreased expression levels of CD36.

[0019] In some embodiments, the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the immune profile of the dendritic cell is determined to have (i) increased expression levels of CD14, PD-L1, ILT3, and CD141, and (ii) decreased expression levels of HLA-DR, CD86, and CDlc.

[0020] In some embodiments, the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the dendritic cell has an increased ratio of phosphorylated mTOR to phosphorylated AMPK.

[0021] In some embodiments, the biological sample and the reference sample is a blood sample. In some embodiments, the blood sample is derived from a human.

[0022] In another aspect, provided herein is a method of identifying a dendritic cell as an inflammatory dendritic cell. The method involves determining two or more of (a) a metabolic profile of a dendritic cell and a reference sample, wherein said determining comprises (i) measuring one or more levels of protein synthesis, mitochondrial dependence, glycolytic capacity, FAAO, and (ii) measuring one or more expression levels of ENO 1, GAPDH, LDHA, GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2; (b) an immune profile of the dendritic cell and a reference sample, wherein said determining comprises measuring one or more expression levels of HLA-DR, CD86, CD206, PD-L1, and CD14; (c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell and a reference sample. The dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased protein synthesis, (ii) increased mitochondrial dependence, (iii) moderate FAAO, decreased expression levels of ENO1, GAPDH, LDHA, and (iv) increased expression levels of GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2; the immune profile of the dendritic cell is determine to have one or more of (i) increased expression levels of HLA-DR, CD86, CD206, and PD-L1, and (ii) decreased expression levels of CD14; and the dendritic cell has a decreased ratio of phosphorylated mTOR to phosphorylated AMPK.

[0023] In another aspect, provided herein is a method of identifying a dendritic cell as a tolerogenic dendritic cell. The method includes determining two or more of: (a) a metabolic profile of a dendritic cell and a reference sample, wherein said determining comprises (i) measuring one or more levels of glycolysis, oxidative phosphorylation, and (ii) measuring one or more expression levels of LDHA, PFKFB4, MCT1, CD36, Cytc, SDHA, CD98, and PPARy; (b) an immune profile of the dendritic cell and a reference sample, wherein said determining comprises measuring one or more expression levels of HLA-DR, CD86, CDlc, PD-L1, ILT3, CD14, and CD141; and (c) the ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell and a reference sample. The dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have (i) increased glycolysis, (ii) increased oxidative phosphorylation, (iii) increased expression levels of MCT1, PFKFB4, LDHA, Cytc, SDHA, CD98, and (iv) decreased expression levels of CD36; the immune profile of the dendritic cell is determined to have (i) increased expression levels of CD14, PD-L1, ILT3, and CD141, and (ii) decreased expression levels of HLA-DR, CD86, and CDlc; and the dendritic cell has an increased ratio of phosphorylated mTOR to phosphorylated AMPK.

[0024] In some embodiments, the biological sample and the reference sample is a blood sample. In some embodiments, the blood sample is derived from a human. In some embodiments, the dendritic cell is monocyte-derived. In some embodiments, the reference sample comprises CD14+ monocytes. In some embodiments, the method further comprises calculating a metabolic score for the dendritic cell and a reference biological sample. In some embodiments, the metabolic score comprises a glycolytic score, an oxidative phosphorylation (OXPHOS) score, a fatty acid oxidation (FAO) score, an amino acid (AA) score, a pentose phosphate pathway (PPP) score, and/or a glutathione biosynthesis (GSH) score. In some embodiments, the score is calculated using a method comprising linear regression analysis between scMEP median metabolic marker expression and log-transformed median normalized SCENITH parameters. In some embodiments, the dendritic cell is characterized as tolerogenic when the glycolytic score is 2 to 3-fold higher than that of the reference sample.

[0025] In another aspect, provided herein is a method of preparing a dendritic cell vaccine. The method involves culturing dendritic cells in a culture medium comprising one or more of the following: (i) a reduced glucose concentration; (ii) an inhibitor of lactate production; (iii) increased fatty acids; (iv) increased amino acids; and (v) an inhibitor of mTOR, an inhibitor of AMPK, or a combination thereof.

In some embodiments, the culturing occurs in low glucose conditions. In some embodiments, the inhibitor of lactate production is an MCT1 inhibitor. In some embodiments, the MCT1 inhibitor is BAY8002. In some embodiments, the inhibitor of mTOR is rapamycin. In some embodiments, the inhibitor of AMPK is dorsomorphin. the fatty acids comprise one or more of palmitic acid, oleic acid, and linoleic acid. In some embodiments, the dendritic cells are immature dendritic cells. In some embodiments, the dendritic cells are mature dendritic cells.

[0026] In some embodiments, the method further includes characterizing the dendritic cells prior to culturing, the method comprising determining two or more of the following: (a) a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample; (b) an immune profile of the dendritic cell in the subpopulation of dendritic cells in (a) and a reference biological sample; (c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample; and (d) characterizing the differentiation state of the dendritic cell.

[0027] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative embodiments and features described herein, further aspects, embodiments, objects and features of the disclosure will become fully apparent from the drawings and the detailed description and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The features of the present disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:

[0029] FIGs. 1 A-1E demonstrate that distinct metabolic profiles and kinetic changes in mTOR/AMPK signaling axis regulate moDC lineage differentiation. FIG. 1 A is a conceptual overview of in vitro culture conditions and experimental setup for scMEP and SCENITH functional metabolic profiling and immune characterization of moDC differentiation states. FIG. IB shows dimensionality reduction and visual tSNE clustering using immune activation markers of moDC differentiation stages. Expression of immune markers over the course of moDC generation is illustrated in flow-cytometry histograms and tSNE of selected marker single-cell expression heatmap overlays. FIG. 1C is an overview of kinetic changes in percentual SCENITH parameters and protein synthesis measurements across moDC differentiation timeline, with lines representing mean SCENITH profiles are shown (precursor stages represent 3 independent donors, iDC and mDC represent 6 independent donors). FIG. ID shows heatmap and clustering analysis of normalized gMFI expression profiles for DC lineage markers over time (N = 3 donors). Protein synthesis levels, Percentual SCENITH parameters, donor label, and moDC differentiation stages are annotated along with expression profiles for signaling factors and calculated mT0R:AMPK phosphorylation ratio. FIG. IE are bar graphs representing correlation coefficients between SCENITH markers and p-AMPK and p-mTOR expression from combined iDC, 4h and 24h mDC gMFI expression data sets from 3 healthy donors. The dotted line indicates the significance threshold of BH FDR-adjusted-p >= 0.05. Colors represent functional categories of the profiled markers.

[0030] FIGs. 2A-2D show gating strategies and metabolic pathway correlation analyses. FIG. 2 A shows gating strategies used to determine frequencies and early precursor stages for CD14⁺ monocyte (top), 24h post-GM-CSF/IL4 stimulus CD14⁺ HLA-DR^LO (middle) and matured moDC HLA-DR⁺CD86⁺ populations (bottom). Puromycin⁺ populations were selected for downstream analyses. In FIGs. 2B and 2C, shown are (arcsinh transformed) expression values for scMEP immune and metabolic markers across moDC differentiation states. Black dots represent population medians and the dotted line separates early precursors from iDC and mDC stages. Violin plots are representative of 1 donor (out of N = 3). On the right is a graphical summary of upper and lower enzymes in the glycolytic pathway. FIG. 2D shows correlations between median normalized SCENITH mitochondrial dependence, scMEP OXPHOS scores and indicated scMEP pathway scores with Spearman correlation coefficient (R), p-value and grey shading denoting 95% confidence interval (CI). Middle and multi-panel graphs depict single-cell scMEP scores for combined and individual DC sample time points respectively. Subsampled single-cell data points for an individual donor (out of N = 3) are shown.

[0031] FIGs. 3 A-3F show that dynamic changes in metabolic regulome and co-expression of multiple metabolic pathways governs the immune reprogramming of moDC. FIG. 3A is a graphical overview of the scMEP approach depicting metabolic enzymes, signaling factors and metabolite receptors spanning multiple metabolic pathways as well DC lineage markers profiled by CyTOF. In FIG. 3B, shown are (arcsinh transformed) expression values for selected scMEP immune markers across moDC differentiation states. Black dots represent population medians and the dotted line separates early precursors from iDC and mDC stages. Violin plots are representative of 1 donor (out of N = 3). On the right side are represented summary (mean) kinetic expression profiles for all measured immune and metabolic scMEP parameters across moDC differentiation. FIG. 3C shows tSNE clustering analysis of moDC stages using scMEP metabolic markers. Heatmap overlay of single-cell CD14, HLA-DR, CD86 and CDlc (arcsinh transformed) expression highlights associations between DC stage clusters and immune maker expression. Data show one representative experiment (out of N = 3 donors). FIG. 3D shows kinetic profiles of normalized SCENITH parameters (calculated as described in materials and methods) to obtain metabolic pathway-dependent changes accounting for ATP production. Lines highlight mean SCENITH profiles (precursor stages represent 3 independent donors, iDC and mDC represent 6 independent donors). FIG. 3E shows kinetic profiles for calculated mean scMEP pathways scores are illustrated. Connecting lines visualize mean pathway changes (N = 3 donors). FIG. 3F shows correlations between median SCENITH parameters and respective calculated median scMEP pathway scores with Spearman correlation coefficient (R), p-value and grey shading denoting 95% confidence interval (CI). Middle and multi-panel graphs depict single-cell scMEP scores for combined and individual DC sample time points respectively. Subsampled single-cell data points for the individual donor (out of N = 3) are shown.

[0032] FIGs. 4A-4G demonstrate that metabolic heterogeneity associates with phenotypic polarization of CDlc¹¹¹ and CDSb^moDC populations. FIG. 4A shows correlation analysis for single-cell glycolytic glycolysis and OXHOS scores with heatmap expression single-cell overly of indicated immune markers. Subsampled single-cell data points for the individual donor (out of N = 3) are shown for the entire figure. FIG. 4B shows mass cytometry scatter plots for CDlc and CD86 expression profiles were used to emphasize the distribution of CDlc¹¹¹ and CDSb¹¹¹ populations. In FIG. 4C, shown are single-cell scatter plot comparisons of the top 4^th quantiles from CDlc¹¹¹ (blue) and CD86^hi(gold) moDC populations. Lower graphs represent histogram distributions of single-cell scMEP metabolic pathway scores in CDlc¹¹¹ and CDSb¹¹¹ populations. FIG. 4D shows expression values of critical glycolytic enzymes in the 1^st (lowest, black) and 4^th (highest, red) quantile from CDlc and CD86 populations across iDC, 4h and 24h mDC. Adjacent box plots represent median expression values for PDK1 in matching quantiles from 3 independent donors. A role for PDK1 in pyruvate to Acetyl-CoA conversion is depicted underneath the graphs. FIG. 4E shows tSNE analysis from SCENITH profiling depicts clustering of DC stages with CDlc expression heatmap overlay. Adjacent gating strategy was used to select CDlc¹¹¹ and CDSb¹¹¹ populations, whose spatial distribution is emphasized (with matching colors) on tSNE clusters divided into separate iDC, 4h and 24h mDC maturation stages. FIG. 4F shows heatmap analysis of gMFI expression for collection of SCENITH phenotyping markers in CDIc¹¹¹ and CDSb¹¹¹ populations from iDC, 4h and 24h mDC stages (N = 3 donors). Donor label, DC differentiation stages population frequency, protein synthesis levels along with SCENITH percentual metabolic profiles are annotated. FIG. 4G shows comparative analysis of gMFI of SCENITH panel signaling factors in top 4^th quantiles from CD I c¹¹¹ (blue) and CD86^hi(gold) populations across indicated moDC stages (N = 4 donors). Bottom boxplots show changes in calculated phosphorylated (p) p-mTOR:p-AMPK and p AMPK: Total -AMPK (unphosphorylated) ratios. Lines connect data points from individual donors.

[0033] FIGs. 5A-5F show associations between single-cell scMEP metabolic pathway scores and immune phenotypes of moDC differentiation stages. FIG. 5A shows histograms depicting quantile ranges of arcsinh transformed CD86 and CDIc expression profiles in 24h mDC used for downstream subsampling and exploratory analyses. FIG. 5B shows single-cell values for individual CDIc and CD86 markers were divided into four quantiles spanning the lowest (black circles) and highest (red circles) expression levels. Quantiles were overlayed on tSNE plots to depict spatial clustering of CDIc and CD86 expression range in iDC, 4h and 24h mDC. In FIG. 5C, CDIc and CD86 quantiles were overlayed on single-cell scMEP GLYC vs OXPHOS correlation scatterplots to emphasize dichotomous stratification of CDIc¹¹¹ and CD86¹¹¹ moDC populations. In FIG. 5D, shown are histogram distributions of single-cell scMEP metabolic pathway scores in CDIc¹¹¹ (blue) and CDSb¹¹¹ (gold) moDC populations. (E) Expression values of scMEP -profiled enzymes/metabolite transporters and signaling factors from indicated metabolic pathways in the 1^st (lowest, black) and 4^th (highest, red) quantile from CDIc and CD86 populations across iDC, 4h and 24h mDC. Violin plots representing one donor (out of N = 3) are shown. In FIG. 5F, shown are median expression values for PDK1 in the top 4^th quantiles from CDIc¹¹¹ (blue) and CD86^hi (gold) moDC populations across differentiation stages.

[0034] FIGs. 6A-6D show immuno-metabolic profiling, clustering analysis and biomarker determination of tolerogenic moDC. FIG. 6A shows comparative analysis of gMFI of SCENITH DC lineage markers and signaling factors in top 4^th quantiles from CDIc¹¹¹ (blue) and CDSb¹¹¹ (gold) populations across indicated moDC stages (N = 4 donors). Lines connect data points from individual donors. FIG. 6B shows brightfield images (lOx magnification) of morphological differences between control and tolerogenic moDC cultures. Flow cytometry histograms for HLA-DR, CD86 and CD 14 expression changes in control (black), vitd3+dexa (purple) and vitd3 (orange) moDC across maturation stages are also shown. FIG. 6C shows normalized gMFI expression values of SCENITH panel surface receptor profiles in control (black), vitd3+dexa (purple) and vitd3 (orange) treatments across maturation stages for three (color-coded) donors. FIG. 6D shows normalized median arcsinh transformed expression values of scMEP panel surface receptor profiles in control (black), vitd3+dexa (purple) and vitd3 (orange) treatments across maturation stages for three (color-coded) donors.

[0035] FIGs. 7A-7D shows immuno-metabolic analysis of inflammatory and tolerogenic moDC. FIG. 7A are volcano plots that show differential gMFI SCENITH marker expression by fold change (logFC) and BH FDR adjusted p-value (-loglO(BH-adj.p)) comparing vitd3+dexa and vitd3 treated cells from controls at iDC, 4h and 24h mDC stages (N = 3 donors). Colors represent functional categories of the profiled markers. For orientation purposes, horizontal solid lines indicate significance threshold (BH-adj.p = 0.05) with vertical dotted lines marking fold change < 1.5. FIG. 7B are boxplots that represent statistical summaries for kinetic changes and differences in percentual (left panel) and normalized (right panel) SCENITH metabolic parameters between control, vitd3+dexa (purple) and vitd3-treated (orange) moDC across differentiation timeline. Paired /-test was used for statistical analysis (precursor stages represent 3 independent donors, iDC and mDC represent 6 independent donors). FIG. 7C are volcano plots that show differential median scMEP marker expression by fold change (logFC) and BH FDR adjusted p-value (-loglO(BH-adj .p)) comparing vitd3+dexa and vitd3 treated cells from controls at iDC, 4h and 24h mDC stages (N = 3 donors). Colors indicate respective scMEP metabolic pathways. For orientation purposes, horizontal solid lines indicate significance threshold (BH- adj.p = 0.05) with vertical dotted lines marking fold change < 1.5. FIG. 7D shows calculated Gini impurity scores determining the relative importance of metabolic markers discriminating control and tolerogenic-treatments across moDC maturation stages. Single-cell expression data were randomly divided into training and validation groups and metabolic scMEP parameters were used in the random forest model testing. Resulting area under the receiver operating characteristic curves (AUC-ROC) indicates the effectiveness of model performance.

Comparative tSNE visualization of clustering similarities treatment conditions and CD36 singlecell heatmap overlays. [0036] FIGs. 8A-8H shows that Vitd3 and dexamethasone alters metabolic and signaling networks in immune-suppressive phenotypes of tol-moDC. FIG. 8A is a schematic diagram of tolerogenic moDC treatment conditions. Control (black), vitd3+dexa (purple) and vitd3 (orange) cells sampled at distinct stages (iDC, 4h and 24h moDC) were subjected to dimensionality reduction (tSNE) using SCENITH phenotyping panel. Single-cell heatmap overlays highlight associations between maturation stages and expression of indicated immune markers in control and tolerogenic cell clusters are shown. FIG. 8B are boxplots that represent changes in SCENITH puromycin protein synthesis (gMFI puromycin) levels across moDC stages and treatment conditions (N =3 donors). FIG. 8C is an overview of kinetic changes and differences in percentual (left panel) and normalized (right panel) SCENITH metabolic parameters between control, vitd3+dexa (purple) and vitd3-treated (orange) moDC across differentiation timeline. Connecting lines visualize mean pathway changes (precursor stages represent 3 independent donors, iDC and mDC represent 6 independent donors). Statistical analyses are shown in supplemental figure 4B. FIG. 8D shows an integrated clustering heatmap of moDC activation stages based on median arcsinh transformed expression values for scMEP metabolic regulators (N = 3). Bottom heatmap annotations include donor labels, treatment conditions and DC differentiation stages. Fluorescent quantitation of mitochondrial size (Mitotracker Deep Red) along with protein translation/ ATP levels are annotated in the form of a heatmap. Point annotations representing lactate and glucose supernatant measurements were determined in iDC, 4h and 24h moDC stages. Heatmap annotation for DC immune signatures are located at the top of the clustering matrix. scMEP markers are colored according to their metabolic pathway activity. FIG. 8E shows kinetic profiles for calculated median scMEP pathways scores for control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across DC maturation timeline. Connecting lines visualize mean pathway changes (N = 3). Statistical analyses are shown in supplemental figure 5A. FIG. 8F shows glucose and lactate measurements in control and tolerogenic moDC culture supernatants. Of note glucose level measurement increase in the media between d3 and iDC stage is due to media change at day 3. Three technical replicates from 3 donors are presented with error bars indicating standard deviation. Unpaired /-test was used for statistical analysis. FIG. 8G shows gMFI expression values of profiled SCENITH signaling factors in control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across maturation stages are shown (N = 4). Lines connect data points from an individual donor. FIG. 8H shows boxplots that represent changes in calculated p-mTOR:p-AMPK ratios between control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC (N = 5 donors) across maturation timeline. Lines connect data points from an individual donor. Paired /-test was used for statistical analysis.

[0037] FIGs. 9A-9G shows immuno-metabolic profiling of stochastic heterogeneity in control and tolerogenic moDC. FIG. 9A shows statistical comparisons for calculated median scMEP pathways scores between control and tolerogenic treatments (depicted in Figure 4E) across DC maturation timeline (N = 3). Lines connect data points from an individual donor. Paired /-test was used for statistical analysis. FIG. 9B shows kinetic profiles for calculated median upregulated (UP) and constitutive (CON) glycolytic scMEP pathways scores for control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across DC maturation timeline.

Connecting lines visualize mean pathway changes (N = 3 donors). FIG. 9C are bar graphs that represent correlation coefficients between marker gMFI values and mitochondrial and glycolytic SCENITH parameters. To capture overall network co-expression patterns of DC maturation and tolerogenic surface receptors and signaling regulators, correlations were derived from expression data sets with pooled control and tolerogenic samples across all sampled maturation stages (iDC, 4h and 24h mDC). The dotted line indicates the significance threshold of BH FDR-adjusted-p >= 0.05. Colors represent functional categories of the profiled markers. FIG. 9D shows tSNE clustering analyses based on metabolic marker expression of control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across 4h and 24h maturation stages from one donor (out of N = 3 donors) are shown. Heatmap overlay of single-cell scMEP metabolic pathway scores and expression of phenotyping markers are depicted at 4h/24h mDC stage to emphasize both immune and underlying metabolic heterogeneity as well as differences between control and tolerogenic moDC. FIG. 9E shows correlations between median glycolytic scMEP pathway scores and arcsinh transformed median HLA-DR and CD86 values emphasizing clustering of control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across DC stages with Spearman correlation coefficient (R) and grey shading denoting 95% confidence interval (CI) are shown (N = 3 donors). FIG. 9F shows flow cytometry histograms emphasize the decrease in overall protein synthesis levels (as measured by puromycin) in oligomycin-treated samples as compared to controls. FIG. 9G shows dot plots show calculated comparisons of p-mTOR:p-AMPK between control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across maturation timeline. Lines connect data points from an individual donor (N = 3 donors).

[0038] FIGs. 10A-10F shows distinct metabolic states of mitochondrial and glycolytic cell populations exhibit unique immune activation moDC profiles in control and tolerogenic culture conditions. FIG. 10A shows correlations between median glycolysis and OXPHOS scMEP scores emphasizing clustering of control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across differentiation stages with Spearman correlation coefficient (R) and grey shading denoting 95% confidence interval (CI) are shown (N = 3 donors). Underneath are depicted single-cell correlations between glycolysis and OPXHOS scores divided into treatment categories and moDC maturation stages representative of one donor (out of N = 3 donors). Heatmap overlays indicate respective single-cell HLA-DR (arcsinh-transformed) expression values. For comparative purposes, white circles represent median population scMEP OXPHOS scores from 3 donors. FIG. 10B shows tSNE clustering plots based on metabolic marker expression of control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across three maturation stages from one donor (out of N = 3 donors) are shown. Heatmap overlay of singlecell scMEP metabolic pathway scores and expression of DC lineage markers are depicted at 4h mDC stage to emphasize both immune and underlying metabolic heterogeneity including differences between control and tolerogenic moDC. FIG. 10C shows schematics of oligomycin- treated SCENITH samples, which separates cells that can effectively utilize glycolysis (red population) for producing ATP measured by protein synthesis when mitochondrial respiration is inhibited. Puromycin/ protein synthesis histograms represent cells isolated from single oligomycin-treated wells. Control (black), vitd3+dexa (purple) and vitd3 (orange)-cultured samples after oligomycin treatment exhibit glycolytic (red) and mitochondrial-dependent (blue) moDC subsets in a tSNE clustering based on immune markers. Single-cell heatmap expression overlays emphasize differences in surface marker expression between glycolytic and mitochondrial moDC subsets. FIG. 10D shows flow cytometry histogram profiles for differential SCENITH panel markers in glycolytic (red, orange) and mitochondrial (blue, black) populations in control and vitd3-tol-moDC samples. Representative histograms from single donor (out of N = 4 donors) are shown. FIG. 10E shows heatmap analysis of gMFI SCENITH marker profiles in glycolytic and mitochondrial metabolic clusters from control, vitd3+dexa and vitd3 moDC across distinct maturation stages. Mean expression values from three independent donors are presented. Donor label, treatment and DC differentiation stages are annotated along with the calculated mTOR:AMPK phosphorylation ratio. Marker colors represent functional categories. FIG. 10F shows schematics of puromycin/protein synthesis quantile levels in oligomycin-treated SCENITH samples. Dot plots show calculated comparisons of p-mTOR:p-AMPK ratio changes between individual quantiles within respective treatment groups across maturation stages. Lines connect data points from an individual donor.

[0039] FIGs. 11 A-l ID shows high mitochondrial dependence and low glycolytic capacity associates with increased expression of maturation markers HLA-DR⁺CD86⁺ in control but is imbalanced in tolerogenic moDC. FIG. 11 A is a schematic depiction and gating strategy for identifying high, mid, and low HLA-DR⁺CD86⁺ expressing control, vitd3+dexa and vitd3 treated moDC populations across differentiation stages. FIG. 1 IB shows boxplots that represent changes in percentual SCENITH parameters emphasizing changes between high, mid, and low HLA- DR⁺CD86⁺ populations from control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across maturation stages (N = 5 donors). Lines connect data points from an individual donor. Frequencies of DC classes across individual samples are depicted at the top of the panel. FIG. 11C shows correlation analysis between normalized SCENITH parameters and scMEP scores for glycolysis vs OXPHOS pathways are shown. Circle colors denote high, mid, and low HLA-DR⁺CD86⁺ DC classes or treatment conditions as indicated and circle sizes represent frequencies for visual and comparative purposes. FIG. 1 ID demonstrates that integrative heatmap and clustering analysis shows gMFI SCENITH immune marker profiles for high, mid, and low HLA-DR⁺CD86⁺ DC classes in control, vitd3+dexa and vitd3 samples across distinct moDC maturation stages. Red boxes highlight interesting immuno-phenotypic differences between control and tol-moDCs within the same DC classes. Schematics summary for metabolic and phenotypic differences between HLA-DR⁺CD86⁺ DC classes across maturation stages in control and tol-moDCs.

[0040] FIGs. 12A-12C show analysis of scMEP scores in low, mid, and high HLA-DR⁺CD86⁺ inflammatory and tolerogenic moDC populations. FIG. 12A shows gating strategies used to determine the frequency and population selection for high, mid, and low HLA-DR⁺CD86⁺ DC classification. FIG. 12B shows boxplots that represent changes in scMEP metabolic pathway scores emphasizing changes between high, mid, and low HLA-DR⁺CD86⁺ populations from control (black), vitd3+dexa (purple) and vitd3 (orange)-treated moDC across maturation stages (N = 3 donors). Lines connect data points from an individual donor. Paired /-test was used for statistical analysis. FIG. 12C shows statistical comparisons of metabolic scores levels in HLA- DR⁺CD86⁺ high, mid, and low moDC populations between control and tolerogenic treatments across differentiation states (N = 3 donors). Lines connect data points from an individual donor. Paired /-test was used for statistical analysis.

