CN112185465A - Method for analyzing periodontitis immune microenvironment through spatial transcriptome technology - Google Patents
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Abstract
The invention discloses a method for analyzing periodontitis immune microenvironment by a space transcriptome technology, which comprises the steps of obtaining periodontium sections of different periodontitis patients and normal controls by a frozen section technology, sequencing each tissue sample by the space transcriptome technology, carrying out subsequent analysis by using space transcriptome data obtained by sequencing, grouping, identifying and effectively positioning cell types in the immune microenvironment of different regions of the periodontal tissues, and further clustering and differential analysis on the types and expression conditions of original genes of different regions to select key genes and related molecular signal paths in a periodontitis immune regulation and control mechanism. The invention is beneficial to screening out key genes and related molecular signal channels in the periodontitis immune regulation mechanism, and provides powerful and effective data support for finally understanding the occurrence and development processes of periodontitis.
Description
Technical Field
The invention belongs to the technical field of oral medicine and biological information, and particularly relates to a method for analyzing a periodontitis immune microenvironment by a space transcriptome technology.
Background
Periodontitis is a type of chronic inflammatory disease mainly characterized by periodontal tissue destruction, alveolar bone resorption and tooth loosening, and is one of the most common oral inflammatory diseases. Epidemiological investigation shows that the worldwide prevalence of periodontitis is as high as 11.2%, and reaches the peak of the disease around the age of 40.
When the periodontal tissues of the body are stimulated to inflammation by pathogens or pathogenic bacteria, i.e., periodontitis, the released inflammatory factors recruit immune cells to the site of inflammation. However, while attacking pathogens or pathogenic bacteria, immune cells can also attack normal tissue cells of the body, and the oral cavity is in a bacteria-carrying state for a long time as a bacteria-carrying environment, so that the inflammatory immune attack can also continuously exist for a long time, generate a large amount of inflammatory factors, seriously damage the normal tissues of the body and interfere with the tissue regeneration and repair process.
Studies have shown that there is a close link between bone tissue loss due to periodontitis and inflammation-induced immune responses. The process is not only related to the innate immune response mediated by mononuclear macrophages, fibroblasts and the like, but also closely related to the adaptive immune response mediated by T cells and B cells. The periodontitis immune microenvironment mainly refers to immune cells and immune related molecules in the periodontitis microenvironment, and classification of the immune environment in the periodontitis microenvironment is helpful for understanding how immune composition and immune state affect inflammatory cells and periodontitis treatment, but specific immune regulation and control mechanisms of periodontitis are not clear in the existing research. Therefore, the research on the periodontitis immune regulation mechanism has important practical significance and application prospect, and a new idea and a new method are urgently needed to carry out deep exploration research on the periodontitis immune microenvironment.
For RNA sequencing, the current major sequencing methods include Bulk transcriptome sequencing (Bulk RNA-seq), single-cell sequencing (single-cell RNA-seq), and spatial transcriptome sequencing. Population transcriptome sequencing is the detection of the expression of each gene in large cell populations, and is mainly used for comparative genomics, and can be used for comparing gene expression differences of the same tissues of different species; the single cell sequencing is used for determining the expression condition of each gene in a single cell, has microcosmic property, and can detect heterogeneous information which cannot be obtained by sequencing a mixed sample compared with the former method; and the spatial transcriptome sequencing additionally reserves the spatial position information of the cells on the basis of single cell sequencing, can project gene expression to the existing spatial information and know the relative position relationship between specific cells and tissue slices.
Disclosure of Invention
The invention aims to provide a method for analyzing a periodontitis immune microenvironment by a space transcriptome technology, and aims to solve the problem that the existing research method cannot clearly analyze and understand a periodontitis immune regulation and control mechanism.
The invention is realized by a method for analyzing periodontitis immune microenvironment by a spatial transcriptome technology, which comprises the following steps:
(1) obtaining periodontal tissue slices of different periodontitis patients and normal control by a frozen section technology,
(2) sequencing each tissue sample by a spatial transcriptome technology to obtain spatial transcriptome data;
(3) and performing standard data analysis according to the spatial transcriptome data, grouping, identifying and effectively positioning cell types in immune microenvironment of different regions of the periodontal tissue, and further clustering and differential analysis on types and expression conditions of original genes of different regions to select key genes and related molecular signal pathways in a periodontitis immune regulation and control mechanism.