[0041] FIG. 13 is a summary Figure of immunometabolic reprogramming of inflammatory and tolerogenic moDC. A schematic depiction of metabolic and immune changes of inflammatory and tolerogenic moDC is shown.

[0042] FIG. 14 shows a conceptual overview of ex vivo mDC culture conditions with indicated timepoints used for profiling methods used in this study including microarray (Array) Seahorse assay, culture supernatants Luminex assay, glucose and lactate measurements (Gluc/Lact), SCENITH and scMEP.

[0043] FIGs. 15 shows Kaplan-Meier survival analysis of OS and PFS comparing the survival benefits of metabolic profiles (SCENITH) in mDC. log-rank test was used to compare the Kaplan-Meier curves.

[0044] FIGs. 16A-16B show SCENITH immune-metabolic profiling of glycolytic and mitochondrial-dependent mDC populations. FIG. 16A is an integrated clustering heatmap of median MFI expression for collection of SCENITH phenotyping markers (marker/antibody information is available in Table 3) in mDC from heathy donor (HD, n=3), good (PR/SD/NED1, n=13) and bad (PD/NED2, n=17) outcome groups. HD and patient response indications and absence (No) or presence (Yes) of patient-derived melanoma antigen (MA)-specific CD8, CD4, combined CD8+CD4 IFN-y T cell responses, as defined in materials and methods, are annotated. SCENITH percentual metabolic profiles are represented as bar graphs on top of the heatmap. FIG. 16B shows a protein synthesis histogram that represents puromycin MFI profile for cultured mDC, which were treated with oligomycin. Protein synthesis profiles in oligomycin samples were binned into 4 quantiles, which represent metabolic states of mDC ranging from glycolytic (red population) to mitochondrial-dependent (blue) populations. Bar graphs represent proportions of cells within each oligomycin quantile within clinical response group. Box plots represent differences in expression of median MFI expression profiles for signaling and immune- phenotyping markers in HD and melanoma mDC among oligomycin quantiles. Each oligomycin quantile contains heathy donor (HD, n=3), good (PR/SD/NED1, n=13) and bad (PD/NED2, n=17) samples.

[0045] FIGs. 17A-17E show metabolic regulome profiling by scMEP with glucose and lactate measurements. FIG. 17A is a heatmap of HD and melanoma iDC and mDC based on median arcsinh transformed expression values for metabolic scMEP markers. Bottom heatmap annotations include DC stages and clinical groups. Quantitation of protein synthesis levels, point annotations representing lactate and glucose supernatant measurements and expression values for DC immune signatures are displayed in the top annotations. Row annotations represent classes of scMEP markers within respective metabolic pathways. FIG. 17B shows box plots that represent differences in expression of median scMEP expression profiles for metabolic markers in mDC between heathy donor (HD, n=3), good (PR/SD/NED1, n=13) and bad (PD/NED2, n=17) clinical groups. FIG. 17C shows median scMEP marker expression stratified by absence (No) or presence (Yes) of positive CD8 and combined CD8+CD4 IFN-y T cell responses specific to melanoma antigens. FIG. 17D shows glucose and lactate measurements from DC culture supernatants in heathy donor (HD, n=3), good (PR/SD/NED1, n=13) and bad (PD/NED2, n=17) clinical groups. Of note glucose level measurement increase in the media between d3 and iDC stage is due to media change at day 3. Three technical replicates from 3 donors are presented with error bars indicating standard deviation. Multiple comparisons were calculated via one-way ANOVA with Tukey’s post-hoc test. FIG. 17E shows Kaplan-Meier survival analysis of OS with indicated log-rank test comparing the inferior survival benefits of increased lactate in supernatants from melanoma patient-derived iDC. Multi-group comparisons in (FIGs. 17C-17E) were tested by one-way ANOVA with Tukey’s post-hoc test. In FIG. 17D, Shapiro-Wilk test was used to assess data normality, Wilcoxon signed-rank test (non-normal data) and Student’s t- test (normal data) was used for statistical analysis.

[0046] FIG. 18 shows the human Checkpoint 14-plex and immune profiling 65-plex assay kit (Thermo-Fisher ProcartaPlex) were used to measure immune-modulatory molecules in mDC culture supernatants from 4 healthy donors and 27 melanoma patients. Row labels include HD and patient response indications and absence (No) or presence (Yes) of patient-derived (MA)- specific CD8, CD4, combined CD8+CD4 IFN-y T cell responses. P-values and 95% confidence intervals indicated. [0047] FIGs. 19A-19C show clinical correlations for immune and metabolic phenotypes of circulating monocyte/myeloid and DC populations from melanoma patients. FIG. 19A is an integrated clustering heatmap of median MFI expression profiles for circulating myeloid/DC subtype populations profiled by SCENITH (marker/antibody information is available in Table 4). Percentual metabolic parameters are shown underneath, with response groups and population labels presented on the top of the heatmap. FIG. 19B shows box plots that represent differences in expression of median scMEP expression profiles for metabolic markers in myeloid/DC populations between good and bad clinical groups. Statistical significance between outcome groups was determined using Student’s t-tests. FIG. 19C shows Univariate Cox regression analyses for marker expression levels and overall and progression free survival. P-values and 95% confidence intervals indicated.

[0048] FIGs. 20A-20B show profiling the effects of metabolic states on immune phenotypes of circulating monocyte/myeloid and DC populations in HD and melanoma patients. FIG. 20A shows box plots that represent differences in SCENITH metabolic parameters in circulating myeloid/DC subtypes between heathy donor (HD, n=3) vs. good (PR/NED1, n=5), stable (SD, n=8) and bad (PD/NED2, n=17) response groups. Pairwise comparisons against a HD reference group in were calculated using Student’s t-test with Holm-Bonferroni correction. FIG. 20B shows box plots that represent comparisons of median MFI expression profiles for circulating myeloid/DC subtype populations between glycolytic (red) and mitochondrial-dependent (blue) oligomycin quantiles. Statistical significance between outcome groups was determined using Student’s t-tests.

[0049] FIGs. 21 A-21D show distinct metabolic profiles regulate in vitro DC-lineage differentiation and blood DC. FIG. 21 A shows percentual SCENITH comparisons between iDC and mDC including Etomoxir and CD-839-derived parameters are shown (bar graphs represent 3 independent replicates from 1 donor with mean ± SE). PyrO abbreviates proteins synthesis due to pyruvate oxidation. Statistical significance is D using two-sided Student’s t-test. For all panels, P-values are represented as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. p- values < 0.05 were considered statistically significant (ns). FIG. 21B shows flow cytometry histograms for Puromycin, HLA-DR and CD86 expression changes in control DC treated with indicated metabolic inhibitors. FIG. 21C shows bar graphs with mean±SE represent gMFI of Puromycin expression changes in control and metabolic inhibitor samples (bar graphs represent 3 independent replicates from 1 donor). Statistical significance was calculated using two-sided Student’s t-test. FIG. 2 ID shows gating strategies for immune characterization and percentual SCENITH profiles for freshly isolated blood monocytes and DC populations from 3 independent donors with mean ± SE. Statistical significance was calculated via one-way ANOVA with Tukey’s posthoc test.

[0050] FIGs. 22A-22C show blockade of lactate transport via MCT1 reduces tolerogenic phenotype of Vitd3-tol-DC. FIG. 22A shows bar graphs with mean ± SE represent gMFI expression values. FIG. 22B shows box plots that represent glucose and lactate measurements for control and vitd3-tol mDC treated with Vehicle (DMSO), Rapamycin (1 pM), Dorsomorphin (3.75 pM) and BAY8002 (80 pM) (N = 3). Diagrams of pathway inhibitor targets and timeline for inhibitor treatment are depicted. FIG. 22C shows volcano plots that show differential median scMEP marker expression by fold change (logFC) and BH FDR adjusted p-value (-logl0(BH- adj.p)) comparing vitd3+dexa and vitd3 treated cells from controls at iDC, actDC, and mDC stages (N = 3 donors). Colors indicate respective scMEP metabolic pathways. For orientation purposes, horizontal solid lines indicate significance threshold (BH-adj.p = 0.05) with vertical dotted lines marking fold change < 1.5.

[0051] FIGs. 23 A-23B show Rapamycin and Dorsomorphin functionally inhibit mTOR and AMPK signaling. FIG. 23 A shows bar graphs with mean±SE represent % viability frequencies in of DC cultured in the presence of Vehicle (DMSO), Rapamycin (1 pM), Dorsomorphin (3.75 pM) and BAY8002 (80 pM) (N = 3). FIG. 23B shows box plots represent gMFI expression/phosphorylation of signaling factors and their indicated calculated rations in Control and Rapamycin (1 pM) samples treated with LPS/fFNy for 30 minutes from 3 independent donors.

DETAILED DESCRIPTION OF THE DISCLOSURE

[0052] The present disclosure generally relates to, among other things, methods of characterizing dendritic cells, as well as methods of identifying dendritic cells as being either inflammatory dendritic cells or tolerogenic dendritic cells. The ablility to accurately characterize and identify dendritic cells has been hampered partly by the fact that it has been found that context-specific metabolic reprograming governs changes in immature, steady state, inflammatory activation and initiation of immune tolerance in different microenvironmental and pathophysiological settings (Thomaz et al., 2018; Wculek et al., 2019). Furthermore, diverse metabolic programs and mitochondrial reprograming underlie cellular fate and function of distinct DC subtypes (Basit et al., 2018). Thus, the natural complexity of dendritic cell types has proved to be a challenge for studying them in detail. As described below, the methods disclosed herein offer unique single-cell approaches for characterizing and identifying these complex populations of cells. Further, as described herein, both bulk and single cell metabolic profiling of melanoma patient DC was performed and metabolic skewing and increased glycolysis which impacts overall survival in melanoma patients receiving ex vivo DC vaccines was identified. The baseline metabolic state of circulating monocyte and DC subsets was also determined in these patients and similar metabolic dysfunction was determined. Thus, these data provide insight that the cancer state induces skewed myeloid cell metabolism, and that ex vivo culture and maturation of such monocytes to DC vaccines by current approaches may not fully reconstitute the optimal balanced cellular metabolic activity, nor the immune stimulatory phenotype of DC. [0053] In the following detailed description, the illustrative alternatives described in the detailed description and claims are not meant to be limiting. Other alternatives may be used and other changes may be made without departing from the spirit or scope of the subject matter presented here. It will be readily understood that the aspects, as generally described herein, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated and make part of this application.

DEFINITIONS

[0054] The singular form “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a cell” includes one or more cells, including mixtures thereof. “A and/or B” is used herein to include all of the following alternatives: “A”, “B”, “A or B”, and “A and B.”

[0055] The terms “cell”, “cell culture”, and “cell line” refer not only to the particular subject cell or cell line but also to the progeny or potential progeny of such a cell, cell culture, or cell line, without regard to the number of transfers or passages in culture. It should be understood that not all progeny are exactly identical to the parental cell. This is because certain modifications may occur in succeeding generations due to either mutations (e.g., deliberate or inadvertent mutations) or environmental influences e.g., methylation or other epigenetic modifications), such that progeny may not, in fact, be identical to the parent cell, but are still included within the scope of the term as used herein, so long as the progeny retain the same functionality as that of the original cell, cell culture, or cell line.

[0056] The term “characterizing” as used herein in relation to cells includes describing the distinguishing qualities of the cells. Included within this definition are the terms “identifying” and “enumerating”.

[0057] As used herein “endogenous” refers to any material from or produced inside an organism, cell, tissue or system.

[0058] As used herein, a “subject” or an “individual” includes animals, such as human (e.g., human subject) and non-human animals. In some embodiments, a “subject” or “individual” is a patient under the care of a physician. Thus, the subject can be a human patient or a subject who has, is at risk of having, or is suspected of having a disease of interest (e.g., cancer) and/or one or more symptoms of the disease. The subject can also be a subject who is diagnosed with a risk of the condition of interest at the time of diagnosis or later. The term “non-human animals” includes all vertebrates, e.g., mammals, e.g., rodents, e.g., mice, non-human primates, and other mammals, such as e.g., sheep, dogs, cows, chickens, and non-mammals, such as amphibians, reptiles, etc.

[0059] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

[0060] All ranges disclosed herein also encompass any and all possible sub-ranges and combinations of sub-ranges thereof. Any listed range can be recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, and so forth. As will also be understood by one skilled in the art all language such as “up to”, “at least”, “greater than”, “less than”, and the like include the number recited and refer to ranges which can be subsequently broken down into sub-ranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 articles refers to groups having 1, 2, or 3 articles. Similarly, a group having 1-5 articles refers to groups having 1, 2, 3, 4, or 5 articles, and so forth.

[0061] It is appreciated that certain features of the disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination. All combinations of the embodiments pertaining to the disclosure are specifically embraced by the present disclosure and are disclosed herein just as if each and every combination was individually and explicitly disclosed. In addition, all sub-combinations of the various embodiments and elements thereof are also specifically embraced by the present disclosure and are disclosed herein just as if each and every such sub- combination was individually and explicitly disclosed herein.

DENDRITIC CELLS

[0062] Dendritic cells (DC) are considered the most efficient antigen presenting cells (APC), uniquely able to initiate, coordinate, and regulate adaptive immune responses. Though their ability to capture, process and present antigens is considered their main characteristic, their phenotypic heterogeneity is striking and very different consequences can come from their action. Human DC are identified by their high expression of major histocompatibility complex (MHC) class II molecules (MHC-II) and of CD11c, both of which are found on other cells, like lymphocytes, monocytes and macrophages (Patente et al., “Human Dendritic Cells: Their Heterogeneity and Clinical Application Potential in Cancer Immunotherapy,” Front. Immunol. 9:3176 (2019)). DC express many other molecules which allow their classification into various subtypes.

[0063] DC precursors migrate from bone marrow and circulate in the blood to specific sites in the body, where they mature. This trafficking is directed by expression of chemokine receptors and adhesion molecules. Upon exposure to antigen and activation signals, the DCs are activated, and leave tissues to migrate via the afferent lymphatics to the T cell rich paracortex of the draining lymph nodes. The activated DCs then secrete chemokines and cytokines involved in T cell homing and activation, and present processed antigen to T cells. This link between DC traffic pattern and functions has led to the investigation of the chemokine responsiveness of DCs during their development and maturation. Chemokines are a subclass of cytokines, which have distinct structural features and biological effects. Their primary activity appears to be on the chemotaxis of leukocytes. All chemokines bind to members of a G-protein coupled serpentine receptor superfamily that span the leukocyte cell surface membrane seven times (7-TM). A review of known chemokines may be found in Rossi (2000) Annual Review of Immunology 18:217-42. For a review of the effect of chemokines on DC subsets, see Dieu-Nosjean (1999) J. Leuk. Biol. 66(2):252-62.

[0064] DCs mature by upregulating costimulatory molecules (CD40, CD80 and CD86), and migrate to T cell areas of organized lymphoid tissues where they activate naive T cells and induce effector rather than tolerogenic immune responses. In the absence of such inflammatory or infectious signals, however, DCs present self-antigens in secondary lymphoid tissues for the induction and maintenance of self-tol erance. The ability of DCs to induce tolerance has led to numerous studies using these cells therapeutically in an effort to control unwanted immune responses.

METHODS OF THE DISCLOSURE

Methods for characterizing a dendritic cell

[0065] In one aspect, provided herein are methods for characterizing a dendritic cell in a subpopulation of dendritic cells in a biological sample. The methods involve determining two or more of the following: (a) a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample; (b) an immune profile of the dendritic cell in the sub-population of dendritic cells in and a reference biological sample; (c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample; and (d) characterizing the differentiation state of the dendritic cell.

[0066] As used herein, dendritic cells can be any member of a diverse population of morphologically similar cell types found in lymphoid or non-lymphoid tissues. Dendritic cells are a class of “professional” antigen presenting cells and have a high capacity for sensitizing MHC-restricted T cells. Dendritic cells can be recognized by function, or by phenotype, particularly by cell surface phenotype. These cells are characterized by their distinctive morphology, intermediate to high levels of surface MHC-class II expression and ability to present antigen to T cells, particularly to naive T cells (Steinman et al. (1991) Ann. Rev. Immunol. 9:271; incorporated herein by reference for its description of such cells). The dendritic cells affected by the methods of the invention can be selected to be immature or mature dendritic cells.

[0067] In one embodiment, the sub-population of dendritic cells in the method described herein, is monocyte-derived.

[0068] A reference sample can be a sample used for determining a standard range for a level of a certain metabolic activity or protein expression. Reference sample can refer to an individual sample from an individual reference subject (e.g., a normal (healthy) reference subject or a disease reference subject), who may be selected to closely resemble a test subject by age and gender. Reference sample can also refer to a sample including pooled aliquots from reference samples for individual reference subjects. The reference sample can be a blood sample. In one embodiment, the reference sample comprises CD14+ monocytes.

[0069] A biological sample for use in the methods described herein includes reference to any sample of biological material derived from an animal such as, blood, for example, whole peripheral blood, cord blood, foetus blood, bone marrow, plasma, serum, urine, cultured cells, saliva or urethral swab, lymphoid tissues, for example tonsils, peyers patches, appendix, thymus. In one embodiment, the biological sample is a blood sample. In one embodiment, the blood sample is derived from a human.

[0070] The biological sample which is tested according to the method of the present disclosure may be tested directly or may require some form of treatment prior to testing. For example, a biopsy sample may require homogenization to produce a cell suspension prior to testing.

Furthermore, to the extent that the biological sample is not in liquid form (for example, it may be a solid, semi-solid or a dehydrated liquid sample), it may require the addition of a reagent, such as a buffer, to mobilize the sample. The mobilizing reagent may be mixed with the biological sample prior to placing the sample in contact with the one or more immunointeractive molecules or the reagent may be applied to the sample after the sample has been placed in contact with the one or more immunointeractive molecules. [0071] As described above, the methods involve determining two or more of the following: (a) a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample; (b) an immune profile of the dendritic cell in the subpopulation of dendritic cells in and a reference biological sample; (c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample.

[0072] Thus, a person of skill in the art would understand that one could determine (a) and (b), (a) and (c), (b) and (c), or (a), (b), and (c). Accordingly, in one embodiment, a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample is determined along with an immune profile of the dendritic cell in the subpopulation of dendritic cells in and a reference biological sample. In another embodiment, a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample is determined along with a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample. In another embodiment, an immune profile of the dendritic cell in the subpopulation of dendritic cells in and a reference biological sample is determine along with a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample. In another embodiment, a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample, an immune profile of the dendritic cell in the sub-population of dendritic cells in and a reference biological sample, and a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample are determined.

[0073] The metabolic profile of a cell can be determined using several different methods in the art including, but not limited to, CyTOF (e.g., scMEP), Mass Spectrometry Imaging (MSI), Seahorse ®, and SCENITH ™. scMEP is an approach that utilizes antibody-based assays to analyze metabolic regulation in combination with cellular identity on the single-cell level (Hartmann et al). MSI is a technique which visualizes the spatial distribution of molecules, including metabolites. Seahorse uses metabolic inhibitors (i.e. 2-Deoxy-D-Glucose/”DG” and Oligomycin A/”O”) while monitoring the extracellular acidification rate (ECAR), as well as oxygen consumption rate (OCR). SCENITH™ is a fluorescent-based technique, which measures changes in protein synthesis (as a surrogate energy-output readout) upon selective metabolic pathway inhibition. However, there have been inherent limitations to each of these techniques individually, including a large requirement for purified cells, high concentrations of substrates, cell sorting, and incubation with cell culture media that can change the metabolic activity of cells. Thus, there is need for improved techniques for characterizing cells at the single-cell level, in small quantities from a heterogenous sample, while not altering metabolic activity.

[0074] As described in the Examples provided herein, SCENITH™ and scMEP techniques were combined, and unexpectedly, it was found that combining these two techniques enable one to determine functionally (inhibitor-based) the cellular metabolic pathway requirements as well as the expression levels of the metabolic regulators within each respective metabolic pathway. This is an important advancement because it has not been well understood to which degree expression of particular enzyme in metabolic pathway predicts the activity of that particular pathway. Here, combination of the techniques led to better understanding of which markers predict the best specific metabolic pathway activity in monocyte-derived dendritic cells. Those markers were then selected for the calculated metabolic scores. Of great importance, these measurements were perfomed at a single cell level, which is critical for interrogating diverse and heterogeneous populations and cell types that are present in low frequencies.

[0075] In one embodiment, the metabolic profile is determined by measuring mitochondrial dependence, glycolytic capacity, and FAAO.

[0076] As used herein, mitochondrial dependence is the inability of a dendritic cell to produce energy without energetic mitochondrial pathways.

[0077] As used herein, glycolytic capacity refers to the ability of cells to produce energy when all other pathways, but not glycolysis, are inhibited.

[0078] Fatty acid and amino acid oxidation capacity (FAAO) indicates the ability of cells to utilize fatty acids and amino acids (AA) as an ATP source during blockade of glucose oxidation. [0079] Mitochondrial dependence, glycolytic capacity, and FAOO can all be measured using SCENITH in the methods described herein. Briefly, the biological sample is contacted with metabolic inhibitors followed by an amount of puromycin. Intracellular staining of puromycin and protein targets by contacting the biological sample with antibodies is then performed. The antibodies are typically conjugated with a detectable label.

[0080] Suitable detectable labels include, for example, a heavy metal, a fluorescent label, a chemiluminescent label, an enzyme label, a bioluminescent label or colloidal gold. Methods of making and detecting such detectably-labeled immunoconjugates are well-known to those of ordinary skill in the art, and are described in more detail below.

[0081] In some embodiments, the antibodies are labeled with a fluorescent compound. The presence of a fluorescently-labeled antibody is determined by exposing the immunoconjugate to light of the proper wavelength and detecting the resultant fluorescence. Particular examples of detectable labels include fluorescent molecules (or fluorochromes). Numerous fluorochromes are known to those of skill in the art, and can be selected, for example from Life Technologies (formerly Invitrogen), e g., see, The Handbook — A Guide to Fluorescent Probes and Labeling Technologies). Examples of particular fluorophores that can be attached (for example, chemically conjugated) to a nucleic acid molecule (such as a uniquely specific binding region) are provided in U S Pat. No. 5,866, 366 to Nazarenko et al., such as 4-acetamido-4'- isothiocyanatostilbene-2,2' disulfonic acid, acridine and derivatives such as acridine and acridine isothiocyanate, 5-(2'-aminoethyl) aminonaphthalene- 1 -sulfonic acid (EDANS), 4- amino -N- [3 vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate (Lucifer Yellow VS), N-(4- anilino-1- naphthyl)mal eimide, antllranilamide, Brilliant Yellow, coumarin and derivatives such as coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4- trifluoromethylcouluarin (Coumarin 151); cyanosine; 4',6-diaminidino-2-phenylindole (DAP I); 5',5"dibromopyrogallol-sulfonephthalein (Bromopyrogallol Red); 7 -diethylamino -3 (4'- isothiocyanatophenyl)-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4'- diisothiocyanatodihydro-stilbene-2,2'-disulfonic acid; 4,4'-diisothiocyanatostilbene-2,2'- disulforlic acid; 5-[dimethylamino] naphthalene- 1-sulfonyl chloride (DNS, dansyl chloride); 4- (4'-dimethylaminophenylazo)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl- d'isothiocyanate (DABITC); eosin and derivatives such as eosin and eosin isothiocyanate; erythrosin and derivatives such as erythrosin B and erythrosin isothiocyanate; ethidium; fluorescein and derivatives such as 5-carboxyfluorescein (FAM), 5-(4,6diclllorotriazin-2- yDaminofluorescein (DTAF), 2'7'dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein, fluorescein isothiocyanate (FITC), and QFITC Q(RITC); 2',7'-difluorofluorescein (OREGON GREEN®); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4- methylumbelliferone; ortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B- phycoerythrin; o-phthaldialdehyde; pyrene and derivatives such as pyrene, pyrene butyrate and succinimidyl 1 -pyrene butyrate; Reactive Red 4 (Cibacron Brilliant Red 3B-A); rhodamine and derivatives such as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, rhodamine green, sulforhodamine B, sulforhodamine 101 and sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives. Other suitable fluorophores include thiol -reactive europium chelates which emit at approximately 617 mn (Heyduk and Heyduk, Analyt. Biochem. 248:216-27, 1997; J. Biol. Chem. 274:3315-22, 1999), as well as GFP, LissamineTM, diethylaminocoumarin, fluorescein chlorotriazinyl, naphthofluorescein, 4,7-dichlororhodamine and xanthene (as described in U S. Pat. No. 5,800,996 to Lee et al.) and derivatives thereof. Other fluorophores known to those skilled in the art can also be used, for example those available from Life Technologies (Invitrogen; Molecular Probes (Eugene, Oreg.)) and including the ALEXA FLUOR® series of dyes (for example, as described in U.S. Pat. Nos. 5,696,157, 6, 130, 101 and 6,716,979), the BODIPY series of dyes (dipyrrometheneboron difluoride dyes, for example as described in U.S. Pat. Nos. 4,774,339, 5,187,288, 5,248,782, 5,274,113, 5,338,854, 5,451,663 and 5,433,896), Cascade Blue (an amine reactive derivative of the sulfonated pyrene described in U.S. Pat No. 5,132,432) and Marina Blue (U.S. Pat. No 5,830,912). The conjugation of the label to monoclonal antibodies can be accomplished using standard techniques known to the art. Typical methodology in this regard is described by Kennedy et al., Clin. Chim. Acta 70: 1, 1976; Schurs et al., Clin. Chim. Acta 81 : 1, 1977; Shih et al, Int'l J. Cancer 46: 1101, 1990; Stein et al., Cancer Res. 50: 1330, 1990; and Coligan, supra.

[0082] Data acquisition can then be performed by flow cytometry. Flow cytometry is a well- accepted tool in research that allows a user to rapidly analyze and sort components in a sample fluid. Flow cytometers use a carrier fluid (e.g., a sheath fluid) to pass the sample components, substantially one at a time, through a zone of illumination. Each sample component is illuminated by a light source, such as a laser, and light scattered by each sample component is detected and analyzed. The sample components can be separated based on their optical and other characteristics as they exit the zone of illumination. Said methods are well known in the art. For example, fluorescence activated cell sorting (FACS) may be therefore used and typically involves using a flow cytometer capable of simultaneous excitation and detection of multiple fluorophores. The cytometric systems may include a cytometric sample fluidic subsystem, as described below. In addition, the cytometric systems include a cytometer fluidically coupled to the cytometric sample fluidic subsystem. Systems of the present disclosure may include a number of additional components, such as data output devices, e.g., monitors, printers, and/or speakers, data input devices, e.g., interface ports, a mouse, a keyboard, etc., fluid handling components, power sources, etc. Preferred methods typically involve the permeabilization of the cells preliminary to flow cytometry. Any convenient means of permeabilizing cells may be used in practicing the methods.

[0083] In some embodiments, the metabolic profile is further analyzed by measuring expression levels of ENO1, GAPDH, LDHA, GLUT1, PFKFB4, MCT1, ATP5A, CS, IDH2, PPARy, CytC, SDHA, CD98, and CD36. Such analysis can be performed using scMEP, as described herein, to quantify the expression of phenotypic markers in conjunction with ratelimiting metabolic enzymes, metabolite transporters and signaling factors encompassing several metabolic pathways. Briefly, the biological sample can be incubated with small molecules to assess biosynthesis rates of DNA, RNA, and protein. Metabolic antibodies are then contacted with the biological sample, and cells are acquired on a CyTOF2 mass cytometer.

[0084] As described above, the dendritic cells can also be characterized by measuring an immune profile the dendritic cell. Both SCENITH and scMEP technologies allow for simultaneous analysis of markers for analyzing immune properties of cultured cells.

[0085] In some embodiments, the immune profile is determined by measuring expression levels of HLA-DR, CD86, CD206, PD-L1, CD14, CD141, ILT3, and CDlc. This can be performed, for example, through a dimensionality reduction approach. Briefly, the single cell expression matrix of these immune parameters, which is comprosed of mixed samples (time points, control and tolerogenic samples) can be subjected to dimensionality reduction clustering analysis, which clusters cells based on similar features (i.e., degree of expression). This enables detection, in an unbiased way, which are the unique features (immune profiles) of individual cell clusters from different differentiation stages as well as control versus tolerogenic treatments. This provides an analysis of which features (i.e., specific immune markers) are the best at separating the clusters and inversely which are are not as important discriminating immune factors between cell states or treatments.

[0086] Alternatively, any suitable method may be used to analyze the biological sample in order to determine the immune profile. Suitable methods include, but are not limited to, chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbant assay (ELISA), antibody linkage, other immunochemical techniques, and combinations thereof.

[0087] As described above, the dendritic cells can also be characterized by determining a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample. As demonstrated in the Examples provided herein, a SCENITH panel can include quantification of total and phosphorylated forms of critical signaling factors.

[0088] As discussed in detail in the Examples provided herein, mTOR is an important upstream activator of glycolytic reprogramming driving high metabolic demands of TLR- activated murine macrophages and DCs (Zhou et al., 2018). In contrast, activation of AMPK opposes mTOR dependent glycolytic reprogramming, skewing cellular metabolism towards energy conservation driving mitochondrial biogenesis. Thus, changes in dendritic cells throughout the differentiation process may be replected partially in the temporal alterations in mTOR and AMPK phosphorylation level, which result in modal changes in overall p-mTOR:p- AMPK ratio. Determination of a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell can be achieved using SCENITH as described herein.

[0089] In one embodiment, the method described herein can further include calculating a metabolic score for the dendritic cell in the subpopulation of dendritic cells and a reference biological sample. The metabolic score, as used herein, can be calculated from the integration of the SCENITH functional parameters with scMEP co-expression patterns. The metabolic scores can be calculated using the linear relationship between log-transformed SCENITH-derived metabolic parameter for a specific pathway (i.e., the glycolytic capacity) and expression values of metabolic enzymes within that pathway (i.e., all glycolytic enzymes/transporters) measured by scMEP. In the case of PPP and GSH scores, expression values of the underlying enzymes can be used to derive the scores for those 2 pathways. The resulting metabolic score represents metabolic pathway activation and can be calculated throughout dendritic cell differentiation. In some embodiments, the metabolic score comprises a glycolytic score, an oxidative phosphorylation score (OXPHOS), a fatty acid oxidation (FAO) score, an amino acid (AA) score, a pentose phosphate pathway (PPP) score, and/ or a glutathione biosynthesis (GSH) score. In some embodiments, the score is calculated using a method comprising linear regression analysis between scMEP median metabolic marker expression and log-transformed median normalized SCENITH parameters.

[0090] Based on the results of determining two or more of the following: (a) a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample; (b) an immune profile of the dendritic cell in the sub-population of dendritic cells in and a reference biological sample; and (c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample, the differentiation state of the dendritic cell can be characterized.

[0091] In one embodiment, the differentiation state of the dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased protein synthesis, (ii) increased mitochondrial dependence, (iii) moderate FAAO, (iv) decreased expression levels of ENO 1, GAPDH, LDHA, and (v) increased expression levels of GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2.

[0092] In one embodiment, the differentiation state of the dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the immune profile of the dendritic cell is determined to have one or more of (i) increased expression levels of HLA-DR, CD86, CD206, and PD-L1, and (ii) decreased expression levels of CD14.

[0093] In one embodiment, the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased glycolysis, (ii) increased oxidative phosphorylation, (iii) increased expression levels of MCT1, PFKFB4, LDHA, Cytc, SDHA, CD98, and (iv) decreased expression levels of CD36.

[0094] In one embodiment, the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the immune profile of the dendritic cell is determined to have (i) increased expression levels of CD14, PD-L1, ILT3, and CD141, and (ii) decreased expression levels of HLA-DR, CD86, and CDlc.

[0095] In one embodiment, the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the dendritic cell has an increased ratio of phosphorylated mTOR to phosphorylated AMPK. Methods of identifying a dendritic cell

[0096] In another aspect, provided herein are methods for identifying a dendritic cell as an inflammatory dendritic cell. The method includes (a) determining a metabolic profile of a dendritic cell and a reference sample, wherein said determining comprises (i) measuring one or more levels of protein synthesis, mitochondrial dependence, glycolytic capacity, FAAO, and (ii) measuring one or more expression levels of ENO1, GAPDH, LDHA, GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2; (b) determining an immune profile of the dendritic cell and a reference sample, wherein said determining comprises measuring one or more expression levels of HLA- DR, CD86, CD206, PD-L1, and CD14; (c) determining a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell and a reference sample; (d) characterizing the dendritic cell as inflammatory when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased protein synthesis, (ii) increased mitochondrial dependence, (iii) moderate FAAO, decreased expression levels of ENO1, GAPDH, LDHA, and (iv) increased expression levels of GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2; the immune profile of the dendritic cell is determine to have one or more of (i) increased expression levels of HLA-DR, CD86, CD206, and PD-L1, and (ii) decreased expression levels of CD14; and the dendritic cell has a decreased ratio of phosphorylated mTOR to phosphorylated AMPK.

[0097] In another aspect, provided herein are methods for identifying a dendritic cell as a tolerogenic dendritic cell. The method includes (a) determining a metabolic profile of a dendritic cell and a reference sample, wherein said determining comprises (i) measuring one or more levels of glycolysis, oxidative phosphorylation, and (ii) measuring one or more expression levels of LDHA, PFKFB4, MCT1, CD36, Cytc, SDHA, CD98, and PPARy; (b) determining an immune profile of the dendritic cell and a reference sample, wherein said determining comprises measuring one or more expression levels of HLA-DR, CD86, CDlc, PD-L1, ILT3, CD14, and CD141; (c) determining the ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell and a reference sample; (d) characterizing the dendritic cell as tolerogenic when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have (i) increased glycolysis, (ii) increased oxidative phosphorylation, (iii) increased expression levels of MCT1, PFKFB4, LDHA, Cytc, SDHA, CD98, and (iv) decreased expression levels of CD36; the immune profile of the dendritic cell is determined to have (i) increased expression levels of CD14, PD-L1, ILT3, and CD141, and (ii) decreased expression levels of HLA-DR, CD86, and CDlc; and the dendritic cell has an increased ratio of phosphorylated mTOR to phosphorylated AMPK.

Methods for making dendritic cell vaccines

[0098] In another aspect, provided herein are methods of making dendritic cell vaccines, as well as the resulting vaccines, and methods of inducing an immune response using the vaccines. As explained in the Examples, dendritic cells from cancer patients exhibit increased glycolytic capacity, increased lactate production, reduced fatty acid oxidation metabolism, and increased phosphorylated mTOR and AMPK as compared to dendritic cells from healthy donors. These qualities were associated with poor overall survival. Thus, by manipulating the conditions in which dendritic cells are cultured for vaccines, an improved dendritic cell vaccine can be produced. By way of example, incubating dendritic cells in culture medium that contains reduced glucose concentrations, reduces lactate, increases amino acids, increases fatty acids and/or reduces phosphorylation of mTOR and AMPK, can improve generation of effective dendritic cell vaccines, by avoiding the immune-suppressive effects of the cancer.

[0099] In the methods described herein, dendritic cells may be deprived of intracellular glucose by culturing them in glucose-free media whereby the intracellular glucose will become depleted over time. In some embodiments, a reduced glucose concentration can be provided in the cell culture media. The reduced glucose concentration can be a concentration of about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, or 30 mM glucose in the cell culture medium. Alternatively, or in addition, the cells may be grown in media in the presence of at least one glucose transporter inhibitor. It would be understood by the skilled person that any glucose transport inhibitors known in the art may be suitable for use in the method described herein. In one embodiment, the at least one glucose transporter is selected from one or more of the group comprising GLUT1 , GLUT2, GLUT3 and GLUT4. In one embodiment, the glucose transport inhibitor is a GLUT1 inhibitor selected from the group STF-31 (4-[[[[4-(l ,1- Dimethylethyl)phenyl]sulfonyl]amino]methyl]-A/-3-pyridinylbenzamide); WZB-117 (3- Fluoro- 1 ,2-phenylene bi s(3 -hydroxybenzoate)); Fasentin (N-[4-chl oro-3- (trifluoromethyl)phenyl]-3- oxobutanamide); Apigenin (5,7-Dihydroxy-2-(4- hydroxyphenyl)-4H-chromen-4-one); Genistein (4',5,7-Trihydroxyisoflavone); oxime- based GLUT1 inhibitors and pyrrolidinone derived GLUT1 inhibitors. In one embodiment, the glucose transport inhibitor is a GLUT4 inhibitor selected from the group amprenavir (Agenerase), atazanavir (Reyataz), darunavir (Prezista), fosamprenavir ( Telzir, Lexiva), indinavir (Crixivan), lopinavir/ritonavir (Kaletra, Aluvia), nelfinavir (Viracept), ritonavir (Norvir), saquinavir (Invirase), tipranavir (Aptivus) and Curcumin.

[0100] As described above and in the Examples herein, an increase in lactate production as well as an increase in the lactate transporter MCT1 was observed in dendritic cells from melanoma patients as compared to healthy control patients. Thus, culturing dendritic cells in culture media which reduces the amount of lactate can skew the dendritic cell to a phenotype more consistent with that from a healthy donor. In some embodiments, lactate production of dendritic cells is reduced by culturing dendritic cells in the presence of an MCT inhibitor. In some embodiments, the MCT inhibitor is one that direct or indirectly inhibits a monocarboxylate transporter (MCT). MCTs are responsible for the inwards and outwards cellular transportation of monocarboxylate derivatives, such as lactate, pyruvate, and ketone bodies. MCT inhibitors include, without limitation, derivatives of cinnamic acid (Halestrap AP, et ai, Biochem J. 1974; 138:313-316; Spencer TL, et al ., Biochem J. 1976; 154:405- 414; Wahl ML, et al., Mol Cancer I her. 2002; 1 :617-628; Coss RA, et ai, Mol Cancer Ther. 2003; 2:383- 388), the N-methylbenzyl derivative (compound 38), AZD3965 (compound 39), lonidamine (Ben-Yoseph, O. el al, J. Neiirooncol, 36, 149-157 (1998)), and BAY8002. Post-transcriptional gene silencing techniques via siRNA (Fire A, et al.. Nature. 1998; 391 : 806-81 1 ; Zamore PD, ci al.. Cell. 2000; 101 :25- 33.) o miRNA (Lee AH. et ai, Cancer Res. 1998; 58: 1901-1908.) can also be used as an alternative to specifically target individual MCT isoforms. In one embodiment, the MCT inhibitor is an MCT1 inhibitor. In one embodiment, the MCT1 inhibitor is BAY8002.

[0101] As described above and in the Examples herein, reduced fatty acid oxidation metabolism is a characteristic of dendritic cells from melanoma patients. Thus, culturing dendritic cells in culture media which increases the amount of fatty acids may also skew the dendritic cell to a phenotype more consistent with that from a healthy donor. In some embodiments, long chain fatty acid species are added to the culture medium. Long chain fatty acid species for use herein include, without limitation, palmitic acid, oleic acid, and linoleic acid. In some embodiments, the long chain fatty acid is conjugated to a carrier, such as BSA, to assist its uptake and stability. [0102] Amino acids may also be added to the cell culture medium of the dendritic cells. The term amino acid is generally intended to mean an essential amino acid added to the culture medium, for example, arginine, cysteine, cystine, glutamine, histidine, Includes isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, tyrosine and valine and non-essential amino acids commonly used in culture media such as alanine, asparagine, aspartate, glutamate, glycine, proline and serine.

[0103] As described above and in the Examples herein, increased phosphorylation of mTOR and AMPK is a characteristic of dendritic cells from melanoma patients. Thus, the present disclosure also encompasses culturing dendritic cells in culture media which decreases the amount of phosphorylated mTOR and/or AMPK such that the dendritic cell is skewed to a phenotype more consistent with that from a healthy donor. In some embodiments, phosphorylation of mTOR and AMPK in dendritic cells is reduced by incubating dendritic cells in the presence of mTOR and/or AMPK inhibitors.

[0104] mTOR inhibitors include, but are not limited to, small molecule, antibody, peptide and nucleic acid inhibitors. For example, an mTOR inhibitor can be a molecule that inhibits the kinase activity of mTOR or inhibits binding of mTOR to a ligand. Inhibitors of mTOR also include molecules that down-regulate expression of mTOR, such as an antisense compound. A number of mTOR inhibitors are known in the art. Exemplary mTOR inhibitor include, without limitation, sirolimus, temsirolimus, everolimus, and rapamycin. In some embodiments, the mTOR inhibitor is rapamycin.

[0105] Similarly, AMPK inhibitors include, but are not limited to, small molecule, antibody, peptide and nucleic acid inhibitors. For example, an AMPK inhibitor can be a molecule that inhibits the kinase activity of AMPK or inhibits binding of AMPK to a ligand. Inhibitors of AMPK also include molecules that down-regulate expression of AMPK, such as an antisense compound. A number of AMPK inhibitors are known in the art. Such inhibitors are known in the art and include, without limitation, dorsomorphin, doxorubicin hydrochloride, GSK690693, BML-275, STO-609, a fasudil salt, gamma-D-glutamylaminomethylsulfonic acid, WZ4003 and HTH-01- 015. In some embodiments, the AMPK inhibitor is dorsomorphin.

[0106] Dendritic cells and/or monocytes, for example, for use in generation of dendritic cell vaccines that can be introduced into a human individual to stimulate an immune response, can be obtained from any human source. In some embodiments, the dendritic cell vaccines are autologous to the ultimate recipient, meaning dendritic cells, or precursor cells thereof (for example but not limited to peripheral blood mononuclear cells, monocytes or other myeloid progenitor cells), are obtained from a human individual, optionally induced to differentiate into dendritic cells, cultured as described herein, and then introduced into the same human individual. Examples of methods of differentiating precursor cells into dendritic cells are described in, e.g., U.S. Patent Publication No. 2021/0139852. Alternatively, the dendritic cells are allogenic to the ultimate recipient, meaning the dendritic cells, or precursor cells thereof, are obtained from a different human compared to the recipient of the vaccine. Dendritic cells obtained from an individual can be mature (e.g., HLA-DR^I1I/CD86¹¹¹) or immature (e.g.,HLA-DR^low/CD86^low). In some embodiments, the dendritic cells obtained from an individual are HLA-DR+, CD86+, CD208+, CD40+, ILT3+ and/or ICOS^low, CD80^low, PD-Ll^¹¹.

[0107] In some embodiments, precursor cells are obtained from a human individual and then induced to differentiate into dendritic cells. For example, in some embodiments, pluripotent or multipotent precursor cells can be obtained from a human donor. In some embodiments, cells from the donor are converted to induced pluripotent stem cells (iPSCs ) or CD34+ stem cells, which are then differentiated into dendritic cells. Examples of precursor cells differentiated into dendritic cells for use as vaccines are described in, e.g., WO 2006/020889. In some embodiments, the dendritic cells comprise, or are enriched for, a dendritic cell subpopulation, for example for myeloid dendritic cells, or CD14+ dendritic cells, e.g., as described in Collin, et al., Immunology. 2013 Sep; 140(1): 22-30.

[0108] Immature or mature dendritic cells can be cultured in culture media containing a sufficient concentration of glucose, glucose uptake inhibitor, fatty acids, amino acids, MCT1 inhibitor, mTOR inhibitor, and/or AMPK inhibitor such that the cells uptake the inhibitors and/or the fatty acids and amino acids and the amount of lactate and glucose in the medium is reduced and the level of phosphorylation of mTOR and AMPK is reduced in the cells. Various culture conditions for dendritic cells can be found in, e.g., U.S. Patent Publication No. 2021/0139852 and PCT Publication No. W02006/020889, as well as in the Example below.

[0109] In some embodiments, and consistent with the Examples described herein, the dendritic cells are characterized prior to culture. In some embodiments, this involves determining two or more of the following: (a) a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample; (b) an immune profile of the dendritic cell in the sub-population of dendritic cells in and a reference biological sample; (c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample; and (d) characterizing the differentiation state of the dendritic cell. Methods of determining (a) - (c) are described above.

[0110] In one embodiment, when the dendritic cells are characterized as tolerogenic (e.g., having one or more of (i) increased glycolysis, (ii) increased oxidative phosphorylation, (iii) increased expression levels of MCT1, PFKFB4, LDHA, Cytc, SDHA, CD98, (iv) decreased expression levels of CD36, (v) increased expression levels of CD14, PD-L1, ILT3, and CD141, (vi) decreased expression levels of HLA-DR, CD86, and CDlc, (vii) increased ratio of phosphorylated mTOR to phosphorylated AMPK), the dendritic cells are cultured as described above to skew the cells towards a more inflammatory state.

[0111] Once prepared, the dendritic cells, optionally formulated in a pharmaceutically- acceptable formulation, can be administered to a human to induce an immune response e.g., a cellular immune response, for example a T-cell response. In some embodiments, the human has cancer. In some embodiments, the human has melanoma. In embodiments in which the dendritic cells are allogeneic, HLA matching can be performed to select dendritic cells that have reduced or no HLA-mismatching to avoid graft-host interactions.

[0112] Dendritic cell preparations can be stored after preparation to be used later for therapeutic administration or further processing. Methods of cryopreconserving dendritic cells both before and after loading are described in PCT publication WO 02/16560.

[0113] No admission is made that any reference cited herein constitutes prior art. The discussion of the references states what their authors assert, and the inventors reserve the right to challenge the accuracy and pertinence of the cited documents. It will be clearly understood that, although a number of information sources, including scientific journal articles, patent documents, and textbooks, are referred to herein; this reference does not constitute an admission that any of these documents forms part of the common general knowledge in the art.

[0114] The discussion of the general methods given herein is intended for illustrative purposes only. Other alternative methods and alternatives will be apparent to those of skill in the art upon review of this disclosure and are to be included within the spirit and purview of this application.

[0115] Throughout this specification, various patents, patent applications and other types of publications (e.g., journal articles, electronic database entries, etc.) are referenced. The disclosure of all patents, patent applications, and other publications cited herein are hereby incorporated by reference in their entirety to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

EXAMPLES

[0116] The practice of the present disclosure will employ, unless otherwise indicated, conventional techniques of molecular biology, microbiology, cell biology, biochemistry, nucleic acid chemistry, and immunology, which are well known to those skilled in the art. Such techniques are explained fully in the literature cited above.

[0117] Additional embodiments are disclosed in further detail in the following examples, which are provided by way of illustration and are not in any way intended to limit the scope of this disclosure or the claims.

EXAMPLE 1

[0118] In vitro moDC generation. PBMCs from healthy donors were purchased (Trima Residuals RE202, Vitalant) and purified by Ficoll-hypaque gradient centrifugation (Fisher Scientific, 45-001-749). Cryopreserved PBMCs were thawed using RPMI (Gibco-Invitrogen) complete media (1% Pen Strep, 1% L-Glutamine, 10% FBS Heat Inactivated Serum (Gibco- Invitrogen, 16000-044), and 0.5% DNase (Sigma, DN-25) and washed twice with PBS. CD14⁺ monocytes were selected using CD 14 microbeads (Miltenyi Biotec, 130-050-201) and cultured for 5 days in CellGenix medium (0020801-0500) supplemented with 800 U/mL GM-CSF (Miltenyi Biotec, 130-095-372) and 500 U/mL IL4 (Miltenyi Biotec, 130-095-373) to generate iDC. At day 3, half of media was replaced and supplemented with fresh cytokines. iDC were matured on day 5 with 1000 U/mL IFN- y (Peprotech, 300-02) and 250 ng/mL LPS (Sigma- Aldrich, L2630). Two types of tol-moDC were generated. To obtain vitd3-tol-moDC lOOnM of vitamin D3 (Sigma, D1530) was added to cultures at dO and day3. And dexa-vitd3-tol-moDC were generated by adding lOOnM of vitamin D3 and 10 nM of dexamethasone (Sigma, D4902) at day 3 to cultures. Both tol-moDC were matured as described above.

[0119] SCENITH cell staining and data acquisition. SCENITH was performed as described in (Arguello et al., 2020). SCENITH™ reagents kit (inhibitors, puromycin and antibodies) were obtained from www.scenith.com/try-it and used according to the provided protocol for in-vitro derived myeloid cells. Briefly, control and tol-moDC cultures at desired timepoints, were treated for 18 minutes with Control (DMSO), 2-Deoxy-Glucose (2-DG; lOOmM), Oligomycin (O;

IpM), a combination of 2DG and Oligomycin (DGO) or Harringtonine (H; 2pg/mL). Following metabolic inhibitors, Puromycin (final concentration 10 pg/mL) was added to cultures for 17 min. After puromycin treatment, cells were detached from wells using TypLE Select (Fisher Scientific, 505914419), washed in cold PBS and stained with a combination of Human TureStain FcX (Biolegend, 422301) and fluorescent cell viability dye (Biolegend, 423105) for 10 min 4°C in PBS. Following PBS wash step, primary antibodies against surface markers were incubated for 25 min at 4°C in Brilliant Stain Buffer (BD Biosciences, 563794). Next cells were fixed and permeabilized using True-Nuclear Transcription Factor Buffer Set (Biolegend, 424401) as per manufacturer instructions. Intracellular staining of puromycin and protein targets was performed for 1 h in diluted (lOx) permeabilization buffer at 4°C. Finally, data acquisition was performed using the Cytek Aurora flow cytometer. Primary conjugated antibody information used in SCENITH panel is listed in Table 1.

Table 1

All antibodies were titrated to reduce spillover and increase resolution using single stained moDC (generated as described above) samples. Unstained cell controls used for autofluorescence extraction were generated for each time point, culture conditions (control, vitd3-tol-moDC and dexa-vitd3-tol-moDC) and metabolic inhibitor treatments (C, 2DG, O, DGO). Samples were unmixed using reference controls generated in combination with stained Ultracomp beads (Fisher Scientific, 01-2222-41) and stained cells using the SpectroFlo Software v2.2.0.1. The unmixed FCS files were used for data processing and analysis using Flow Jo (BD, version 10.7.1). Manually gated CD14'HLA-DR⁺CD86⁺ cells were used for downstream analysis. gMFI expression values were imported into R environment for correlation and heatmap analysis using the below described R packages.

[0120] Single-cell metabolic regulome profiling (scMEP) by mass cytometry. scMEP analysis was performed as recently described (Hartmann et al., 2021). In short, antibodies targeting metabolic features were conjugated in-house using an optimized conjugation protocol (Hartmann et al., 2019) and validated on multiple sample types. Cells were prepared for scMEP analysis by incubation with small molecules to be able to assess biosynthesis rates of DNA, RNA and protein, cisplatin-based live/dead staining, PFA-based cell fixation and cryopreservation (dx.doi.org/10.17504/protocols.io.bkwkkxcw). Next, cells were stained with metabolic antibodies in a procedure that includes surface staining for 30min at RT, PFA-fixation for lOmin at RT, MeOH-based permeabilization for lOmin on ice, intracellular staining for Ih at RT and DNA intercalation (dx.doi.org/10.17504/protocols.io.bntnmeme). Finally, cells were acquired on a CyT0F2 mass cytometer (Fluidigm). Protein targets and antibody information used in scMEP are listed in Table 2.

Table 2.

[0121] Mass cytometry data processing and analysis. Raw mass spectrometry data were pre- processed, de-barcoded and imported into R environment using the flowCore package (version 2.0.1) (Hahne et al., 2009). Values were arcsinh transformed (cofactor 5) and normalized (Hartmann et al., 2021) for downstream analyses based on previously reported workflow (Nowicka et al., 2017). Mean cell radius (forward scatter from Cytek analysis, FSC-A) was used to calculate changes in cell volume across DC differentiation. Expression of scMEP factors was normalized to account for increase in cell volume from precursors to mature moDC. All clustering analyses were performed on subsampled (20-25,000 cell s/treatm ent time point) HLA- DR⁺CD86⁺-gated cell populations with indicated input makers. Multi-dimensional plots were generated using R package limma (version 3.44.) and dimensionality reduction clustering analysis was performed using Rtsne (version 0.15). For visualization and heatmap analyses we utilized R packages ggplot2 (version 3.3.3) and Compl exHeatmap (version 2.4.3) (Gu et al., 2016), respectively. Differential expression analysis of marker expression between treatment groups was determined separately for each DC maturation time point using linear mixed effect model accounting for donor variability using the lme4 (version 1.1-26) package. Spearman correlation coefficient correlation matrix for marker expression profiles was computed and visualized using the corrr (version 0.4.3), Hmisc (version 4.5.0) and corrplot (version 0.88) R packages.

[0122] Calculation of scMEP pathway scores. Percentual SCENITH parameters were first normalized to protein synthesis levels (using puromycin gMFI) with accounting for puromycin background according to the following formula (SCENITH parameter) *(Co-DGO). To determine OXPHOS, glycolysis, FAO and AA scMEP pathway scores, linear regression analysis between the scMEP median metabolic marker expression (arcsinh transformed) and log- transformed median normalized SCENITH parameters was applied using the R package ImerTest (version 3.13). For calculating scMEP scores, the most significant and positively correlated markers within each metabolic pathway were summarized and divided by the number of markers within that pathway. Spearman correlations between scMEP pathway scores and SCENITH parameters were represented using the ggpubr (version, 0.4.0) R package.

[0123] Mean Gini Score Calculations. Random forests were trained individual cells from on CyTOF dataset using the randomForest packagel (version, 4.6 - 14) by randomly selecting 80% of the cells in each sample then comparing Ctrl vs vid3+dexa, Ctrl vs vid3 and vid3 vs vid3+dexa treatments, at three time points (iDC, 4h mDC and 24h mDC) in triplicate at different starting seed values for a total of 27 unique models (Robin et al., 2011). The Gini indices were determined for each model and a mean of the triplicate was as a representative as a “Mean Gini Score”.

[0124] Mitochondrial size flow cytometry analysis. Mitochondrial size was determined using MitoTracker™ Deep Red FM (Invitrogen, M22426) as per manufacturer instructions.

[0125] Extracellular glucose and lactate measurements. Glucose and lactate levels were analyzed in moDC culture supernatants using the BG1000 Blood Glucose Meter & test strips (Clarity, 75840-796) and Blood Lactate Measuring Meter Version 2 test strips (Nova Biomedical, Lactate Plus), respectively.

[0126] Statistical Data Analysis. Statistical comparisons between groups were performed using paired-sample /-tests unless otherwise stated using R (version 4.0.2). P values are represented as *p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001. p values < 0.05 were considered statistically significant. Numerical labels indicate near significant values). The Shapiro-Wilk test was used to assess data normality, and statistical tests were performed using R. Figure graphs were generated using the R package ggplot2 (version 3.3.3) and ggpubr (0.4.0).

EXAMPLE 2

IMMUNO-METABOLIC PROFILING OF MONOCYTE TO DC DIFFERENTIATION REVEALS EXTENSIVE REPROGRAMMING FROM GLYCOLYTIC TO PREDOMINANTLY MITOCHONDRIAL METABOLISM

[0127] To evaluate the impact of metabolic pathway inhibition during moDC differentiation, SCENITH coupled with a multi-parametric panel encompassing DC surface and signaling markers was employed (FIG. 1A). This enabled employment of both manual gating and unsupervised clustering approaches to profile immune-phenotypes and metabolic activity of CD 14+ monocytes, moDC precursors (mono 24h/48h), immature (day 5 iDC) and mature (4h and 24h-LPS/ZFNy) moDC with single-cell resolution. Dimensionality reduction analysis using t- distributed stochastic neighbor embedding (tSNE) based on nine immune markers identified 5 distinct clusters of differentiation states with iDC and 4h-matured co-occupying similar clustering features (FIG. IB). Monocyte differentiation induced rapid loss of CD14 expression, which was paralleled by an increase and maturation-boosted upregulation of MHC surface receptor HLA-DR, co-stimulatory molecules CD86, CD206, including acquisition of the conventional DC 2 (cDC2) marker CDlc (BDCA-1), checkpoint regulator programmed cell death ligand-1 (PD-L1/CD274) (FIG. IB) and modest increase in co-inhibitory Ig-like transcript 3 (ILT3/CD85) (FIG. IB). The DC SCENITH panel and gating strategies for precursors and DC populations are shown in Table 1 and FIG. 2 A.

[0128] In agreement with Arguello et al. (Arguello et al., 2020), monocytes relied primarily on glucose oxidation having the highest glycolytic capacity and minimal dependency on mitochondrial energy production (FIG. 1C). Within 24 hours of GM-CSF/IL4 stimulus, monocytes undergo a dramatic metabolic shift from 0% mitochondrial dependence and high glycolytic capacity to relying predominantly on OXPHOS (80% mitochondrial dependence), as well as maintaining low levels of glycolytic capacity (20%). Day 5 iDC exhibited further increase in mitochondrial dependence along with reduced glucose dependence and elevated utilization of fatty/amino acids as an energy source. Engagement of mitochondrial respiration over the course of iDC differentiation paralleled increase in protein synthesis, which peaked 4h post LPS/IFNY-induced DC maturation along with transient increase in glycolytic capacity (FIG. 1C). However, contrary to TLR-induced activation of murine BMDCs, characterized by a maximal upregulation of glycolysis with repressed OXPHOS and collapsed mitochondrial activity (Everts et al., 2012; Krawczyk et al., 2010), maturation of human moDC undergo only moderate and transient increase in glycolytic metabolism (Malinarich et al., 2015). Fully matured moDC exhibited lowest protein synthesis, and dominant mitochondrial dependence with moderate FAAO (25%) and low glycolytic profile (15%) (FIG. 1B-1C). Glucose was the predominant energy source for fueling OXPHOS in the final 24h mDC state. Etomoxir and CB- 839 inhibitors were used to further separate contributions of fatty acids (long-chain) and glutamine, respectively, towards fueling protein synthesis in iDC and mDC. These inhibitors did not alter DC markers’ expression and allowed us to reveal that while iDC showed similar 19% Glutaminolysis and FAO dependence, mDC had lower, 7% FAO dependency and increased 41% Glutaminolysis dependence (P < 0.05; FIG. 22A, FIG. 22B and 22C)

[0129] To determine if the metabolic changes observed during differentiation of cells in cell culture media in-vitro correlate with ex-vivo data, we derived SCENITH parameters for monocytes and CD123+ plasmacytoid (pDC) and CD141+ CDlc- cDCl sub-populations in freshly isolated PBMCs (FIG. 22D). Similar to in vitro conditions, ex vivo monocytes had highest glucose dependence (72%) and 71% glycolytic capacity. In contrast, cDCl exhibited highest mitochondrial dependence (79%) and unlike inflammatory in vitro matured DC, both cDCl and pDC exhibited highest FAAO capacity (65%). This suggests that high levels of glucose in culture media is potentially skewing a preference for glucose as an energy source for in vitro derived DC.

EXAMPLE 3

TEMPORAL BALANCE IN MTOR: AMPK PHOSPHORYLATION RATIO REFLECTS MITOCHONDRIAL VS GLYCOLYTIC METABOLIC PROGRAMS IN MOPC

[0130] To gain additional insights into signaling cascades regulating moDC metabolism, this SCENITH panel included quantification of total and phosphorylated forms of critical signaling factors with a focus on the complex interplay between mammalian target of rapamycin (mTOR) and AMP-activated protein kinase (AMPK) regulatory axis at specific stages of moDC differentiation. As a downstream target of the PI3k/Akt pathway, mTOR is an important upstream activator of glycolytic reprogramming driving high metabolic demands of TLR- activated murine macrophages and DCs (Zhou et al., 2018). As a critical cellular nutrient sensor controlling an array of cellular responses, growth and survival, mTOR concurrently supports de novo biosynthesis of lipids, proteins, and amino acids (Amiel et al., 2012; Snyder and Amiel, 2019). Activation of AMPK opposes mTOR dependent glycolytic reprogramming, skewing cellular metabolism towards energy conservation driving mitochondrial biogenesis via peroxisome proliferator-activated receptor-y (PPARy) co-activator-la (PGCla) signaling axis to increase activity of mitochondrial enzymes and OXPHOS. AMPK also upregulates fatty acid transporter carnitine palmitoyltransferase la (CPTla) favoring catabolic FAO (Herzig and Shaw, 2018; Kelly and O’Neill, 2015). Here phosphorylation changes of p-AMPK (Thr- 183/172), p-mTOR (Ser-2448) were integrated along with expression changes in PPARy and a downstream mTORCl target ribosomal protein S6 kinase 1 (pS6K) to show that metabolic reprogramming of moDC closely reflects temporal alterations in mTOR and AMPK phosphorylation level, which result in modal changes in overall p-mTOR:p-AMPK ratio (FIG. ID). Specifically, it was observed that the initial glycolytic state as well transient increase in glycolytic capacity following 4h LPS/IFNy stimulation favored increased in p-mTOR and pS6K levels. However, the initial switch into OXPHOS and the final 24h-matured moDC stages with predominant mitochondrial metabolism exhibited higher p-AMPK, resulting in decreased p- mTOR: p-AMPK ratio (FIG. ID). Expression of PPARy also exhibited a bi-modal and transient expression pattern in early differentiating precursors and following 4h post-iDC activation, which mirrored a transient increase in FAAO (FIG. 1C). Consistent with the notion that pAMPK is a positive influence for moDC differentiation, analysis of marker co-expression patterns showed that p-AMPK positively correlated with key DC lineage marker CD86, HLA-DR and CDlc, with inverse correlation with pS6K, p-mTOR, CD206 and iNOS (FIG. IE).

[0131] Next, we used Rapamycin and Dorsomorphin concentrations to functionally inhibit mTOR and AMPK signaling during maturation phase in control and tolerogenic DC respectively minimal effects on cell viability (FIG. 23 A, FIG. 24A). Rapamycin inhibition of p-mTOR was confirmed during early (30 min) LPS/IFNy activation phase (FIG. 24B) of iDC. Rapamycin treatment reduced HLA-DR, with near significant decrease in CD86, PD-L1 in control cells and significantly reduced expression of tolerogenic marker ILT3 in vitd3-mDC samples (FIG. 23 A). Dorsomorphin dramatically downregulated immune activation markers, FA transporter CD36 and significantly reduced ILT3 and CD141 in both control and vitd3-tol-DC (FIG. 23 A). We also noticed that in addition to p-AMPK, Dorsomorphin also reduced total mTOR levels in both Ctrl and vitd3-DC, which was previously observed in malignant cells (Ghanaatgar-Kaabi et al., AMP-kinase inhibitor dorsomorphin reduces the proliferation and migration behavior of colorectal cancer cells by targeting the AKT/mTOR pathway. lubmb Life 71, 1929-1936 (2019)) and poses a limitation to precise interpretation of its effects in our system. Both inhibitors exhibited effects on cellular metabolism by reducing glucose consumption and lactate production. This effect appears to be more pronounced in vitd3-tol-DC as compared to inflammatory DC (FIG. 23B). These results collectively suggest that mTOR and AMPK play critical functional role and exhibit opposing regulation primarily at the actDC maturation stage. Furthermore, tolerogenic cells are more sensitive to mTOR and/or AMPK signaling inhibition.

EXAMPLE 4

TEMPORAL CHANGES IN THE METABOLIC REGULOME REFLECT FUNCTIONAL REPROGRAMMING OF MITOCHONDRIAL RESPIRATION AND AXILLARY PATHWAY ACTIVATION IN MOPC

[0132] To further map the metabolic rewiring underlying functional specialization of antigen presenting moDC, scMEP was utilized to quantify the expression of phenotypic markers in conjunction with rate-limiting metabolic enzymes, metabolite transporters and signaling factors encompassing several metabolic pathways depicted in FIG. 3 A. Kinetic profiles for multiple DC- lineage surface markers recapitulated SCENITH immune-profiling. Along with the loss of CD 14, there was a maturation-specific boost in HLA-DR, CD86, PD-L1 and CDlc, while CD206, CD11c, CD1 lb peaked 4h-post maturation induction (FIG. IB, FIG. 2B). In hand with functional switch from glycolytic precursors to mitochondrial dependence measured by SCENITH, GM-CSF/IL4 treatment triggered robust upregulation of components of the tricarboxylic acid (TCA) cycle (IDH2, CS) and electron transport chain (ETC) complexes (SDHA, ATP5A), confirming an increase in OXPHOS (FIG. 3B, FIG. 2C). While the FAAO SCENITH parameter is unable to distinguish FAO and glutaminolysis pathways, concurrent upregulation of metabolic markers regulating fatty acid oxidation (HADHA) together with AA transporters (ASCT2, CD98) and glutaminase (GLS) enzyme involved in glutaminolysis, reflects higher capacity for auxiliary energy sources derived from fatty acids and AA over the course of moDC differentiation (FIG. 3B, FIG. 2C). Of note FA transporters CD36 and CPT1 A exhibited moderate decrease followed with constitutive expression across maturation. Intracellular glutathione redox status plays important role in differentiation and Thl and Th2 responses of mo-DCs (D’Angelo et al., 2010; Kamide et al., 2011). Glutathione synthase (GSS), a potent antioxidant, which catalyzes glutathione (GSH) biosynthesis, protects cells from oxidative damage (Ghezzi, 2011). GSS exhibited increased expression towards iDC stage and remained constant following DC maturation, which further confirms the requirement of GSH synthesis for DC functions. The pentose phosphate pathway (PPP) represents a branch of glucose metabolism, which regulates redox homeostasis, production of reactive oxygen species (ROS), nitric oxide (NO) and fatty acid synthesis by producing the vital intermediate NADPH as well as nucleic acid building block ribose 5-phosphate (R5P) (Ge et al., 2020). The effects of PPP on DC activation have been only studied in context of murine DC differentiation to date. The study by Everts et al. (Everts et al., 2014), showed that inhibition of PPP enzyme glucose-6-phosphae dehydrogenase (G6PD) diminished LPS-mediated proinflammatory cytokine production and lipid accumulation, which resulted in preventing murine BMDC maturation. Along with its role in DC maturation, G6PD was elevated in iDC and 24h mDC stages (FIG. 3B). The slight decrease at 4h is likely due to an increase in glucose shuttling through glycolysis, which is transiently enhanced in early activated (4h) cells. Mitochondrial biogenesis and dynamics were monitored using PPARy coactivator-la (PGCla) and translocase of outer mitochondrial membrane 20 (TOMM 20), which were both strongly upregulated through DC differentiation (FIG. 5C). It is noted that the expression changes of scMEP metabolic enzymes were normalized to the observed 4-fold increase in cell volume from monocyte to mature moDC.

EXAMPLE 5

UPPER GLYCOLYTIC PATHWAY ENZYMES AND LACTATE TRANSPORTER REPRESENT INDUCIBLE CHECKPOINTS OF GLYCOLYSIS IN MOPC

[0133] Metabolic scMEP profiling supports the hypothesis that active mitochondrial biogenesis in conjunction with increased expression of respiratory complexes, auxiliary AA/FAO pathways and antioxidant protection systems are central to meeting energy demands associated with moDC differentiation and effector functions (Zaccagnino et al., 2012). Quantification of the glycolytic pathway identified ENO1, GAPDH and LDHA, which are factors in the lower steps of the glycolytic pathway as a subset of enzymes highly expressed in monocytes (FIG. 3 A, 3B). Their expression decreased following differentiation, which is consistent with reduced glycolytic capacity of maturing mo-DCs representing only 20-25% of their metabolic activity. It is further shown that factors in the higher steps of glycolysis pathway including glucose transporter GLUT1, phosphorylation factors PFKFB4 and lactate transporter MCT1 controlling the lactate export step showed significant increase during mo-DC differentiation (FIG. 3B, FIG. 2C).

EXAMPLE 6

SCMEP-BASED CO-EXPRESSION PATTERNS DEFINE SINGLE-CELL MODC IMMUNE DIVERSITY

[0134] Next, the SCENITH functional parameters were integrated with scMEP co-expression patterns to calculate metabolic pathway scores across moDC differentiation. To reflect the contribution of each SCENITH pathway percentual measurements with respect to total energy output over time, SCENITH parameters were normalized with respect to protein synthesis levels (FIG. 3D). As described in the methods section, correlations between normalized SCENITH metabolic profiles and scMEP marker co-expression were tested and a previously described approach (Hartmann et al., 2021) was used to derive in silico scores, used to represent metabolic pathway activation. Using scMEP scores temporal changes in OXPHOS, glycolysis, FAO, AA, PPP and GSH metabolic remodeling were able to be mapped across moDC differentiation timeline, which closely mirrored measured changes in normalized SCENITH parameters and delineated kinetic changes in metabolism across moDC lineage generation (FIG. 3D, 3E). Due to more complex co-expression patterns of glycolytic factors, separate scMEP scores for inducible (GLYC-UP) and constitutive (GLYC-CON) arms of the moDC glycolytic pathway (FIG. 3E) were also calculated.

EXAMPLE 7

TEMPORAL CHANGES AND ENGAGEMENT OF MULTIPLE OF METABOLIC PATHWAYS UNDERLIES HETEROGENEITY OF MODC IMMUNE PHENOTYPES

[0135] Collectively, metabolic changes in SCENITH and scMEP pathway analyses suggested simultaneous increase of mitochondrial and glycolytic pathways at specific stages of moDC differentiation (FIG. 3D, 3E). Because the SCENITH profiling used to derive scMEP scores represents a bulk metabolic measurement encompassing whole well cultures, it remained unclear whether there are distinct OXPHOS and/or glycolytic DC sub-populations contributing to the final metabolic output. Independently calculated single-cell level scMEP scores revealed that differentiating moDC populations simultaneously upregulate both OXPHOS and FAO pathways with homogenous distribution throughout differentiation with peak expression at the iDC and 4h mDC activation stages (FIG. 3F). AA pathway followed a similar but less correlative upregulation pattern as FAO and exhibited the highest heterogeneity in 24h mDC (FIG. 3F). This suggests a divergence of these pathways and cell skewing towards a preference for glucose and fatty acids uptake as opposed to AA to fuel metabolism of matured moDC. Positive correlations and unified distribution with mitochondrial dependence were also observed for and GSH pathway and mitochondrial dynamics scores arguing for the importance of glutathione redox status and mitochondrial biosynthesis during moDC maturation (FIG. 2D). Coordinate engagement of both OXPHOS and glycolysis, which resembled metabolic remodeling of activated CD8 T cells (Ahl et al., 2020; Hartmann et al., 2021) was observed. However, the single-cell approach revealed that cells span a wide spectrum of possible glycolytic and respiratory scMEP scores, indicating a range of metabolic heterogeneity within each differentiation time point (FIG. 3F). To better capture this previously unrecognized metabolic diversity, heatmap overlays of DC-lineage marker expression were mapped on the single-cell scMEP pathway score co-expression plots. Analysis of 24h mDC revealed that a range of OXPHOS and glycolytic co-expression profiles underlies distinct DC immune phenotypes. Specifically, it was seen that while HLA-DR and PD-L1 distribution showed wide glycolytic and OXPHOS potential, CD86^H1 cells CD lc^H1 cells exhibited preferential enrichment toward OXPHOS and glycolytic phenotype, respectively (FIG. 4A).

EXAMPLE 8

DIFFERENTIAL COACTIVATION OF MITOCHONDRIAL VS GLYCOLYTIC PATHWAYS GOVERNS MODC IMMUNE PHENOTYPES AND SEPARATES CD1C^HI CD86^HIMODC POPULATIONS

[0136] To further determine metabolic properties associated with CDlc⁺ vs CD86⁺ phenotypic polarization (FIG. 4B), single-cell data sets were divided into 4 quantiles, which were determined from CDlc and CD86 expression ranges (FIG. 5 A). Quantile overlay on tSNE plots generated based on metabolic marker clustering consistently revealed exclusive segregation of CDlc¹¹¹ vs CDSb¹¹¹ population regions across moDC stages (FIG. 5B). Quantile visualization of single-cell correlation scMEP scores further confirmed that while CDlc¹¹¹ phenotype associates with both glycolytic and OXPHOS pathways, CD86⁺ polarization skews predominantly towards aerobic OXPHOS metabolism (FIG. 5C). Apart from slightly enhanced mitochondrial dynamics, distribution of scMEP population scores for constitute glycolytic factors or additional pathways did not show significant changes between the top CD I c¹¹¹ and CDSb¹¹¹ quantiles (FIG. 4C, FIG. 5D). Survey of scMEP marker expression confirmed that inducible factors GLUT1, MCT1 and PFKFB4 are the critical checkpoints driving glycolytic flux in CD I c¹¹¹ cells (FIG. 4D, FIG. 5E). PDK1 was also significantly increased in the top 4^th (CDlc¹¹¹) quantile, which was not observed in CD86 quantiles comparisons (FIG. 4D, FIG. 5F). PDK1 participates in inhibiting phosphorylation of the pyruvate dehydrogenase complex, thereby preventing conversion of pyruvate produced by glycolysis to acetyl -CoA and its entry to the TCA cycle, as diagramed in FIG. 4F (Stacpoole, 2017). Its critical role in glucose homeostasis was demonstrated in a study by Tan et al., (Tan et al., 2015) in which PDK1 -knockdown reduced glycolysis, glucose oxidation and enhanced mitochondrial respiration causing attenuated inflammatory response in Ml macrophages. Therefore, it was postulated that elevated PDK1 prevents pyruvate entry into mitochondria and supports the observed glycolytic flux in CDlc¹¹¹ moDC. To functionally validate the phenotypic mass cytometry data, SCENITH parameters of manually gated CD I c¹¹¹ and CDSb¹¹¹ populations were analyzed (FIG. 4E). Indeed, CDSb¹¹¹ cells exhibited high mitochondrial dependence (>75%), which gradually increased towards the final mDC stage reaching 90% together with increased glucose dependence with lowered FAAO utilization. In contrast, CD Ic¹¹¹ phenotypes showed lower mitochondrial dependence (<60%) re-routed close to 60% of their metabolic activity towards glycolysis to meet their energetic requirements (FIG. 4F). While glycolysis of CD I c¹¹¹ cells reaches maximal capacity 4h post maturation, convergence of the metabolic pathways towards predominantly mitochondrial respiration as cells acquire both immune markers and advance in maturation was observed (FIG. 4C, 4E). This is consistent with single-cell scMEP score profiles, showing that earlier iDC and 4h mDC stages exhibited a higher degree of the CD I c¹¹¹ vs CD86¹¹¹ histogram overlay separation as compared to fully mature moDC (FIG. 4C) with more homogenous population and enhancement of double-positive CD86⁺ CDlc⁺ cells (FIG. 4E). Immune molecules HLA-DR, CD206, PD-L1, CD276 (B7-H3), CCR7 and CDI 1c were elevated on CDSb¹¹¹, while CD80 and ILT3 were enriched on CD I c¹¹¹ populations (FIG. 4F, FIG. 6A). While signaling factor profiling showed coordinate upregulation of both p- mTOR and p-AMPK in CDSb¹¹¹, consistent with higher glycolytic potential, increased p- mTOR:p-AMPK ratio and elevated unphosphorylated AMPK (particularly 24h mDC) was observed as well as significantly elevated iNOS expression in CD I c¹¹¹ populations (FIG. 4G).

EXAMPLE 9

A PROPORTIONAL SHIFT FROM GLUCOSE DEPENDENCE TO FAAO UTILIZATION AND SIMULTANEOUS UPREGULATION OF SEVERAL METABOLIC PATHWAYS UNDERLY IMMUNE-SUPPRESSIVE PHENOTYPES TOL-MOPC

[0137] Single-cell approaches were next applied to investigate DC skewed to be tolerogenic to identify critical metabolic features and potential biomarkers defining the major modes of in vitro tolerogenic moDC generation. Tol-moDC were generated using la,25-dihydroxyvitamin D3 (vitd3) alone (vitd3-tolDC) or in sequential combination with dexamethasone (dexa; vitd3-dexa- tolDC) as depicted in FIG. 8A, and their immuno-metabolic profiles were monitored along with inflammatory moDC across the maturation timeline. Tol-moDCs exhibited classical changes with elongated spindle-like characteristics (Ferreira et al., 2011) along with reduced HLA-DR, CD86, CDlc with retention of CD14 surface expression, respectively, which was confirmed by both SCENITH and scMEP panels (FIG. 6B-6D). As expected, CD303 was undetected. HLA- DR⁺CD86⁺ populations were used for all downstream analyses to ensure that comparisons are representative of DC-cell linages and not undifferentiated CD14+ monocyte contaminations.

[0138] Differential expression analysis of the full spectrum of SCENITH markers showed that both tolerogenic signals significantly decreased HLA-DR at the iDC stage and prevented maturation dependent increase of numerous costimulatory molecules including HLA-DR, CD86, and CDlc (FIG. 6C). CD276 (B7-H3) was specifically downregulated in vitd3-tolDC and expression of CD206, CCR7, CD11c and CD80 was overall upregulated but more variable between the tolerogenic conditions. Neutral amino acid transporter CD98 (LAT1) and CD36 exhibited upregulated and downregulated expression pattern in both tolerogenic DC types respectively (FIG. 7A). Unsupervised tSNE clustering using SCENITH immune parameters revealed 6 district clusters, precisely delineating control and tolerogenic treatments in which vitd3-tol-moDC and vitd3-dexa-tol-moDC co-occupied the same clusters (Figure 4 A). Singlecell heatmap overlay of selected immune markers demonstrated a progressive increase of HLA- DR and CD86 with maturation in control wells, which was reduced in both tolerogenic counterparts. Consistent with previous reports (Malinarich et al., 2015; Megen et al., 2021), tol- rnoDC exhibited a robust increase in CD 14, CD 141, and ILT3, and PD-L1 across all maturation stages (FIG. 8A, FIG. 7A).

[0139] Based on SCENITH parameters, both tol-moDC types significantly increase global rate of protein synthesis (FIG. 8B). Percentual SCENITH profiles revealed a metabolic shift from mitochondrial dependence to a 25% increase in glycolytic capacity in the early 24h-vitd3-moDC precursors (FIG. 8C left panel, FIG. 7B left panel). While the glycolytic reprogramming was not apparent across later maturation stages, a significant shift from glucose dependence to a 15% increase in FAAO was observed in iDC and 4h mDC, which was reversed in vitd3-tol-moDC 24h-post maturation. Accounting for changes in total protein synthesis, tolerogenic moDC showed a significant transient increase in glycolytic capacity, OXPHOS and FAAO normalized SCENITH parameters (FIG. 8C right panel, FIG. 7B right panel). Overall changes in metabolism were enhanced in vitd3 -generated tol-moDC as compared to vid3-dexa-tol-moDC.

[0140] In parallel with SCENITH profiling, dynamic changes in scMEP pathways validate metabolic hyper-activation of tol-moDC. Comparative heatmaps of unsupervised clustering analysis of median of scMEP metabolic proteins across 3 donors robustly separated inflammatory from tol-moDCs while preserving expression differences within each differentiation/maturation stage (FIG. 8D). Differential expression analysis of scMEP makers showed significant and sustained upregulation of TCA/ETC machinery (CS, CytC, SDHA, ATP5 A) together with dramatic and transient increase multiple glycolytic factors (LDHA, MCT1, PFKFB4, ENO1) with the highest degree of differences at the iDC and 4h maturation stages of both types of tol-moDCs (FIG. 7C). Among robustly upregulated factors were components of mitochondrial dynamics (GSS, TOMM20), amino acid transporter CD98 along with signaling mTOR downstream target pS6K, which validates SCENITH panel measurements (FIG. 7C). The CD36 (downregulated in tol-moDC) was the most robust predictor of tol-moDC phenotypes based on single-cell random forest permutation analysis (FIG. 7D). Kinetic analysis of scMEP pathway scores further showed the transient nature of metabolic pathway upregulation in both types of vid3-dexa-tol-moDC and in a sense agreed with previous study (Malinarich et al., 2015) showing that glycolytic capacity returns close to normal immunogenic DCs levels following long term (24-48h) LPS treatment of vid3-dexa-tol-moDCs (FIG. 8E, FIG. 9A).

Furthermore, dynamic changes associated with elevated OXPHOS, FAO and AA pathways, PPP flux and increase in GSH activation consistent with increased redox state and production of reactive oxygen species by tol-moDC were uncovered (Ferreira et al., 2011; Malinarich et al., 2015). Of note, both constitutive and inducible glycolytic scMEP pathway scores were upregulated in tol-moDC as compared to immunogenic counterparts (FIG. 9B). In agreement with persistent increase in glucose dependence (Figure 4C right panel) and high glycolytic scMEP scores (FIG. 8E) with upregulated lactate transporter MCT1 expression, decreased glucose levels were detected with parallel increase in secreted lactate in tol-moDC culture supernatants (FIG. 8F). In summary, the results showed that tol-moDC undergo a proportional shift from glucose dependence to FAAO utilization and exhibit an increase in metabolic activity and simultaneous upregulation of several metabolic pathways.

EXAMPLE 10

CHANGES IN PMTOR:PAMPK RATIO IMPACTS METABOLIC ALTERATIONS OF TOL-MODCS

[0141] Upregulation of multiple factors associated with glycolytic reprogramming including p- mTOR/pS6k/iNOS signaling axis were revealed as well as changes in both total and phosphorylated form of AMPK at distinct stages in both tolerogenic phenotypes (FIG. 7A). While previous reports implicated involvement of PI3K/Akt/mTOR pathway (Ferreira et al., 2015) along with the p38 MAPK, ERK1/2 and STAT3 signaling axis (Danova et al., 2015) controlling glycolytic phenotypes and high glucose consumption underlying induction and maintenance of the tolerogenic DC phenotype, the role for AMPK in both immunologic and tol- moDC biology is largely understudied and the precise role of mTOR/AMPK balance is controversial (Thomaz et al., 2018; Wculek et al., 2019). As demonstrated in FIG. 8G, p-mTOR and iNOS are significantly upregulated together with pS6K following similar trend in both tol- moDC. PPARy is upregulated in all DC stages with striking increase in vitd3-dexa-treated samples at 4h post maturation (FIG. 8G). pAMPK resembles an expression decrease at peak glycolytic capacity at 4h mDC, and while upregulation primarily in vitd3-tol-moDC is observed, analysis of p-mTOR:p-AMPK ratio revealed a significant skewing towards higher p-mTOR dominance in both tolerogenic moDC types (FIG. 8H). Collective analysis of marker coexpression networks showed strong correlative profiles with glycolytic and mitochondrial dependencies. Arguing for the importance of p-AMPK in DC maturation, ranking of marker expression profiles with SCENITH metabolic parameters showed that phosphorylation of AMPK positively correlated with expression of numerous DC activation markers including CD86, HLA- DR, PD-L1 and CCR7, which significantly associated with mitochondrial dependence (FIG. 9C). Consistent with their role in regulating glycolytic metabolism during DC activation (Thwe and Amiel, 2018), p-mTOR, pS6K, iNOS co-expressed with lineage markers CD206, CD80 and CD11c and exhibited a positive correlation with glycolytic capacity (FIG. 9C).

EXAMPLE 11

BLOCKADE OF LACTATE TRANSPORT VIA MCT1 REDUCES TOLEROGENIC PHENOTYPE OF VITD3- TOL-DC

[0142] Consistent with enhanced glycolytic metabolism, increased lactate production by tol- DC has been implicated in immuno-regulatory effects on T cell proliferation (Marin, E. et al. Human tolerogenic dendritic cells regulate immune responses through lactate synthesis. Cell Metab. 30, 1075-1090. e8 (2019)). Since transporter MCT1 was significantly upregulated in tol- DC (FIG. 23C) we used selective inhibitor BAY8002 to determine consequences of MCT1 inhibition on vitd3-tol-DC. BAY8002 treatment of both Ctrl and vitd3- tol-DC treatment dramatically reduced lactate in culture media and affected glucose consumption by vitd3 -tol-DC (FIG. 23B). While not affecting immune markers on control cells, BAY8002 significantly reduced expression of tolerogenic markers ILT3, CD141 including PDL1 vitd3-tol-DC (FIG. 23A).

EXAMPLE 12

VITD3 AND DEXAMETHASONE IMPACTS POPULATION-SPECIFIC FUNCTIONAL METABOLIC STATES AND ALTERS DYNAMICS OF IMMUNE PHENOTYPES

[0143] To dissect population-specific metabolic heterogeneity of inflammatory vs tol-moDC, analysis of single-cell scMEP scores confirmed significant enhancement and correlative upregulation of OXPHOS and glycolysis pathways in tol-moDC (FIG. 10A). Unsupervised tSNE clustering based on the median expression of metabolic scMEP markers effectively delineated control from tolerogenic treatments across all time points (FIG. 10B). In contrast to immune marker clustering (FIG. 8 A), metabolic parameter separations of inflammatory from tolerogenic samples yielded a continuum of transitional metabolic states, depicted by heatmap overlay of scMEP pathway scores (FIG. 10B). While maintaining spatial distinctions between control and treatment groups, underlying single-cell metabolic pathway scores elegantly resolve metabolically similar and distinct populations in moDC cultures with respective immune signatures (FIG. 10B). Changes in spatial distribution of metabolic patterns segregated controls from tol-moDC with reduced HLA-DR⁺, CD86⁺ and CDlc⁺ with retention of CD14⁺ and elevated PD-L1⁺ characteristics. However, despite the lack of full activation of DC maturation markers in tol-moDC, here it is shown that CDlc¹¹¹ and CDSb¹¹¹ cells still retained their metabolic identity, in which CD I c¹¹¹ cells resided in glycolytic space, while CDSb¹¹¹ skewed towards OXPHOS phenotype. With reduced CDlc¹¹¹ and CDSb¹¹¹ cell frequency the spatial differentials persisted at later 24h-stage of maturation (FIG. 9D). Another interesting phenotypic pattern revealed that expression of hallmark maturation markers HLA-DR and CD86 associated with higher mitochondrial/OXPHOS regions (FIG. 10B) and exhibited significant inverse relationship with glycolytic metabolism (FIG. 9E).

[0144] Next, concurrently processed SCENITH samples treated with oligomycin, prior to translation monitoring (FIG. 9F), were taken advantage of. As demonstrated in FIG. 10C, the lower part of the puromycin histogram represents “mitochondrial-dependent” (blue) cells with blocked translation, which are unable to utilize or switch effectively to glycolysis. As previously reported (Arguello et al., 2020) cells at the higher spectrum of translation in the presence of oligomycin are able to produce ATP independent of mitochondrial respiration and are labeled as “glycolytic” (red). Unsupervised tSNE clustering of oligomycin-treated single-well 4h mDC samples based on immune parameters showed interesting polarization of mitochondrial and glycolytic cell populations in control and tol-moDC (FIG. 10C). This analysis validated scMEP findings and showed that the opposing metabolic populations, varied in marker expression in which HLA-DR and CD86 colocalized with mitochondrial and CDlc and CD 14 predominantly associated with glycolytic clusters (FIG. 10C).

[0145] It was observed that the subset of moDC with the most advanced maturation phenotype, (exhibiting highest levels of HLA-DR and CD86 expression) are primarily dependent on mitochondrial respiration (FIG. 10D). Conversely, cells in transitional state with lowered maturation status are still glycolytic or can readily switch/utilize glycolysis in the absence of mitochondrial respiration (FIG. 10D). Importantly, cells cultured under tolerogenic conditions express only moderate levels of differentiation markers with elevated CD14, PD-L1 and CD141 irrespective of their metabolic profile. While p-AMPK was largely unaltered, both iNOS and p- mTOR levels were upregulated in glycolytic cells irrespective of treatment (FIG. 10D). Integrative heatmap of gMFI further depicts the underlying co-expression patterns of immune and signaling markers that differentiate mitochondrial from glycolytic cell populations with respect to inflammatory or tolerogenic moDC phenotype (FIG.10E). Lastly, to determine the status of mTOR and AMPK phosphorylation levels in mitochondrial and glycolytic cell populations, oligomycin treated data sets were subdivided into 3 quantiles encompassing low, medium, and high puromycin expression as diagramed (FIG. 10F). This enabled conformation that p-mTOR:pAMPK ratio was persistently elevated in both tol-moDC types (FIG. 9G). Importantly p-mTOR:pAMPK ratio and was significantly higher in glycolytic quantile 3 moDC populations at all maturation stages and treatments (FIG. 10E, 10F).

EXAMPLE 13

ELEVATED GLYCOLYTIC CAPACITY CONFERS MATURATION RESISTANCE AND DE-DIFFERENTIATED IMMUNOSUPPRESSIVE PHENOTYPE OF TOL-MOPC

[0146] Next, to reflect the metabolic heterogeneity associated with natural differentiation stochasticity of in vitro differentiating control moDC cultures and compare these to maturationdeficiencies and deregulated metabolism of vitd3 and dexamethasone-induced tol-moDC, the maturation stages of both control and tol-moDC were classified into high, mid, and low quantiles based on their HLA-DR expression range (FIG. 11 A, FIG. 12 A) and their SCENITH parameters were analyzed. Tolerogenic conditions reduced frequencies of high-DC populations, and it was found using SCENITH that high-DC with the best differentiation quality exhibit a statistically significant increase in mitochondrial dependence and lowest glycolytic capacity irrespective of control or tolerogenic culture conditions (FIG. 1 IB). Analysis of OXPHO S, FAO and AA scMEP pathway scores largely validated this metabolic SCENITH classification and showed that tol-moDC have overall increased expression of scMEP scores as compared to controls (FIG. 12B, 12C). Glycolytic scores strongly agreed with SCENITH in tolerogenic conditions and exhibited less differential expression between DC classes in control wells. Correlative analysis between SCENITH and scMEP measurements further pointed out that tol-moDC exhibit increased coordinate expression of OXPHOS and glycolytic scMEP scores (FIG. 11C). Heatmap analysis of immune marker expression demonstrated that tolerogenic moDC do not just represent stochastically delayed DC maturation lineage (FIG. 1 ID). Although with diminished frequencies, tol-moDC in the highest moDC class are not equivalent to the inflammatory counterparts. Despite their close to equivalent levels of HLA-DR and CD86 expression, high-DC class tol-moDC are marked by unique immunoregulatory receptor signatures. Furthermore, the results suggest that metabolic reprogramming of tol-moDC is not due to a proportional switch in metabolic pathways, but rather due to overall enhancement of metabolic pathway activity.

Discussion of Examples 1-13

[0147] Metabolism has a critical impact on DC activation, and differences in metabolic wiring have been attributed to distinct DC subtypes (Audiger et al., 2020; Basit et al., 2018; Du et al., 2018), differentiation stimuli (Fliesser et al., 2015) and T-cell priming stages (Patente et al., 2019a), murine vs human origin (Amiel et al., 2012), immunotolerance (Sim et al., 2016), mechanical stiffness (Chakraborty et al., 2021) and microenvironmental influence in various pathophysiological settings (Giovanelli et al., 2019). Precise understanding of immunometabolic networks has been limited due to low abundance of DC subsets in the blood as well as challenges associated with bulk metabolic measurement which may not reflect natural temporal stochasticity and more recently recognized heterogeneity of ex-vivo cell cultures (Helft et al., 2015; Sander et al., 2017). In this study, high-dimensional techniques enabled us to characterize changes in immune phenotypes with simultaneous profiling of major cellular metabolic axes in the context of inflammatory and tol-moDC development at the single-cell level. To date, multiparametric platforms have provided an excellent tool for identification of metabolic diversity in various aspects of T cell activation states and subtypes (Ahl et al., 2020; Hartmann et al., 2021; Levine et al., 2021). Furthermore, quantification of key metabolic proteins in the OXPHOS and glycolytic pathways predicted respective metabolic activity when combined with functional ECAR/OCR seahorse measurements (Ahl et al., 2020; Hartmann et al., 2021; Levine et al., 2021).

[0148] By combining SCENITH (Arguello et al., 2020) and scMEP -based quantifying metabolic enzymes, transporters and signaling factors (Hartmann et al., 2021) it is shown that changes in the metabolic regulome and coordinate activation of multiple metabolic pathways across distinct stages of moDC differentiation and maturation are at play (FIG. 13). Clustering of cell samples solely based on scMEP metabolic markers robustly segregated early precursors (Oh, 24h) from further differentiated moDC cell populations. scMEP metabolic tSNE clustering closely resembled dimensionality reduction analysis based on SCENITH phenotyping, in which 24h-matured DCs segregated from more closely related iDC and 4h mDC stages. Importantly, it is demonstrated that divergent expression of metabolic-markers remarkably delineated moDC immune features segregating CD14⁺ precursors, early differentiation stages expressing low levels of HLA-DR, CD206, CDl lb, CDl lc and maturation-dependent increase in CD86, PD-L1 as well subsets of CDlc⁺ moDC phenotypes.

[0149] Upregulation of critical lineage markers requires a switch from glycolytic precursors to mitochondrial metabolism during early moDC differentiation, 24h post-GM-CSF/IL4 stimulus. Based on functional measurements, day 5 iDC utilized 75% mitochondrial 25% glycolytic metabolism with primary 75%-dependence on glucose oxidation. The remaining 25% of energy sources constitute FAO and/or glutaminolysis. This metabolic profile was mirrored by coordinate activation of all measured components of the TCA/ETC pathway, FAO markers CPT1A, HADHA together with AA transporters ASCT2, CD98 and glutaminolysis enzyme GLS. In support of active mitochondrial biogenesis and elevated respiratory complex-dependent ROS formation during moDC differentiation (Prete et al., 2008; Zaccagnino et al., 2012), elevated expression of PGCla, TOMM20 and antioxidant GSS persisting through maturation was observed. To establish relationships between enzyme and metabolite transporter abundance and metabolic pathway dependence co-expression patterns of selected enzymes and metabolite transporters were benchmarked with normalized SCENITH parameters. The highest correlative metabolic analytes were used to define scMEP metabolic scores. While quantification of metabolic proteins robustly indicated cellular capacity for respective metabolic pathways in this study and multi-dimensional analyses of whole blood PBMC and T cells (Ahl et al., 2020; Hartmann et al., 2021), it was noted that not all measured regulators correlated with SCENITH functional measurements. Out of 5 measured ETC/TCA regulators, CytC was the least correlative with mitochondrial dependence, and beta-oxidation pathway enzyme HADHA and glutamine transporter ASCT2 were the highest correlating enzymes with FAO and/or glutaminolysis. Dichotomous pattern of glycolytic factors was observed with both, constitutive and inducible expression profiles out of which, MCT1 and PFKFB4 were highest correlating enzymes with glycolytic capacity across moDC differentiation. Monocytes maintain their glycolytic metabolism ex-vivo (Pence and Yarbro, 2019) and exhibited high expression of ENO1, GAPDH and LDHA, which are in the later steps of glycolytic pathway. As primary drivers of flux through glycolysis (Tanner et al., 2018) factors functioning in the early glycolytic steps, regulating glucose import (GLUT1), phosphorylation (PFKFB4, HK2) and the last glycolytic step of lactate export (MCT1) exhibited inducible upregulation and are critical checkpoints of glycolysis in moDC. While scMEP analysis revealed novel insights into metabolic regulome of inflammatory and tolerogenic moDC differentiation, here a subset of metabolic regulators was focused on. The discrepancy between expression of certain scMEP factors and SCENITH functional measurements suggests that additional isoenzymes, metabolite transporters and/or post-translational modifications are at play and will be further evaluated in future studies.

[0150] An important factor to consider is that metabolism of in vitro cultured cells may not properly reflect cellular metabolism in vivo, given the high concentration of glucose in media. Our comparative SCENITH analysis revealed in vitro cultured inflammatory DC exhibited highest glucose dependence (90%) as compared to total DC populations, pDC and cDCl from freshly isolated PBMCs. While these differences may be due to inherent differences in metabolic reprograming of specific DC subsets (Basit, F., Mathan, T., Sancho, D. & Vries, I. J. Mde Human dendritic cell subsets undergo distinct metabolic reprogramming for immune response. Front Immunol. 9, 2489 (2018)), we did observe that DC populations from PBMCs exhibited higher FAAO capacity (65%), suggesting their heightened utilization of FAO and Glutaminolysis as an energy source in vivo. Our results are consistent with a study by Patente et al. (Patente, T. A. et al. Human dendritic cells: their heterogeneity and clinical application potential in cancer immunotherapy. Front Immunol. 9, 3176 (2019)), demonstrating that TLR- stimulation causes increased mitochondrial content with high OXPHOS fueled by glutamine metabolism in pDC.

[0151] While activation of immature murine BMDCs depends on a switch to glycolytic reprogramming (Kelly and O’Neill, 2015), in human DC blood subsets, TLR-stimulation causes increased mitochondrial content with high OXPHOS fueled by glutamine metabolism in pDC, which is inversed in CDlc⁺ myeloid DC with reduced OXPHOS and elevated glycolysis (Patente et al., 2019b). By profiling single-cell dynamics, coordinate activation of multiple metabolic pathways is revealed across development and maturation of moDC. LPS/ZFNy activation of immature moDC engages transient increase in glycolysis and FAAO with OXPHOS remaining as a dominant metabolic pathway. Along with dynamic changes 4h post maturation, a transient decrease in PPP was observed, which is likely due to an increase in glucose shuttling through glycolysis. In contrast to homogenous co-activation of OXPHOS, FAO, AA, mitochondrial dynamics and glutathione synthesis, it was observed that glycolysis scMEP scores spanned a wider range of metabolic heterogeneity. This was directly related to divergent immune phenotypes of maturing moDC with CD86^H1 cells CD1C^H1 cells exhibiting preferential enrichment toward OXPHOS and glycolytic metabolism, respectively. CDSb¹¹¹ moDC also exhibited higher expression of additional DC markers including HLA-DR, CD206, PD-L1 and CD276. The results demonstrating that glycolytic metabolisms underlies polarization of CDlc expression in ex vivo differentiating moDC is consistent with a recent study by Basit et al., (Basit et al., 2018) demonstrating that TLR7/8-stimulation of circulating CDlc⁺ mDC induced mitophagy- dependent shift towards glycolysis with reduced expression of OXPHOS -related genes and mitochondrial content. It is further shown that elevated glycolytic capacity in CDlc¹¹¹ moDC might be explained by the enhanced expression of critical rate-limiting glycolytic factors GLUT1, MCT1 and PFKFB4, and elevated PDK1, which skews glucose homeostasis (Stacpoole, 2017) by preventing pyruvate shuttling into TCA into mitochondria.

[0152] Regulation of activation states of p-AMPK in the context of moDC development is largely unknown and its associations with immature and tolerogenic moDC phenotype are primarily based on AMPKla mRNA (Ferreira et al., 2015; Malinarich et al., 2015) and the use of pharmacological activator AICAR did not yield conclusive results (Ferreira et al., 2015), which may partly by due to its AMPK -independent effects including blockade of NF-KB transactivation (Kirchner et al., 2018; Visnjic et al., 2021). AMPK activation was shown to antagonize mTORCl signaling and glycolytic switch in murine BMDC (Krawczyk et al., 2010) and its inactivation fostered inflammatory function and maturation of murine macrophages and myeloid APC (Carroll et al., 2013). Kratchmarov et al., (Kratchmarov et al., 2018) showed that APMK modulated Flt3L-induced progenitor development into cDCl/cDC2 cell fate and AMPK/TRV activation was reported to mediate the suppressive effects of oleoyl ethanol ami de on TLR4/NF- xB-dependent BDMC maturation (Yao et al., 2019). In a recent study, CCR7-engagement blocked the pro-apoptotic role of AMPK and promoted survival of mDC (Lopez-Cotarelo et al., 2015). Contrary to the anti-inflammatory role in murine DC, activation of AMPK was recently shown to support OXPHOS reprogramming and interferon type I & III production in TLR7/9 activated human pDC (Hurley et al., 2021). Of note, another group reported that antiviral defense of human pDC is dependent on glycolysis (Bajwa et al., 2016). These differences point out that timing, context, and species-specific regulation of metabolism and dynamics of signaling by AMPK and mTOR are critical. In support of this, the kinetic profiles demonstrate temporal roles for mTOR and AMPK phosphorylation across moDC development and maturation. While AMPK activation correlated with increase in mitochondrial metabolism and engagement of auxiliary FAO and AA pathways towards iDC stage, GM-CSF-triggered early spike in p-mTOR is consistent with its role in survival of non-proliferative precursors (Woltman et al., 2003). Following maturation, p-mTOR levels mirrored transient increase in glycolytic capacity along with auxiliary metabolic pathways, which transitioned into dominant mitochondrial OXPHOS with increased AMPK activation and DC maturation marker expression. Inhibition of mTOR and AMPK signaling at the time of LPS/fFNy-induced maturation reduced inhibited lactate production and prevented upregulation of critical immune surface markers HLA-DR, CD86 and PD-L1 on DC. Therefore, this suggests a novel role for p-AMPK in human inflammatory moDC differentiation and propose that it is the temporal balance in mTOR: AMPK phosphorylation ratio that underlies metabolic reprogramming across distinct differentiation stages of moDC and reflects differences in mitochondrial vs glycolytic programs in CD1C^H1 and CD86^H1moDC populations.

[0153] Tolerogenic DC have been evaluated as promising cellular products for treatment of multiple autoimmune diseases. Clinically, human - tol-moDC are an abundant source of cells with the ability to perform antigen-specific presentation to polarize immune responses towards tolerance (Marin et al., 2018). However, reports using a variety of protocols used to generate tol- moDC in vitro (Yoo and Ha, 2016) show that metabolic plasticity and the heterogeneous nature associated with inherent epigenetic and transcriptional reprogramming is a cofounding factor in precise understanding of tol-moDC and requires the use of high-dimensional phenotyping (Megen et al., 2021; Navarro-Barriuso et al., 2018). The scMEP revealed simultaneous upregulation of TCA/ETC machinery, glycolytic factors, which was further confirmed by functional SCENITH measurements showing elevated upregulated glycolysis, OXPHOS in tol- moDC. This recapitulated previous studies (Ferreira et al., 2011, 2015; Garcia et al., 2021; Malinarich et al., 2015; Vanherwegen et al., 2019) and further showed that specific metabolic pathways are already elevated at the iDC stage and transient in nature following tol-moDC maturation, which was not previously described. In fact, tol-iDC and 4h-activated tol-mDC exhibited the highest diversity in metabolic pathway markers including upregulation of FAO (CPTla, HADHA), mitochondrial dynamics and components of glutamine metabolism regulating its transport (ASCT2) and conversion to TCA cycle intermediate a-ketoglutarate (GLS) (Miyajima, 2020). SCENITH analysis confirmed persistent increase glucose oxidation (75-80%) in tol-moDC, known to fuel glycolysis and TCA cycle (Garcia et al., 2021; Marin et al., 2019; Vanherwegen et al., 2019) and maintain tolerogenic phenotype of vitd3-tol-moDC (Ferreira et al., 2015). Importantly, scMEP analysis further showed that the transient 4h maturation stage likely represents highly dynamic metabolic window in tol-moDC with increased glutathione biosynthesis and capacity for auxiliary energy sources derived from fatty acids and glutaminolysis. MCT1 and PFKFB4 were the best predictors of glycolytic capacity SCENITH parameters and upregulated MCT1 correlated with increased glycolysis and lactate production by tol-moDC, which was shown to exert immune-suppressive effects on T cell proliferation on proinflammatory cytokine production (Marin et al., 2019). As a downstream target of mTORCl, it was observed that PPARy expression levels mirrored modal dynamics of p-mTOR activation in early precursors and following maturation, which is consistent with its role in transcriptional control of lipid metabolism in developing moDC (Szatmari et al., 2004, 2007). However, overactivation of PPARy was shown to be immunosuppressive as it reduced costimulatory markers expression, T-cell priming and proliferative capacity of DC (Nencioni et al., 2002) and in a recent study, Wnt5a-P-catenin-PPARy pathway promoted IDO-production and tolerogenic Treg-activating DC phenotype in melanoma (Zhao et al., 2018). Glucose-derived production of palmitate and palmitoleate was recently shown to fuel fatty acid synthesis pathway in vitd3 -generated tol-moDC regulating CD14 and IL10 expression by these cells (Garcia et al., 2021). While the precise implications of FA synthesis in tol-moDC are yet to be determined it is reported for the first time that all tol-moDC stages exhibited upregulated PPARy expression with striking increase in vitd3-dexa-treated samples at 4h post maturation. Therefore, it is hypothesized that enhanced PPARy signaling may be responsible for driving elevated FA synthesis and thereby influencing tolerogenic DC phenotype. [0154] A critical role for PI3K/Akt/mTOR pathway in promoting vitd3-induced glycolytic reprogramming and tolerogenic effects on moDC (Ferreira et al., 2015) has been previously established and here a similarly persistent increase in p-mTOR levels in both types of tol-moDC is shown. Inhibition of mTOR and AMPK using Rapamycin and Dorsomorphin respectively, significantly decreased tolerogenic marker expression in vitd3-mDC samples. While the use of Dorsomorphin is more challenging to interpret because it also reduced total mTOR levels, targeting mTOR signaling significantly and to greater extend reduced glucose consumption and lactate production in vitd3-tol-DC as compared to inflammatory DC. Because lactate exerts immune-suppressive effects on T cells (Marin, E. et al. Human tolerogenic dendritic cells regulate immune responses through lactate synthesis. Cell Metab. 30, 1075-1090. e8 (2019)), we asked whether blockade of lactate transporter MCT1 induces changes in metabolic and tolerogenic immune phenotype of DC. Blockade of MCT1 dramatically reduced lactate levels with modest effects on glucose consumption by DC. While not affecting immune markers on inflammatory DC, MCT1 inhibition significantly reduced expression of tolerogenic markers ILT3, CD141 including PD-L1 vitd3-tol-DC. This data provide evidence that modulations of cellular metabolism by targeting AMPK:mT0R signaling axis and/or inhibiting lactate transport influence tolerogenic phenotype of DC. However, these observations are further extended and it is proposed that while upregulation of both p-mTOR and p-AMPK signaling reflects the metabolic hyperactivation of tol-moDC it is the increased mTOR over AMPK signaling that further differentiates glycolytic subsets of moDC cultures irrespective of treatment conditions. Based on oligomycin-treated single-cell experiments, it is demonstrated that glycolytic metabolism underlies transitional and less-well matured immune phenotypes expressing moderate HLA-DR and CD86, which resembled maturation-deficient tol-moDC levels. On the other hand, cells with high mitochondrial dependence and elevated p-AMPK over p-mTOR signaling exhibited the highest maturation phenotype which further argues for a critical role of p- AMPK in moDC biology. It is also reported that vitd3 -treatment reduced CDlc¹¹¹ and CD86¹¹¹ cell frequencies, but these populations still retained their metabolic identity with glycolytic and OXPHOS metabolism, respectively.

[0155] Because it was observed that a wide range of OXPHOS and glycolytic scores represent metabolic heterogeneity of single culturing wells, low, mid and high HLA-DR and CD86 expressing cells were separated and their metabolic and immune features were compared between inflammatory and tolerogenic conditions. It was examined whether maturation delays and stochastic heterogeneity in inflammatory moDC parallels “maturation resistant” immune phenotype of tol-moDC. SCENITH profiling HLA-DR and CD86 high cells in both control and tolerogenic cultures exhibited similar metabolic pathway percent activation with highest OXPHOS and lowest glycolytic capacity. scMEP analysis further revealed that along with parallel increase in p-AMPK and p-mTOR levels, augmented OXPHOS, glycolysis and FAO activation is at play in vitd3 and dexa-vitd3-tol-moDC. In addition to augmented metabolic immune profiling, it was revealed that tol-moDC with the highest differentiation quality are marked by unique immunoregulatory receptor signatures, which do not reflect maturation delays of in vitro inflammatory moDC. Therefore, it is proposed that tol-moDC are not only locked in a “maturation-resistant” state with reduced expression of DC-lineage markers, but also resemble a cross-differentiated phenotype by retention of CD 14 and increased CD 141 and immunosuppressive checkpoint receptors PD-L1 and ILT3 (Chang et al., 2002; Zahorchak et al., 2018).

[0156] The use of single-cell high-dimensional techniques enabled us to validate as well as capture previously obscured immune-metabolic diversity of moDC. The results provide a strong basis for future monitoring of the metabolic states underlying phenotypic heterogeneity of immunogenic and tolerogenic DC physiology for management and improvement of DC-based immunotherapies (Patente et al., 2019b).

EXAMPLE 14

[0157] In-Vitro monocyte-derived DC Generation. Day 5 Immature Dendritic Cells (DC) were generated from cryopreserved elutriated healthy donor and patient monocytes using 1000 U/mL GM-CSF (Genzyme and Sanofi) and IL-4 (Cell Genix). Dendritic Cells were matured using rhlFNy (1000 U/mL) (Actimmune and R&D Systems) ++ LPS (250ng) (Sigma Aldrich) in DC medium for 24hrs. Immature and matured Dendritic Cells were harvested. Viability was analyzed using a Trypan Blue viability dye.

[0158] Microarray and Gene Expression Analysis (GSE111581). Total RNA from 5xl0e6 iDC, mDC and vaccine DC was isolated using RNAlater (Qiagen). HUGENE 2.0 ST arrays (Affymetrix) was used for gene expression analyses. [0159] Differential gene expression was analyzed using limma (Version 3.38.3) with weights generated by the voom function (Law, C.W., Chen, Y., Shi, W., and Smyth, G.K. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 75, R29. 10.1186/gb-2014-15-2-r29; Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA- sequencing and microarray studies. Nucleic Acids Res 43, e47-e47. 10.1093/nar/gkv007.) A log2 fold change of 2 and FDR-adj.p-value threshold of 0.05 was used to determine statistical significance. Web-based tool gProfiler (Raudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., and Vilo, J. (2019). g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res 47, W191-W198.

10.1093/nar/gkz369), was used for pathway analysis of significantly up and down-regulated gene sets. Gene set enrichment analysis (GSEA) was conducted using gene sets from the Molecular Signature Database (MSigDB, Version 6.2) in the C2 curated gene category (2005, PNAS 102, 15545-15550). Plots were generated using the R package ggplot2 (Version 3.1.1) and the javaGSEA application (version 3.0). Molecular interation networks were determined and visualized using the Cytoscape (version 3.7.0) (Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P - L., and Ideker, T. (2011). Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431-432. 10.1093/bioinformatics/btq675).

[0160] Metabolic Assays. Metabolic assays were performed as described in Santos et. al, 2019 (Santos, P.M., Menk, A.V., Shi, J., Tsung, A., Delgoffe, G.M., and Butterfield, L.H. (2019).

Tumor-Derived a-Fetoprotein Suppresses Fatty Acid Metabolism and Oxidative Phosphorylation in Dendritic Cells. Cancer Immunol Res 7, 1001-1012. 10.1158/2326-6066. cir-18-0513). Day 5 immature and Day 6 matured were plated at 100,000cells/well on Seahorse culture plates.

DMEM media was used, supplemented with 1% BSA, 25mM glucose, ImM pyruvate, and 2mM glutamine. The cells were analyzed using the Seahorse XFe96 (Agilent). Basal oxygen consumption and extracellular acidification rates were collected every 30 minutes. The cells were stimulated with oligomycin (2 pM), FCCP (0.5 pM), 2-deoxyglucose (10 mM) and rotenone/antimycin A (0.5 pM) to obtain maximal respiratory and control values. Fatty Acid Beta Oxidation was measured using the XF Palmitate Oxidation Stress Test Kit (Aligent). To measure oxidation levels, palmitate-BSA or BSA control (30 uls) was added to the wells immediately prior to running the assay. Cells were stimulated with oligomycin (2 pM), FCCP (0.5 pM), 2-deoxy glucose (10 mM) and rotenone/antimycin A (0.5 pM) to obtain maximal respiratory and control values. For both metabolic assays, the measurements were performed in triplicates. The OXPHOS and glycolytic indices were calculated as follows Basal respiration = OCR pre-Oligo - OCR post-RA

Maximal oxygen consumption = OCR _post-FCCP - OCR_post-RA Spare respiratory capacity = OCR _post-FCCP - OCR _Pre-oiigo Proton leak OCR post-oiigo - OCR post-RA

Maximal respiration of exogenous FA = = (CTRL)OCR _post-FCCP - (Palm/ETO)OCR _post-FCCP Basal glycolysis = ECAR _Pre-oiigo - ECAR _post-RA

Glycolytic capacity = ECAR _post-oiigo - ECAR _post-RA

[0161] SCENITH staining and data acquisition. SCENITH was performed as described in Arguello et al. (2020). SCENITH: A Flow Cytometry-Based Method to Functionally Profile Energy Metabolism with Single-Cell Resolution. Cell Metab 32, 1063-1075. e7.

10.1016/j .cmet.2020.11.007 and Adamik et al (2022). Distinct metabolic states guide maturation of inflammatory and tolerogenic dendritic cells. Nat Commun 13, 5184. 10.1038/s41467-022- 32849-1. SCENITH™ reagents kit (inhibitors, puromycin and antibodies) were obtained from www.scenith.com/try-it and used according to the provided protocol for in-vitro derived myeloid cells.

[0162] Briefly, 2xl0⁶ melanoma patient elutriated fraction 5 cells, 2xl0⁶ HD PBMC, or monocytic mDC cultures (2.5xl0⁵/24-well plate) harvested as indicated in the maturation protocol at day 6, were treated for 18 minutes with Control (DMSO), 2-Deoxy-Glucose (2-DG; lOOmM), Oligomycin (O; IpM), Etomoxir (4pM) (Selleckchem, S8244), CB-839 (3pM) (Selleckchem, S7655), a combination of 2DG and Oligomycin (DGO) or Harringtonine (H; 2pg/mL). Following metabolic inhibitors, Puromycin (final concentration 10 pg/mL) was added to cultures for 17 min. After puromycin treatment, cells were detached from wells using TypLE Select (Fisher Scientific, 505914419), washed in cold PBS and stained with a combination of Human TureStain FcX (Biolegend, 422301) and fluorescent cell viability dye (Biolegend, 423105) for 10 min 4°C in PBS. Following PBS wash step, primary antibodies against surface markers were incubated for 25 min at 4°C in Brilliant Stain Buffer (BD Biosciences, 563794).

Cells were fixed and permeabilized using True-Nuclear Transcription Factor Buffer Set (Biolegend, 424401) as per manufacturer instructions. Intracellular staining of puromycin and protein targets was performed for 1 h in diluted (lOx) permeabilization buffer at 4°C. Finally, data acquisition was performed using the Cytek Aurora flow cytometer. Primary conjugated antibody information used in SCENITH panels are listed in Tables 3 and Table 4.

Table 3

Table 4

All antibodies were titrated to reduce spillover and increase resolution using single stained DC (generated as described above) samples. Unstained cell controls used for autofluorescence extraction were generated with additions of respective metabolic inhibitors (C, 2DG, O, DGO). Samples were unmixed using reference controls generated in combination with stained Ultracomp beads (Fisher Scientific, 01-2222-41) and stained cells using the SpectroFlo Software v2.2.0.1. The unmixed FCS files were used for data processing and analysis using CellEngine (CellCarta). For in-vitro cultured mDC, manually gated CD14-HLA-DR+CD86+ cells were used for downstream analysis. For median expression analyses MFI expression values from respective mDC and circulating cell populations from CellEngine were imported into R environment for correlation and heatmap clustering analyses using the below described R packages.

[0163] Calculations used to derive SCENITH parameters: C= MFI of anti-Puro-Fluorochrome upon Control treatment 2DG= MFI of anti-Puro-Fluorochrome upon 2DG treatment O = MFI of anti-Puro-Fluorochrome upon Oligomycin treatment Eto =MFI of anti-Puro-Fluorochrome upon Etomoxir treatment Tele = MFI of anti-Puro-Fluorochrome upon CB-839 treatment DGO = MFI of anti-Puro-Fluorochrome upon 2DG + Oligomycin (DGO) treatment Glucose dependence = 100(C - 2DG)/(C-DGO) Mitochondrial dependence = 100(C - O)/(C-DGO) FAO dependence = 100(C - Eto)/(C-DGO)

Glutaminolysis dependence = 100(C - Tele)/(C-DGO) Glycolytic Capacity = 100 - Mitochondrial dependence FAAO = 100 - Glucose dependence

[0164] Single-cell metabolic regulome profiling (scMEP) by mass cytometry. scMEP analysis was performed as recently described. In short, monocytes and DC cultures were plated (2.5xl0⁶/6-well plate) and harvested at desired timepoints. Antibodies targeting metabolic features were conjugated in-house using an optimized conjugation protocol 8and validated on multiple sample types. Cells were prepared for scMEP analysis by incubation with small molecules to be able to assess biosynthesis rates of DNA, RNA and protein, cisplatin-based live/dead staining, PFA-based cell fixation and cryopreservation (dx.doi.org/10.17504/protocols.io.bkwkkxcw). Next, cells were stained with metabolic antibodies in a procedure that includes surface staining for 30min at RT, PFA-fixation for lOmin at RT, MeOH-based permeabilization for lOmin on ice, intracellular staining for Ih at RT and DNA intercalation (dx.doi.org/10.17504/protocols.io.bntnmeme). Finally, cells were acquired on a CyT0F2 mass cytometer (Fluidigm). Protein targets and antibody information used in scMEP are listed in Table 5.

Table 5.

[0165] Mass cytometry and spectral flow cytometry data processing and analysis. Raw mass spectrometry data were pre-processed, de-barcoded and imported into R environment using the flowCore package (version 2.0.1) (Hahne, F., LeMeur, N., Brinkman, R.R., Ellis, B., Haaland, P., Sarkar, D., Spidlen, J., Strain, E., and Gentleman, R. (2009). flowCore: a Bioconductor package for high throughput flow cytometry. Bmc Bioinformatics 70, 106. 10.1186/1471-2105-10-106). Values were arcsinh transformed (cofactor 5) and normalized 10 for downstream analyses based on previously reported workflow (Nowicka, M., Krieg, C., Crowell, H.L., Weber, L.M., Hartmann, F.J., Guglietta, S., Becher, B., Levesque, M.P., and Robinson, M.D. (2017). CyTOF workflow: differential discovery in high-throughput highdimensional cytometry datasets. FlOOOresearch 6, 748. 10.12688/fl000research.l 1622.2). Dimensionality reduction principal component analysis (PCA) and T-distributed stochastic neighbor embedding (tSNE) analyses were performed using stats (version 4.1.3) and Rtsne (version 0.15), respectively. Uniform Manifold Approximation and Projection (UMAP) was performed using R package umap. (version 2.9.0). For visualization and heatmap clustering we utilized R packages ggplot2 (version 3.3.3) and Compl exHeatmap (version 2.4.3) (Gu, Z., Eils, R., and Schlesner, M. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847-2849.

10.1093/bioinformatics/btw313)), respectively. Stats (version 4.1.3) was used for linear regression analyses and Spearman correlation coefficient correlation matrix for marker expression profiles was computed and visualized using the corrr (version 0.4.3), Hmisc (version 4.5.0) and corrplot (version 0.88) R packages.

[0166] Extracellular glucose and lactate measurements. Glucose and lactate levels were analyzed in DC culture supernatants using the BG1000 Blood Glucose Meter & test strips (Clarity, 75840-796) and Blood Lactate Measuring Meter Version 2 test strips (Nova Biomedical, Lactate Plus), respectively.

[0167] Serum Luminex. The human immune monitoring 65-Plex (Thermo-Fisher Procarta Plex) was used to analyze pro-inflammatory cytokines in cell-free supernatants harvested from HD (n=4) vs. melanoma patient (n=23) mDC.The human Checkpoint 14-plex kit (Thermo-Fisher ProcartaPlex) was also used for detection of culture supernatant checkpoint and costimulatory molecules.

[0168] IFN-y ELISPOT Assays. Detailed methodology for melanoma antigen (MA)-specific T cell responses is descried in Butterfield, et al. (2019). Multiple antigen-engineered DC vaccines with or without IFNa to promote antitumor immunity in melanoma. J Immunother Cancer 7, 113. 10.1186/s40425-019-0552-x and Santos, et al (2020). Impact of checkpoint blockade on cancer vaccine-activated CD8+ T cell responses. J Exp Medicine 277, e20191369. 10.1084/jem.20191369. To quantify specific responses to the melanoma antigens a positive response call (Yes) was defined as >10 spots counted per well and at least a 2-fold increase over baseline. To account for background, AdVLacZ response was subtracted from the AdV- melanoma antigen response.

[0169] Statistical Analysis. Multi-group comparisons were tested by one-way ANOVA with Tukey’s post-hoc test. The Shapiro-Wilk test was used to asses data normality, and statistical tests were performed using R (version 3.6.1). Wilcoxon signed-rank test (non-normal data) and Student’s t-test (normal data) was used for statistical analysis between 2 groups. Figure graphs were generated using the R package ggplot2 (version 3.1.1). Kaplan-Meier survival curve analysis and Cox proportional-hazards modeling were carried out using the R packages survival (version 3.1-8) and survminer (version 0.4.6).

EXAMPLE 15

HD AND MELANOMA PATIENT MPC EXHIBIT DIFFERENCES IN GLOBAL TRANSCRIPTIONAL PROFILES

[0170] In depth transcriptomic and immune-metabolic profiling was applied to analyze maturation states of melanoma-patient derived fFNy+LPS matured DC (mDC) used for autologous vaccine preparation. Figure 1 A FIG. 14 shows a schematic of the DC maturation protocol with time points used for the four profiling methods. Microarray profiling of melanoma patient mDC revealed differential gene expression of 2077 genes (not shown), which reflects the global phenotypic and transcriptomic changes during DC maturation (Schinnerling, K., Garcia- Gonzalez, P., and Aguillon, J.C. (2015). Gene Expression Profiling of Human Monocyte-derived Dendritic Cells - Searching for Molecular Regulators of Toler ogeni city. Front Immunol 6. 528. 10.3389/fimmu.2015.00528; Castiello, L., Sabatino, M., Jin, P., Clayberger, C., Marincola, F.M., Krensky, A.M., and Stroncek, D.F. (2011). Monocyte-derived DC maturation strategies and related pathways: a transcriptional view. Cancer Immunol Immunother 60, 457-466. 10.1007/s00262-010-0954-6; Zhong, W., Fei, M., Zhu, Y., and Zhang, X. (2009)).

Transcriptional profiles during the differentiation and maturation of monocyte-derived dendritic cells, analyzed using focused microarrays. Cell Mol Biology Lett 14, 587-608. 10.2478/sl 1658- 009-0023-3. Comparison of melanoma patient with the publicly available HD mDC microarray profiles (Jin, P., Han, T.H., Ren, J., Saunders, S., Wang, E., Marincola, F.M., and Stroncek, D.F. (2010). Molecular signatures of maturing dendritic cells: implications for testing the quality of dendritic cell therapies. J Transl Med 8, 4. 10.1186/1479-5876-8-4) further revealed that 725 and 818 genes were significantly up and down regulated respectively in melanoma mDC (not shown). The top 15 significant upregulated genes in melanoma mDC were RP11-44K6.2, CXCL11, IDO1, TNFAIP6, GBP4, CXCL10, ANKRD22, IL12B, IL2RA, CXCL9, C15orf48, ILIA, MT1M, MT1G, and MT1H. gProfiler pathway enrichment analysis of HD mDC showed significant upregulation MHC class I antigen-receptor processing/presentation and CCR5 chemokine receptor binding pathways. In contrast, VEGFA, TGFP receptor, NLRP3 inflammasome and Oncostatin M signaling were selectively upregulated, while antigen processing and pattern recognition receptor activity genes were down-regulated in melanoma mDC (not shown). False discovery rate (-loglO(FDR)) was used to denote significance for pathway enrichment. Gene set enrichment analysis (GSEA) of overlapping signatures showed selective downregulation of metabolic genes involved in TCA cycle and electron transport chain/OXPHOS in HD and FA/phospholipid metabolism and PPAR pathway in melanoma mDC (not shown). In the APC/Cytokine/Chemokine/Immune category, differences in IL-2, IL-3 and STAT3 signaling pathways in melanoma and Wnt, Rho GTPases MAPK4/6 signaling pathways in HD mDC were observed. In addition to HD vs melanoma differences, we explored differential gene signature correlations with clinical outcome groups (as previously described, “good” being PR+SD>6 mo.+ non-recurrent high risk NED; “bad” being PD +SD</=6 mo + recurrence post NED (Butterfield 2019, Maurer 2021, Adamik 2022). Among enriched immune and metabolic pathways, LPS/inflammatory response, NFKB targets, DC maturation, VEGF/Hypoxia, APC/MHC/Interleukin/Matrisome/Intergins and FAO/Sphingolipid metabolism associated with favorable clinical outcome (not shown). In contrary, genes in the DNA Repair, TCA ETC, mRNA processing, Interferon signaling and Golgi-ER transport/Glycosylation category were upregulated in the worse outcome mDC. While necessarily descriptive, these microarray differences indicated that many signaling pathways associated with cellular metabolism were important to examine functionally.

EXAMPLE 16

INCREASED GLYCOLYSIS AND REDUCED FAO METABOLISM DISTINGUISH MELANOMA MDC FROM HD

[0171] To confirm that the altered metabolic gene expression profile of melanoma patient mDC had downstream functional impact, we performed an assessment of mitochondrial and glycolytic metabolism in patient and HD cells using the Seahorse assay. There were no differences in oxygen consumption rate (OCR)-derived parameters between melanoma and HD mDC, yet melanoma mDC demonstrated a trend towards decreased maximal oxygen consumption rate and spare respiratory capacity (not shown). Significant increase in basal glycolysis was observed in both melanoma patient clinical groups, while the glycolytic capacity was significantly increased primarily in bad outcome mDC (not shown). To assess the capacity of mDC to oxidize exogenous fatty acids, we evaluated changes in OCR after addition of palmitate-BSA. Melanoma mDC exhibited significantly reduced ability to metabolize long-chain fatty acids compared to HD. Sequential addition of the ATP synthase inhibitor oligomycin enabled us to determine changes in proton leak, which was very low in HD, but significantly enhanced in a stepwise fashion in good and more so in bad outcome melanoma mDC (not shown). Increased proton leak along with reduced ATP-synthesis and increased reactive oxygen species production has been previously associated with age-related mitochondrial dysfunction, with inhibitory effects to phagocytosis and T cell MHC cross-presenting activity of aging DC (Chougnet, C.A., Thacker, R.I., Shehata, H.M., Hennies, C.M., Lehn, M.A., Lages, C.S., and Janssen, E.M. (2015). Loss of Phagocytic and Antigen Cross-Presenting Capacity in Aging Dendritic Cells Is Associated with Mitochondrial Dysfunction. J Immunol 195, 2624-2632. 10.4049/jimmunol.1501006). Together, the reduced FAO utilization activity and increased glycolytic capacity with the increased proton leak across the membrane further supports mitochondrial bioenergetic dysfunction in melanoma derived mDC.

EXAMPLE 17

INCREASE MITOCHONDRIAL METABOLISM, FAO AND GLUTAMINOLYSIS IN MDC ASSOCIATE WITH INCREASED SURVIVAL IN MELANOMA PATIENTS

[0172] We employed the single-cell energetic metabolism by profiling translation inhibition (SCENITH) assay to both further validate our Seahorse observations, and also determine the impact of metabolic alterations on the immune phenotype of melanoma mDC (Arguello, R. J., Combes, A.J., Char, R., Gigan, J.-P., Baaziz, A.I., Bousiquot, E., Camosseto, V., Samad, B., Tsui, J., Yan, P., et al. (2020). SCENITH: A Flow Cytometry-Based Method to Functionally Profile Energy Metabolism with Single-Cell Resolution. Cell Metab 32, 1063-1075. e7.

10.1016/j.cmet.2020.11.007; Adamik, J., Munson, P.V., Hartmann, F.J., Combes, A. J., Pierre, P., Krummel, M.F., Bendall, S.C., Arguello, R.J., and Butterfield, L.H. (2022). Distinct metabolic states guide maturation of inflammatory and tolerogenic dendritic cells. Nat Commun 13, 5184. 10.1038/s41467-022-32849-l; Verberk, S.G.S., Goede, K.E. de, Gorki, F.S., Dierendonck, X.A.M.H. van, Arguello, R. J., and Bossche, J.V. den (2022). An integrated toolbox to profile macrophage immunometabolism. Cell Reports Methods 2, 100192.

10.1016/j.crmeth.2022.100192). The use of metabolic inhibitors 2DG, Oligomycin, Etomoxir and CB-839 in SCENITH enabled us to derive percentual parameters of metabolic activity in mDC. Consistent with Seahorse analysis, HD DC exhibited trends towards increased mitochondrial dependence and FAO with reduced glycolytic capacity compared to melanoma mDC as determined by linear regression results for SCENITH metabolic parameter associations with HD (n=3), good (PR/SD/NED1, n=13), and bad (PD/NED2, n=17) outcome groups (not shown). Significant reductions in glutaminolysis dependence from 18.3% in HD to 15.6% and 8.6% in good and bad outcome respectively was observed in melanoma mDC when median expression values were compared for changes in percentual SCENITH parameters in mature mDC between HD (n=3), good (PR/SD/NED1, n=13), and bad (PD/NED2, n=17) outcome groups (not shown). Additional comparisons using SCENITH revealed close to significant decrease in mitochondrial dependence from 84.4% to 76.4%, with corresponding increase in glycolytic capacity from 15.6% to 23.6%, with 7% decrease in glutaminolysis dependence in bad outcome groups (not shown). We also observed that melanoma mDC significantly increased the overall rate of protein synthesis as shown by comparisons of median MFI expression profiles for mDC between good and bad outcome groups (not shown). SCENITH metabolic parameters were divided into binary high and low categories based on selected optimal cutoff values using the maximally selected rank statistics (Lausen, B., Lerche, R., and Schumacher, M. (2002). Maximally Selected Rank Statistics for Dose-Response Problems. Biometrical J 44, 131-147. 10.1002/1521-4036(200203)44:2<131::aid-bimj l31>3.0.co;2-z) (not shown). Cox’s proportional hazards models based on these binary categories show that higher mitochondrial dependence in patient mDC was significantly associated with longer OS and PFS rate. FAO and glutaminolysis dependence showed close to significant values (not shown). Kaplan-Meier (KM) survival analysis comparing SCENITH metabolic differences further confirmed significant associations between mitochondrial dependence (as well as trending FAO and glutaminolysis dependence) with longer OS and PFS rate (FIG. 15). In the clinical trial, the mDC were used to generated adenovirally antigen-engineered DC vaccines, we performed ex vivo ELISPOT assays to detect IFNy-producing CD8 and CD4 T cell responses specific to the encoded melanoma- associated antigens Tyrosinase, MART-1 and MAGE-A6. While we did not see significant associations between metabolic parameters in patient mDC and melanoma antigen-specific T cell responses, SCENITH percentual metabolic parameters were stratified by absence or presence of positive CD8, CD4, combined CD8+CD4 IFN-y to melanoma antigens and showed an increased mitochondrial and FAO dependence showed a trend towards increased T cell activation in CD8 and CD4 T-cells respectively (not shown).

EXAMPLE 18

MELANOMA MDCS WITH THE HIGHEST GLYCOLYTIC CAPACITY EXHIBIT ABERRANT EXPRESSION OF

INFLAMMATORY DC MARKERS [0173] SCENITH assay analysis integrated a full spectrum of DC phenotypic markers and the co-expression patterns of immune and signaling markers, the underlying changes in metabolic percentual parameters as well as clinical outcome and melanoma antigen-specific T cell responses in melanoma compared to HD mDC data (FIG. 16 A). Here we show that immune and co-stimulatory molecules HLA-DR, CD86, CD206, CD40 as well as the inhibitory checkpoint molecule ILT3 and were significantly over-expressed in worse outcome patient mDC (not shown). To gain mechanistic insights into signaling pathways regulating melanoma mDC metabolism, we employed antibodies recognizing the total and phosphorylated forms of AMPK (Thr-183/172) and p-mTOR (Ser-2448) (Table 3). These molecules were identified as a key regulatory node in our analysis of HD DC polarized to either inflammatory or tolerogenic profiles. Both p-mTOR and p-AMPK levels trended towards upregulation in bad outcome patients, however the p-AMPK:p-mTOR ratio increased in good outcome patients (not shown). This is consistent with increased mitochondrial dependence in good outcome mDC, as well as our previous study that demonstrated an increased p-AMPK:p-mTOR ratio plays an important role in maintaining mitochondrial metabolism of differentiated mDC..Next, we analyzed SCENITH profiles from mDC in oligomycin-treated samples (FIG. 16B). Oligomycin-treated cells at the higher spectrum of protein synthesis that are able sustain metabolic activity independent of mitochondrial respiration are indicative of high glycolytic capacity (“glycolytic”), while cells that are “mitochondrial-dependent” are unable to utilize or switch effectively to glycolysis (FIG. 16B) (Arguello, R.J., Combes, A.J., Char, R., Gigan, J.-P., Baaziz, A.I., Bousiquot, E., Camosseto, V., Samad, B., Tsui, J., Yan, P., et al. (2020). SCENITH: A Flow Cytometry-Based Method to Functionally Profile Energy Metabolism with Single-Cell Resolution. Cell Metab 32, 1063-1075. e7. 10.1016/j. cm et.2020.11.007). Analysis of mDC cell proportions showed that the worse responders contained the highest number of cells in the highest glycolytic quantile as compared to HD, good and stable disease outcome groups (FIG. 16B).

[0174] Phosphorylated AMPK and mTOR levels were gradually elevated in increasingly glycolytic cells, but the ratio of the two factors was skewed towards increased pAMPK in cell populations with the highest mitochondrial dependence (FIG. 16B). We also observed distinct patterns of DC immunophenotypic marker expression between distinct metabolic populations. While DC markers including fatty acid transporter CD36 and CDlc did not differ among the metabolic groups, expression of ILT3, HLA-DR, CD86, PD-L1, CD206 and CD40 was significantly associated with glycolytic populations (FIG. 16B). ICOSLG exhibited the opposite pattern and was elevated in mitochondrial-dependent mDC. These data further support that the underlying metabolic states can influence immune phenotypes of maturating DCs. Distinct DC surface markers can be prone to altered expression based on the glycolytic or mitochondrial polarization of mDC.

[0175] Dimensionality reduction of the four metabolic mDC states solely based on 12 immune DC surface markers showed that patient mDC in the glycolytic groups are more phenotypically diverse compared to the more uniform mitochondrial populations (which also clustered in the vicinity of the HD samples (not shown)). We compared immune marker expression among HD and clinical outcome groups in the highest glycolytic and mitochondrial populations. This analysis revealed that it is the glycolytic cells within the worse outcome patient mDC populations which exhibit the increase in pro-inflammatory DC markers HLA-DR, CD86, CD40, PD-L1, CDlc and ILT3 (not shown). As suggested by more uniform UMAP clustering, the mitochondrial patient mDC outcome groups exhibited less variation in the overall immune marker expression profiles and trended toward downregulation as compared to HD (not shown). This single cell-based analysis approach provides further insight into the bulk Seahorse measurements and initial SCENITH results (not shown) to show the effects of underlying changes in glycolytic metabolism on the immune phenotypes of patient-derived mDC that would be otherwise be impossible to detect. Collectively, these results suggest that mDC from melanoma patients, and, predominantly of the worst outcome group, contain highest number of glycolytic cells, which overexpress multiple immune inflammatory surface markers.

EXAMPLE 19

DISTINCTIONS BETWEEN HD AND MELANOMA CELLS ARE REFLECTED BY CHANGES IN METABOLIC

REGULOME [0176] In parallel with SCENITH, we employed mass cytometry based single-cell profiling of the metabolic regulome to integrate functional metabolic changes with quantification of metabolite transporters, enzymes and signaling factors across major cellular metabolic axes in immature and mature DC states (Adamik, J., Munson, P. V., Hartmann, F. J., Combes, A. J., Pierre, P., Krummel, M.F., Bendall, S.C., Arguello, R.J., and Butterfield, L.H. (2022). Distinct metabolic states guide maturation of inflammatory and tolerogenic dendritic cells. Nat Commun 13, 5184. 10.1038/s41467-022-32849-l; Hartmann, F.J., Mrdjen, D., McCaffrey, E., Glass, D.R., Greenwald, N.F., Bharadwaj, A., Khair, Z., Verberk, S.G.S., Baranski, A., Baskar, R., et al. (2021). Single-cell metabolic profiling of human cytotoxic T cells. Nat Biotechnol 39, 186-197. 10.1038/s41587-020-0651-8) (Table 5, FIG. 17A). Heatmap clustering using solely metabolic molecules enabled us to visualize patient iDC and mDC-specific scMEP regulome differences with overlayed immune phenotypes. While we did not observe a clinical outcome specific clustering trend, HD mDC cells grouped together along with several good outcome patients. We noted that in the mDC, scMEP markers segregated cell populations with higher HLA-DR vs CD1 lb and CD14 expression profiles (FIG. 17A). Differential expression analysis for scMEP markers between HD vs good (top) and bad (bottom) outcome groups revealed significant upregulation of metabolic TCA/ETC regulators (CytC, ATP5A) along with glutaminolysis enzyme glutaminase (GLS) in HD as compared to melanoma patient mDC (not shown). This was consistent with the overall observed increase in mitochondrial and glutaminolysis metabolic functions as measured by Seahorse and SCENITH assays (not shown).

[0177] HD also expressed higher levels of PPARy co-activator-la (PGCla), which was used in our panel for monitoring overall mitochondrial biogenesis and dynamics (Zaccagnino, P., Saltarella, M., Maiorano, S., Gaballo, A., Santoro, G., Nico, B., Lorusso, M., and Prete, A.D. (2012). An active mitochondrial biogenesis occurs during dendritic cell differentiation. Int J Biochem Cell Biology 44, 1962-1969. 10.1016/j.biocel.2012.07.024; Stenmark, K.R., and Tuder, R.M. (2018). Peroxisome Proliferator-activated Receptor y and Mitochondria: Drivers or Passengers on the Road to Pulmonary Hypertension? Am J Resp Cell Mol 58, 555-557.

10.1165/rcmb.2017-0318ed). Glutathione synthase (GSS) is involved in ROS detoxification (Ghezzi, P. (2011). Role of glutathione in immunity and inflammation in the lung. Int J Gen Medicine 4, 105-113. 10.2147/ijgm.sl5618) and its expression is significantly lower in worst outcome mDC compared to HD. Because deficiencies in mitochondrial glutathione have been implicated in increased ROS production (Bilbao, F.D., Arsenijevic, D., Vallet, P., Hjelle, O.P., Ottersen, O.P., Bouras, C., Raffin, Y., Abou, K., Langhans, W., Collins, S., et al. (2004). Resistance to cerebral ischemic injury in UCP2 knockout mice: evidence for a role of UCP2 as a regulator of mitochondrial glutathione levels. J Neurochem 89, 1283-1292. 10.1111/j .1471- 4159.2004.02432.x), decreased levels of GSS in melanoma mDC may contribute to the increased protein leak observed by Seahorse measurement (not shown). PD-L1 and HLA-DR were immune markers significantly downregulated in melanoma patient mDC (not shown). While the differential expression of multiple scMEP factors overlapped between good and bad outcome groups, mDC from bad outcome groups exhibited more pronounced and significant changes in scMEP marker differences from HD (not shown). Additional comparisons reveal that numerous TCA/ETC scMEP molecules showed reduced expression in melanoma patient mDC (FIG. 17B). The lactate transporter MCT1, which was the most robust marker correlating with glycolytic metabolism in monocyte-derived mDC in our recent study exhibited an increased expression trend in melanoma mDC (FIG. 17B). Consistent with reduced FAO capacity, P-oxidation pathway enzyme HADHA exhibited a decreased expression trend in melanoma mDC (FIG. 17B).

[0178] To determine whether immune scMEP marker expression correlated with ex-vivo melanoma antigen-specific T cell responses, we observed that CD11c and PD-L1 were significantly elevated in mDCs from patients with positive CD8 and combined CD8+CD4 T cells responses (FIG. 17C). We did not detect any significant differences in scMEP metabolic marker expression with respect CD8/CD4 T cell responses (not shown).

EXAMPLE 20

DISTINCTIONS BETWEEN HD AND PATIENT DC ARE REFLECTED BY CHANGES IN METABOLIC REGULOME

[0179] The functional implication of increased MCT1 expression and glycolytic metabolism was further demonstrated by measuring the byproduct of glycolytic pathway activity, lactate, as well as glucose in mDC supernatants. Lactate levels correlated with MCT1 expression and were significantly increased in culture media from melanoma patient-derived cells particularly at the iDC differentiation stage (FIG. 17D). Lactate levels inversely correlated with the glucose concentration in media, indicating increased glucose consumption (lowered glucose in media) by melanoma DC (not shown). Lactate is a potent immunosuppressive metabolite in the context of oncogenesis and inflammation, and has been considered a predictive or prognostic biomarker of clinical response in the clinic (Hayes, C., Donohoe, C.L., Davem, M., and Donlon, N.E. (2021). The oncogenic and clinical implications of lactate induced immunosuppression in the tumour microenvironment. Cancer Lett 500, 75-86. 10.1016/j.canlet.2020.12.021). KM survival analysis comparing levels of lactate in iDC culture supernatant confirmed that increased lactate secretion by DC significant correlated with inferior OS rate of patients (FIG. 17E).

EXAMPLE 21

DC CYTOKINE EXPRESSION PANEL

[0180] To gain further insights into the protein secretion profiles of the patient mDC, culture supernatants were tested for cytokines, chemokines and growth factors, as well as secreted or shed checkpoint and costimulatory molecules (HD (n=4) vs. melanoma patient (n=23)) (FIG.

18). A heatmap showing the cumulative data clustered by clinical outcomes and indicating CD4+ and CD8+ T cell response results is in FIG. 18. Patients with PD show the least secretion of any of the proteins measured. The statistical significance of these results with clinical outcome indicates that DC secreting higher levels of many of the analytes associates with positive outcome (not shown). While it is surprising that the T and NK cell growth and survival factor IL- 15 was associated with poor outcome, this may be due to the very low levels of this protein measured overall, and particularly high expression in a single PD patient culture.

[0181] A large number of secreted proteins associated with good and bad clinical response were significant in linear regression analysis (not shown). In many cases melanoma patient DC secreted much lower levels of analytes than HD, regardless of clinical outcome (HGF, IL-12p70, TNFA). While IL-12p70 has been a major focus for DC due to its promotion of Thl/Tcl immunity, in this and other studies (Butterfield 2019), the amount of IL-12p70 secreted by DC did not correlate with T cell response or clinical outcome. Other analytes showed a trend of being highest in HD, then good outcome and lowest in bad outcome patient DC (CXCL13, eotaxin, IL-23, IL-31, IL-5, MCP-1, MIG, sCD40L, TIM3, TRAIL). These proteins are associated with multiple response profiles, including Thl, Th2 and myeloid cell trafficking. There is also a subset of analytes which are strong in both HD and good outcome patient cells, but reduced in bad outcome patients (IFNa, IL-18, IL-la, IL-21) all of which have type 1 skewing and antitumor immunity activity.

EXAMPLE 22

IMMUNE PHENOTYPE ALTERATION OF CIRCULATING MONOCYTE AND DC SUBSETS DIFFERENTIATE CANCER PATIENTS FROM HD PATIENTS AND ELEVATED ILT3 AND PD-L1 EXPRESSION ASSOCIATE WITH WORSE PROGNOSIS

[0182] Given the clear impact of the metabolic state of DC vaccines on immune phenotype and clinical outcome of vaccinated patients, it was critical to determine whether the progenitor cells, the circulating monocytes, were already impacted at baseline in melanoma tumor-bearing patients. To characterize metabolic states of circulating monocytes as well as DC subsets in melanoma patients, we combined SCENITH with the high-dimensional immune phenotyping panel based on the most recent literature classifying monocytes, plasmacytoid and conventional DC subpopulations (Mair, F., and Liechti, T. (2021). Comprehensive Phenotyping of Human Dendritic Cells and Monocytes. Cytom Part A 99, 231-242. 10.1002/cyto.a.24269; Roussel, M., Ferrell, P.B., Greenplate, A.R., Lhomme, F., Gallou, S.L., Diggins, K.E., Johnson, D.B., and Irish, J.M. (2017). Mass cytometry deep phenotyping of human mononuclear phagocytes and myeloid-derived suppressor cells from human blood and bone marrow. J Leukocyte Biol 102, 437-447. 10.1189/jlb.5mal l l6-457r) (Table 4).

[0183] We observed a significant increase in plasmacytoid CD123+ DC (pDC) with decrease in conventional CD5+cDC2 and CD14-DC3 frequency in melanoma patients (not shown). There were no significant changes in cDCls and classical (cMo) and intermediate (iMo) showed a trend of an decrease and increase in frequency, respectively (not shown). Clustering analysis of the combination of inflammatory, classical and more recently defined lineage markers revealed specific patterns of key molecule expression, and delineated circulating monocyte and human DC subsets (FIG. 19A). PC A analysis using these 21 immune markers revealed cluster separation based on monocyte, plasmacytoid and conventional clusters (not shown). Overlay of the clinical responses showed separation of HD from melanoma groups, particularly in the monocyte and plasmacytoid clusters. Further evaluation of immune marker expression revealed that PD-L1 in majority of populations and CD36 on ncMo and pre-DC were significantly increased on heathy donors (not shown).

[0184] Additional comparisons showed significant upregulation ILT3 in iMo and cDCls with downregulation of PD-L1 and CD206 in iMo in non-responders (FIG. 19B). Cox’s regression analysis further demonstrated that increased expression of ILT3 on selected monocyte and conventional DC subtypes was significantly associated with decreased OS in melanoma patients. In contrast, CD11c expression on monocyte lineages is a protective predictor, as well as PD-L1 expression on conventional subsets, which is associated with improved progression free survival in melanoma patients (FIG. 19C).

EXAMPLE 23

METABOLIC CHANGES IN MONOCYTE/MYELOID CIRCULATING POPULATIONS UNDERLIE IMMUNE DIFFERENCES BETWEEN HD AND MELANOMA PATIENTS

[0185] Finally, we performed single cell analysis of concurrently tested oligomycin-treated SCENITH samples. Dimensionality reduction based on 21 immune markers using tSNE maps showed visual separation of monocyte and DC populations (not shown). Furthermore, overlay of puromycin expression quantiles from these oligomycin-treated samples enabled us to visualize the single cell glycolytic and mitochondrial states of these distinct populations (not shown).

[0186] Analysis of cell proportions within each metabolic quantile demonstrated that circulating classical to non-classical monocyte populations exhibit shift form glycolytic to mitochondrial metabolism. Within the conventional DC subsets cDCls have the highest proportion of cells with mitochondrial respiration quantile (not shown). The majority of cells in the pre-DC, CD5+cDC2 and DC3s populations utilize glycolytic metabolism. We compared cell subtype metabolic profiles between HD and melanoma patients stratified by clinical outcome. cMo and ncMo exhibited significant decrease in mitochondrial dependence with decreased trends in FAO and glutaminolysis (FIG. 20 A). iMo did not reveal significant metabolic changes while pDC and pre-DC showed a trend towards progressive decrease in glutaminolysis dependence in non-responders (FIG. 20A). Glucose dependence was significantly reduced in conventional cDCls and CD14+DC3s, while both cDC2 subtypes exhibit decreased mitochondrial dependence.

[0187] Based on the overall assessment of metabolic profiles, amino acid metabolism was more severely affected in melanoma patient circulating myeloid subsets compared to FAO. We further demonstrate that the effects of underlying metabolism on immune phenotypes is also reflected at the baseline circulating myeloid cell level. However, it is important to consider that there is variable expression of the immune markers in different circulating myeloid subtypes (FIG. 20B). HLA-DR expression particularly in iMo, pre-DCs, cDCls, cDC2s does not seem to be impacted by the underlying metabolic states. In contrary, ILT3 and PD-L1 levels on most circulating myeloid monocytes and DC subtypes is differentially expressed on glycolytic and mitochondrial populations respectively (FIG. 20B).

Discussion of Examples 14-23

[0188] Metabolism has a critical impact on DC activation, and differences in metabolic wiring have been attributed to distinct DC subtypes, differentiation stimuli and T-cell priming stages (Patente et al., 2019a), murine vs human origin¹¹ and microenvironment influence in multiple pathophysiological settings. Precise understanding of immunometabolic networks has been limited due the to low abundance of DC subsets in the blood as well as challenges associated with bulk metabolic measurement. To date, quantification of key metabolic proteins in OXPHOS and glycolytic pathways have predicted respective metabolic activity when combined with functional ECAR/OCR seahorse measurements. In our recent study with polarized HD DC and now here, analyzing cancer patient cells with mature clinical data, by combining functional SCENITH and scMEP quantification of metabolic enzymes, transporters and signaling nodes we show that changes in the metabolic regulome and coordinate activation of multiple metabolic pathways in mDC differentiation and maturation are important correlates of clinical outcome.

[0189] Single-cell metabolic score profiling enabled us to monitor dynamics of multiple pathways in cell populations before and after mDC differentiation. While the population-based microarray data accurately predicted the relevance of metabolic pathways to the difference between HD and cancer patient DC, the heterogeneity of the patient DC did not allow molecular pathway identification. The population-based Seahorse metabolic flux functional testing identified increased glycolytic capacity and basal glycolysis as important negative functional skewing in poor outcome patients, but the potential significance of other pathways was difficult to discern.

References

Ahl, P.J., Hopkins, R.A., Xiang, W.W., Au, B., Kaliaperumal, N., Fairhurst, A.-M., and Connolly, J.E. (2020). Met-Flow, a strategy for single-cell metabolic analysis highlights dynamic changes in immune subpopulations. Commun Biology 3, 305.

Amiel, E., Everts, B., Freitas, T.C., King, I.L., Curtis, J.D., Pearce, E.L., and Pearce, E.J. (2012). Inhibition of Mechanistic Target of Rapamycin Promotes Dendritic Cell Activation and Enhances Therapeutic Autologous Vaccination in Mice. J Immunol 189, 2151-2158.

Arguello, R.J., Combes, A.J., Char, R., Gigan, J.-P., Baaziz, A.I., Bousiquot, E., Camosseto, V., Samad, B., Tsui, J., Yan, P., et al. (2020). SCENITH: A Flow Cytometry-Based Method to Functionally Profile Energy Metabolism with Single-Cell Resolution. Cell Metab 32, 1063- 1075. e7.

Artyomov, M.N., and Bossche, J.V. den (2020). Immunometabolism in the Single-Cell Era. Cell Metab 32, 710-725.

Audiger, C., Fois, A., Thomas, A.L., Janssen, E., Pelletier, M., and Lesage, S. (2020). Merocytic Dendritic Cells Compose a Conventional Dendritic Cell Subset with Low Metabolic Activity. J Immunol 205, 121-132.

Bajwa, G., DeBerardinis, R.J., Shao, B., Hall, B., Farrar, J.D., and Gill, M.A. (2016). Cutting Edge: Critical Role of Glycolysis in Human Plasmacytoid Dendritic Cell Antiviral Responses. J Immunol 196, 2004-2009.

Basit, F., Mathan, T., Sancho, D., and Vries, I.J.M. de (2018). Human Dendritic Cell Subsets Undergo Distinct Metabolic Reprogramming for Immune Response. Front Immunol 9, 2489.

Carroll, K.C., Viollet, B., and Suttles, J. (2013). AMPKal deficiency amplifies proinflammatory myeloid APC activity and CD40 signaling. J Leukocyte Biol 94, 1113-1121.

Chakraborty, M., Chu, K., Shrestha, A., Revelo, X.S., Zhang, X., Gold, M.J., Khan, S., Lee, M., Huang, C., Akbari, M., et al. (2021). Mechanical Stiffness Controls Dendritic Cell Metabolism and Function. Cell Reports 34, 108609. Chang, C.C., Ciubotariu, R., Manavalan, J.S., Yuan, J., Colovai, A.I., Piazza, F., Lederman, S., Colonna, M., Cortesini, R., Dalla-Favera, R., et al. (2002). Tolerization of dendritic cells by TS cells: the crucial role of inhibitory receptors ILT3 and ILT4. Nat Immunol 3, 237-243.

Dalod, M., Chelbi, R., Malissen, B., and Lawrence, T. (2014). Dendritic cell maturation: functional specialization through signaling specificity and transcriptional programming. Embo J 33, 1104-1116.

D’Angelo, J. A., Dehlink, E., Platzer, B., Dwyer, P., Circu, M.L., Garay, J., Aw, T.Y., Fiebiger, E., and Dickinson, B.L. (2010). The Cystine/Glutamate Antiporter Regulates Dendritic Cell Differentiation and Antigen Presentation. J Immunol 185, 3217-3226.

Danova, K., Klapetkova, A., Kayserova, J., Sediva, A., Spisek, R., and Jelinkova, L.P. (2015). NF-KB, p38 MAPK, ERK1/2, mTOR, STAT3 and increased glycolysis regulate stability of paricalcitol/dexamethasone-generated tolerogenic dendritic cells in the inflammatory environment. Oncotarget 6, 14123-14138.

Du, X., Wen, J., Wang, Y., Karmaus, P.W.F., Khatamian, A., Tan, H., Li, Y., Guy, C., Nguyen, T.-L.M., Dhungana, Y., et al. (2018). Hippo/Mst signalling couples metabolic state and immune function of CD8a+ dendritic cells. Nature 558, 141-145.

Everts, B., Amiel, E., Windt, G.J.W. van der, Freitas, T.C., Chott, R., Yarasheski, K.E., Pearce, E.L., and Pearce, E.J. (2012). Commitment to glycolysis sustains survival of NO-producing inflammatory dendritic cells. Blood 120, 1422-1431.

Everts, B., Amiel, E., Huang, S.C.-C., Smith, A.M., Chang, C.-H., Lam, W.Y., Redmann, V., Freitas, T.C., Blagih, J., Windt, G.J.W. van der, et al. (2014). TLR-driven early glycolytic reprogramming via the kinases TBKl-IKKe supports the anabolic demands of dendritic cell activation. Nat Immunol 15, 323-332.

Ferreira, G.B., Kleijwegt, F.S., Waelkens, E., Lage, K., Nikolic, T., Hansen, D.A., Workman, C.T., Roep, B.O., Overbergh, L., and Mathieu, C. (2011). Differential Protein Pathways in 1,25- Dihydroxyvitamin D 3 and Dexamethasone Modulated Tolerogenic Human Dendritic Cells. J Proteome Res 11, 941-971.

Ferreira, G.B., Vanherwegen, A.-S., Eelen, G., Gutierrez, A.C.F., Van Lommel, L., Marchal, K., Verlinden, L., Verstuyf, A., Nogueira, T., Georgiadou, M., et al. (2015). Vitamin D3 Induces Tolerance in Human Dendritic Cells by Activation of Intracellular Metabolic Pathways. Cell Reports 10, 711-725.

Fliesser, M., Morton, C.O., Bonin, M., Ebel, F., Hiinniger, K., Kurzai, O., Einsele, H., and Loftier, J. (2015). Hypoxia-inducible factor la modulates metabolic activity and cytokine release in anti-Aspergillus fumigatus immune responses initiated by human dendritic cells. Int J Med Microbiol 305, 865-873. Garcia, A.M., Bishop, E.L., Li, D., Jeffery, L.E., Garten, A., Thakker, A., Certo, M., Mauro, C., Tennant, D.A., Dimeloe, S., et al. (2021). Tolerogenic effects of 1,25 -dihydroxyvitamin D on dendritic cells involve induction of fatty acid synthesis. J Steroid Biochem Mol Biology 277, 105891.

Ge, T., Yang, J., Zhou, S., Wang, Y., Li, Y., and Tong, X. (2020). The Role of the Pentose Phosphate Pathway in Diabetes and Cancer. Front Endocrinol 77, 365.

Ghezzi, P. (2011). Role of glutathione in immunity and inflammation in the lung. Int J Gen Medicine , 105-113.

Giovanelli, P., Sandoval, T.A., and Cubillos-Ruiz, J.R. (2019). Dendritic Cell Metabolism and Function in Tumors. Trends Immunol 40, 699-718.

Gu, Z., Eils, R., and Schlesner, M. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847-2849.

Hahne, F., LeMeur, N., Brinkman, R.R., Ellis, B., Haaland, P., Sarkar, D., Spidlen, J., Strain, E., and Gentleman, R. (2009). flowCore: a Bioconductor package for high throughput flow cytometry. Bmc Bioinformatics 10, 106.

Hartmann, F.J., Simonds, E.F., Vivanco, N., Bruce, T., Borges, L., Nolan, G.P., Spitzer, M.H., and Bendall, S.C. (2019). Mass Cytometry, Methods and Protocols. Methods Mol Biology 1989, 55-81.

Hartmann, F.J., Mrdjen, D., McCaffrey, E., Glass, D.R., Greenwald, N.F., Bharadwaj, A., Khair, Z., Verberk, S.G.S., Baranski, A., Baskar, R., et al. (2021). Single-cell metabolic profiling of human cytotoxic T cells. Nat Biotechnol 39, 186-197.

Helft, J., Bbttcher, J., Chakravarty, P., Zelenay, S., Huotari, J., Schraml, B.U., Goubau, D., and Reis e Sousa, C. (2015). GM-CSF Mouse Bone Marrow Cultures Comprise a Heterogeneous Population of CD1 lc+MHCII+ Macrophages and Dendritic Cells. Immunity 42, 1197-1211.

Herzig, S., and Shaw, R.J. (2018). AMPK: guardian of metabolism and mitochondrial homeostasis. Nat Rev Mol Cell Bio 19, 121-135.

Hurley, H.J., Dewaid, H., Rothkopf, Z.S., Singh, S., Jenkins, F., Deb, P., De, S., Barnes, B.J., and Fitzgerald-Bocarsly, P. (2021). Frontline Science: AMPK regulates metabolic reprogramming necessary for interferon production in human plasmacytoid dendritic cells. J Leukocyte Biol 109, 299-308.

Kamide, Y., Utsugi, M., Dobashi, K., Ono, A., Ishizuka, T., Hisada, T., Koga, Y., Uno, K., Hamuro, J., and Mori, M. (2011). Intracellular glutathione redox status in human dendritic cells regulates IL-27 production and T-cell polarization. Allergy 66, 1183-1192. Kelly, B., and O’Neill, L.A. (2015). Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res 25, 771-784.

Kirchner, J., Briine, B., and Namgaladze, D. (2018). AICAR inhibits NFKB DNA binding independently of AMPK to attenuate LPS-triggered inflammatory responses in human macrophages. Sci Rep-Uk 8, 7801.

Kratchmarov, R., Viragova, S., Kim, M.J., Rothman, N.J., Liu, K., Reizis, B., and Reiner, S.L. (2018). Metabolic control of cell fate bifurcations in a hematopoietic progenitor population. Immunol Cell Biol 96, 863-871.

Krawczyk, C.M., Holowka, T., Sun, J., Blagih, J., Amiel, E., DeBerardinis, R.J., Cross, J.R., Jung, E., Thompson, C.B., Jones, R.G., et al. (2010). Toll-like receptor-induced changes in glycolytic metabolism regulate dendritic cell activation. Blood 115, 4742-4749.

Levine, L.S., Hiam-Galvez, K.J., Marquez, D.M., Tenvooren, I., Madden, M.Z., Contreras, D.C., Dahunsi, D.O., Irish, J.M., Oluwole, O.O., Rathmell, J.C., et al. (2021). Single-cell analysis by mass cytometry reveals metabolic states of early-activated CD8+ T cells during the primary immune response. Immunity 54, 829-844. e5.

Lopez-Cotarelo, P., Escribano-Diaz, C., Gonzalez-Bethencourt, I.L., Gomez-Moreira, C., Deguiz, M.L., Torres-Bacete, J., Gomez-Cabanas, L., Fernandez-Barrera, J., Delgado-Martin, C., Mellado, M., et al. (2015). A Novel MEK-ERK-AMPK Signaling Axis Controls Chemokine Receptor CCR7-dependent Survival in Human Mature Dendritic Cells*. J Biol Chem 290, 827- 840.

Malinarich, F., Duan, K., Hamid, R.A., Bijin, A., Lin, W.X., Poidinger, M., Fairhurst, A.-M., and Connolly, J.E. (2015). High Mitochondrial Respiration and Glycolytic Capacity Represent a Metabolic Phenotype of Human Tolerogenic Dendritic Cells. J Immunol 194, 5174-5186.

Marin, E., Cuturi, M.C., and Moreau, A. (2018). Tolerogenic Dendritic Cells in Solid Organ Transplantation: Where Do We Stand? Front Immunol 9, 274.

Marin, E., Bouchet-Delbos, L., Renoult, O., Louvet, C., Nerriere-Daguin, V., Managh, A.J., Even, A., Giraud, M., Manh, T.P.V., Aguesse, A., et al. (2019). Human Tolerogenic Dendritic Cells Regulate Immune Responses through Lactate Synthesis. Cell Metab 30, 1075-1090. e8.

Megen, K.M. van, Chen, Z., Joosten, A.M., Laban, S., Zwaginga, J. -J., Natarajan, R., Nikolic, T., and Roep, B.O. (2021). 1,25-dihydroxyvitamin D3 induces stable and reproducible therapeutic tolerogenic dendritic cells with specific epigenetic modifications. Cytotherapy 23, 242-255.

Miyajima, M. (2020). Amino acids: key sources for immunometabolites and immunotransmitters. Int Immunol 32, dxaa019-.

Navarro-Barriuso, J., Mansilla, M.J., Naranjo-Gomez, M., Sanchez-Pla, A., Quirant-Sanchez, B., Teniente- Serra, A., Ramo-Tello, C., and Martinez-Caceres, E.M. (2018). Comparative transcriptomic profile of tolerogenic dendritic cells differentiated with vitamin D3, dexamethasone and rapamycin. Sci Rep-Uk 8, 14985.

Nencioni, A., Grunebach, F., Zobywlaski, A., Denzlinger, C., Brugger, W., and Brossart, P. (2002). Dendritic Cell Immunogenicity Is Regulated by Peroxisome Proliferator-Activated Receptor y. J Immunol 169, 1228-1235.

Nowicka, M., Krieg, C., Crowell, H.L., Weber, L.M., Hartmann, F.J., Guglietta, S., Becher, B., Levesque, M.P., and Robinson, M.D. (2017). CyTOF workflow: differential discovery in high- throughput high-dimensional cytometry datasets. FlOOOresearch 6, 748.

Patente, T.A., Pelgrom, L.R., and Everts, B. (2019a). Dendritic cells are what they eat: how their metabolism shapes T helper cell polarization. Curr Opin Immunol 58, 16-23.

Patente, T.A., Pinho, M.P., Oliveira, A.A., Evangelista, G.C.M., Bergami- Santos, P.C., and Barbuto, J.A.M. (2019b). Human Dendritic Cells: Their Heterogeneity and Clinical Application Potential in Cancer Immunotherapy. Front Immunol 9, 3176.

Pence, B.D., and Yarbro, J.R. (2019). Classical monocytes maintain ex vivo glycolytic metabolism and early but not later inflammatory responses in older adults. Immun Ageing 16, 3.

Prete, A.D., Zaccagnino, P., Paola, M.D., Saltarella, M., Celis, C.O., Nico, B., Santoro, G., and Lorusso, M. (2008). Role of mitochondria and reactive oxygen species in dendritic cell differentiation and functions. Free Radical Bio Med 44, 1443-1451.

Qian, C., and Cao, X. (2017). Dendritic cells in the regulation of immunity and inflammation. Semin Immunol 35, 3-11.

Raker, V.K., Domogalla, M.P., and Steinbrink, K. (2015). Tolerogenic Dendritic Cells for Regulatory T Cell Induction in Man. Front Immunol 6, 569.

Ritprajak, Kaewraemruaen, and Hirankarn (2019). Current Paradigms of Tolerogenic Dendritic Cells and Clinical Implications for Systemic Lupus Erythematosus. Cells 8, 1291.

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., and Muller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. Bmc Bioinformatics 12, 77.

Sander, J., Schmidt, S.V., Cirovic, B., McGovern, N., Papantonopoulou, O., Hardt, A.-L., Aschenbrenner, A.C., Kreer, C., Quast, T., Xu, A.M., et al. (2017). Cellular Differentiation of Human Monocytes Is Regulated by Time-Dependent Interleukin-4 Signaling and the Transcriptional Regulator NCOR2. Immunity 47, 1051-1066. el2.

Santos, P.M., and Butterfield, L.H. (2018). Dendritic Cell-Based Cancer Vaccines. J Immunol 200, 443-449. Sim, W.J., Ahl, P.J., and Connolly, J.E. (2016). Metabolism Is Central to Tolerogenic Dendritic Cell Function. Mediat Inflamm 2016, 1-10.

Snyder, J.P., and Amiel, E. (2019). Regulation of Dendritic Cell Immune Function and Metabolism by Cellular Nutrient Sensor Mammalian Target of Rapamycin (mTOR). Front Immunol 9, 3145.

Stacpoole, P.W. (2017). Therapeutic Targeting of the Pyruvate Dehydrogenase Complex/Pyruvate Dehydrogenase Kinase (PDC/PDK) Axis in Cancer. Jnci J National Cancer Inst 109.

Szatmari, I., Gogolak, P., Im, J.S., Dezso, B., Rajnavolgyi, E., and Nagy, L. (2004). Activation of PPARy Specifies a Dendritic Cell Subtype Capable of Enhanced Induction of iNKT Cell Expansion. Immunity 21, 95-106.

Szatmari, I., Tbrbcsik, D., Agostini, M., Nagy, T., Gurnell, M., Barta, E., Chatterjee, K., and Nagy, L. (2007). PPARy regulates the function of human dendritic cells primarily by altering lipid metabolism. Blood 110, 3271-3280.

Tan, Z., Xie, N., Cui, H., Moellering, D.R., Abraham, E., Thannickal, V.J., and Liu, G. (2015). Pyruvate Dehydrogenase Kinase 1 Participates in Macrophage Polarization via Regulating Glucose Metabolism. J Immunol 194, 6082-6089.

Tanner, L.B., Goglia, A.G., Wei, M.H., Sehgal, T., Parsons, L.R., Park, J.O., White, E., Toettcher, J.E., and Rabinowitz, J.D. (2018). Four Key Steps Control Glycolytic Flux in Mammalian Cells. Cell Syst 7, 49-62. e8.

Thomaz, L. de L., Peron, G., Oliveira, J., Rosa, L.C. da, Thome, R., and Verinaud, L. (2018). The impact of metabolic reprogramming on dendritic cell function. Int Immunopharmacol 63, 84-93.

Thwe, P.M., and Amiel, E. (2018). The role of nitric oxide in metabolic regulation of Dendritic cell immune function. Cancer Lett 412, 236-242.

Thwe, P.M., Pelgrom, L., Cooper, R., Beauchamp, S., Reisz, J. A., D’ Alessandro, A., Everts, B., and Amiel, E. (2017). Cell-Intrinsic Glycogen Metabolism Supports Early Glycolytic Reprogramming Required for Dendritic Cell Immune Responses. Cell Metab 26, 558-567. e5.

Vanherwegen, A.-S., Eelen, G., Ferreira, G.B., Ghesquiere, B., Cook, D.P., Nikolic, T., Roep, B., Carmeliet, P., Telang, S., Mathieu, C., et al. (2019). Vitamin D controls the capacity of human dendritic cells to induce functional regulatory T cells by regulation of glucose metabolism. J Steroid Biochem Mol Biology 187, 134-145.

Visnjic, D., Lalic, H., Dembitz, V., Tomic, B., and Smoljo, T. (2021). AICAr, a Widely Used AMPK Activator with Important AMPK-Independent Effects: A Systematic Review. Cells 10, 1095. Wculek, S.K., Khouili, S.C., Priego, E., Heras-Murillo, I., and Sancho, D. (2019). Metabolic Control of Dendritic Cell Functions: Digesting Information. Front Immunol 10, 775.

Woltman, A.M., Kooij, S.W. van der, Coffer, P.J., Offringa, R., Daha, M.R., and Kooten, C. van (2003). Rapamycin specifically interferes with GM-CSF signaling in human dendritic cells, leading to apoptosis via increased p27KIPl expression. Blood 101, 1439-1445.

Yao, E., Zhang, G., Huang, J., Yang, X., Peng, L., Huang, X., Luo, X., Ren, J., Huang, R., Yang, L., et al. (2019). Immunomodulatory effect of oleoylethanolamide in dendritic cells via TRPV1/AMPK activation. J Cell Physiol 234, 18392-18407.

Yoo, S., and Ha, S.-J. (2016). Generation of Tolerogenic Dendritic Cells and Their Therapeutic Applications. Immune Netw 16, 52-60.

Zaccagnino, P., Saltarella, M., Maiorano, S., Gaballo, A., Santoro, G., Nico, B., Lorusso, M., and Prete, A.D. (2012). An active mitochondrial biogenesis occurs during dendritic cell differentiation. Int J Biochem Cell Biology 44, 1962-1969.

Zahorchak, A.F., Macedo, C., Hamm, D.E., Butterfield, L.H., Metes, D.M., and Thomson, A.W. (2018). High PD-L1/CD86 MFI ratio and IL-10 secretion characterize human regulatory dendritic cells generated for clinical testing in organ transplantation. Cell Immunol 323, 9-18.

Zhao, F., Xiao, C., Evans, K.S., Theivanthiran, T., DeVito, N., Holtzhausen, A., Liu, J., Liu, X., Boczkowski, D., Nair, S., et al. (2018). Paracrine Wnt5a-P-Catenin Signaling Triggers a Metabolic Program that Drives Dendritic Cell Totlization. Immunity 48, 147-160. e7.

Zhou, N., Chen, C., Wang, X., and Li, Y. (2018). mTOR-mediated regulation of dendritic cell differentiation and function. Int J Immunol 41, 461-466

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method of characterizing a dendritic cell in a sub-population of dendritic cells in a biological sample, the method comprising determining two or more of the following:

(a) a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample;

(b) an immune profile of the dendritic cell in the sub-population of dendritic cells in (a) and a reference biological sample;

(c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample; and

(d) characterizing the differentiation state of the dendritic cell.

2. The method of claim 1, wherein the metabolic profile is determined by measuring mitochondrial dependence, glycolytic capacity, and FAAO.

3. The method of claim 2 further comprising measuring expression levels of ENO 1 , GAPDH, LDHA, GLUT1, PFKFB4, MCT1, ATP5A, CS, IDH2, PPARy, CytC, SDHA, CD98, and CD36.

4. The method of any preceding claim, wherein the immune profile is determined by measuring expression levels of HLA-DR, CD86, CD206, PD-L1, CD14, CD141, ILT3, and CDlc.

5. The method of claim any preceding claim, wherein the sub-population of dendritic cells is monocyte-derived.

6. The method of claim any preceding claim, wherein the reference sample comprises CD14+ monocytes.

7. The method of claim any preceding claim further comprising: (d) calculating a metabolic score for the dendritic cell in the subpopulation of dendritic cells and a reference biological sample.

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8. The method of claim 7, wherein the metabolic score comprises a glycolytic score, an oxidative phosphorylation (OXPHOS) score, a fatty acid oxidation (FAO) score, an amino acid (AA) score, a pentose phosphate pathway (PPP) score, and/or a glutathione biosynthesis (GSH) score.

9. The method of any one of claims 7 or 8, wherein the score is calculated using a method comprising linear regression analysis between scMEP median metabolic marker expression and log-transformed median normalized SCENITH parameters.

10. The method of any one of claims 1-3, wherein the differentiation state of the dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased protein synthesis, (ii) increased mitochondrial dependence, (iii) moderate FAAO, (iv) decreased expression levels of ENO1, GAPDH, LDHA, and (v) increased expression levels of GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2.

11. The method of any one of claims 1 or 4, wherein the differentiation state of the dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the immune profile of the dendritic cell is determined to have one or more of (i) increased expression levels of HLA-DR, CD86, CD206, and PD-L1, and (ii) decreased expression levels of CD14.

12. The method of claim 1, wherein the differentiation state of the dendritic cell is characterized as inflammatory when, compared to the reference biological sample, the dendritic cell has a decreased ratio of phosphorylated mTOR to phosphorylated AMPK.

13. The method of any one of claims 1-3, wherein the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased glycolysis, (ii) increased oxidative phosphorylation, (iii) increased expression levels of MCT1, PFKFB4, LDHA, Cytc, SDHA, CD98, and (iv) decreased expression levels of CD36.

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14. The method of any one of claims 1 or 4, wherein the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the immune profile of the dendritic cell is determined to have (i) increased expression levels of CD14, PD-L1, ILT3, and CD141, and (ii) decreased expression levels of HLA-DR, CD86, and CDlc.

15. The method of claim 1, wherein the differentiation state of the dendritic cell is characterized as tolerogenic when, compared to the reference biological sample, the dendritic cell has an increased ratio of phosphorylated mTOR to phosphorylated AMPK.

16. The method of any one of claims 1-15, wherein the biological sample and the reference sample is a blood sample.

17. The method of claim 16, wherein the blood sample is derived from a human.

18. A method of identifying a dendritic cell as an inflammatory dendritic cell, the method comprising:

(a) determining a metabolic profile of a dendritic cell and a reference sample, wherein said determining comprises (i) measuring one or more levels of protein synthesis, mitochondrial dependence, glycolytic capacity, FAAO, and (ii) measuring one or more expression levels of ENO1, GAPDH, LDHA, GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2;

(b) determining an immune profile of the dendritic cell and a reference sample, wherein said determining comprises measuring one or more expression levels of HLA-DR, CD86, CD206, PD-L1, and CD14;

(c) determining a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell and a reference sample;

(d) characterizing the dendritic cell as inflammatory when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have one or more of (i) increased protein synthesis, (ii) increased mitochondrial dependence, (iii) moderate FAAO, decreased expression levels of ENO1, GAPDH, LDHA, and (iv) increased expression levels of GLUT1, PFKFB4, MCT1, ATP5A, CS, and IDH2; the immune profile of the dendritic cell is

95 determine to have one or more of (i) increased expression levels of HLA-DR, CD86, CD206, and PD-L1, and (ii) decreased expression levels of CD14; and the dendritic cell has a decreased ratio of phosphorylated mTOR to phosphorylated AMPK.

19. A method of identifying a dendritic cell as a tolerogenic dendritic cell, the method comprising:

(a) determining a metabolic profile of a dendritic cell and a reference sample, wherein said determining comprises (i) measuring one or more levels of glycolysis, oxidative phosphorylation, and (ii) measuring one or more expression levels of LDHA, PFKFB4, MCT1, CD36, Cytc, SDHA, CD98, and PPARy;

(b) determining an immune profile of the dendritic cell and a reference sample, wherein said determining comprises measuring one or more expression levels of HLA-DR, CD86, CDlc, PD-L1, ILT3, CD14, and CD141;

(c) determining the ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell and a reference sample;

(d) characterizing the dendritic cell as tolerogenic when, compared to the reference biological sample, the metabolic profile of the dendritic cell is determined to have (i) increased glycolysis, (ii) increased oxidative phosphorylation, (iii) increased expression levels of MCT1, PFKFB4, LDHA, Cytc, SDHA, CD98, and (iv) decreased expression levels of CD36; the immune profile of the dendritic cell is determined to have (i) increased expression levels of CD14, PD-L1, ILT3, and CD141, and (ii) decreased expression levels of HLA-DR, CD86, and CDlc; and the dendritic cell has an increased ratio of phosphorylated mTOR to phosphorylated AMPK.

20. The method of any one of claims 18-19, wherein the biological sample and the reference sample is a blood sample.

21. The method of claim 20, wherein the blood sample is derived from a human.

22. The method of any one of claims 18-19, wherein the dendritic cell is monocyte-derived.

23. The method of any one of claims 18-19, wherein the reference sample comprises CD14+

96 monocytes.

24. The method of claim 19 further comprising calculating a metabolic score for the dendritic cell and a reference biological sample.

25. The method of claim 24, wherein the metabolic score comprises a glycolytic score, an oxidative phosphorylation (OXPHOS) score, a fatty acid oxidation (FAO) score, an amino acid (AA) score, a pentose phosphate pathway (PPP) score, and/or a glutathione biosynthesis (GSH) score.

26. The method of claim 25, wherein the score is calculated using a method comprising linear regression analysis between scMEP median metabolic marker expression and log-transformed median normalized SCENITH parameters.

27. The method of any of claims 24-26, wherein the dendritic cell is characterized as tolerogenic when the glycolytic score is 2 to 3 fold higher than that of the reference sample.

28. A method of preparing a dendritic cell vaccine, the method comprising, culturing dendritic cells in a culture medium comprising one or more of the following:

(i) a reduced glucose concentration;

(ii) an inhibitor of lactate production;

(iii) increased fatty acids;

(iv) increased amino acids; and

(v) an inhibitor of mTOR, an inhibitor of AMPK, or a combination thereof.

29. The method of claim 28, wherein (i) comprises culturing the dendritic cells in the presence of a glucose transport inhibitor.

30. The method of claim 28, wherein the inhibitor of lactate production is an MCT1 inhibitor.

31. The method of claim 30, wherein the MCT1 inhibitor is BAY8002.

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32. The method of claim 28, wherein the inhibitor of mTOR is rapamycin.

33. The method of claim 28, wherein the inhibitor of AMPK is dorsomorphin.

34. The method of claim 28, wherein the fatty acids comprise one or more of palmitic acid, oleic acid, and linoleic acid.

35. The method of any one of claims 28-34, wherein the dendritic cells are immature dendritic cells.

36. The method of any one of claims 28-34, wherein the dendritic cells are mature dendritic cells.

37. The method of any one of claims 28-36 further comprising characterizing the dendritic cells prior to culturing, the method comprising determining two or more of the following:

(a) a metabolic profile of a dendritic cell in a sub-population of dendritic cells from a biological sample and a reference biological sample;

(b) an immune profile of the dendritic cell in the sub-population of dendritic cells in (a) and a reference biological sample;

(c) a ratio of phosphorylated mTOR to phosphorylated AMPK in the dendritic cell in the subpopulation of dendritic cells and a reference sample; and

(d) characterizing the differentiation state of the dendritic cell.

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