Preferably, in step (2), the mRNA captured by the oligonucleotide chain in the capture region of the slide glass and labeled with the unique molecular identifier UMI and the special spatial barcode in each tissue section is sequenced by the spatial transcriptome technique to obtain spatial transcriptome data.
Preferably, in step (3), the standard data analysis according to the spatial transcriptome data comprises the following steps:
filtering spatial transcriptome data by adopting Fastp software to obtain sequencing data which can be directly used for subsequent analysis;
counting cell barcode information and corresponding counts contained in the filtered statistical sequencing data by adopting a barcode processing algorithm, so as to judge the actually detected lattice number in the sequencing sample and obtain real space transcriptome sequencing information;
comparing reads corresponding to cell barcodes in sequencing data to a genome corresponding to a known species, analyzing similarity and difference between a detected unknown sequence and a known sequence, and obtaining a bam file compared with the unknown sequence;
transforming the bam file containing the various information after genome comparison, merging the monomolecular labels compared to the same gene in the file, removing repeated UMI sequences in the monomolecular labels to obtain the number of UMI corresponding to each gene, counting to obtain the number of genes detected by each space lattice, and performing visual display in a space staining sheet.
Preferably, in step (3), the grouping, identifying and effectively locating all detected cell types in the immune microenvironment of different regions of the periodontal tissue comprises the following steps:
based on PCA algorithm, performing dimensionality reduction on spatial sequencing data to obtain two-dimensional/low-dimensional information, clustering spots with similarity in thousands of spatial lattices by using t-SNE algorithm and UMAP algorithm to further obtain a cluster subset (cluster), and performing visual display in a spatial staining sheet;
and finally identifying the Marker gene of each cluster by combining verified Marker genes in a cellMarker database through Welcht-tests on the log expression quantity of each gene between every two clusters to obtain the differential gene of top10 as a Marker gene candidate.
Preferably, in step (3), the further clustering and difference analysis of the types and expression conditions of the original genes in different regions comprises the following steps:
performing enrichment analysis on the Marker gene of the cluster and the gene in the cellMarker database by using a clusterProfiler software package, and observing the cell type in which a specific cluster is enriched so as to complete the identification of the cell type;
taking the genes with obvious differential expression in each cluster obtained by enrichment analysis as candidate gene groups, and analyzing by combining with each large database;
analyzing cell components, molecular functions and biological processes of the candidate genes through a Gene Ontology database;
performing Pathway Analysis on the candidate gene by using a KEGG database, and observing the signal paths on which the candidate gene is mainly enriched;
and combining the candidate gene with a DisGeNET database, and observing the close correlation between the candidate gene and certain diseases through enrichment analysis.
The invention overcomes the defects of the prior art and provides a method for analyzing periodontitis immune microenvironment by a space transcriptome technology, wherein periodontal tissue slices of different periodontitis patients and normal control are obtained by a frozen section technology, each tissue sample is sequenced by the space transcriptome technology, the space transcriptome data obtained by sequencing is utilized for subsequent analysis, then grouping, identifying and effectively positioning the cell types in the immune microenvironment of different regions of the periodontal tissue are detected, and the types and the expression conditions of original genes of different regions are further clustered and differentially analyzed, so that key genes and related molecular signal channels in the periodontitis immune regulation mechanism can be selected.
Compared with the defects and shortcomings of the prior art, the invention has the following beneficial effects: the method can obtain the transcription information of specific spatial positions in the complete tissue slices, is beneficial to screening out key genes and related molecular signal channels in the periodontitis immune regulation mechanism, and provides powerful and effective data support for finally understanding the occurrence and development processes of periodontitis.
Drawings
FIG. 1 is a flowchart illustrating the operation of the method for analyzing the immune microenvironment for periodontitis by the spatial transcriptome technique according to the present invention;
FIG. 2 is a schematic view showing the position of a periodontal tissue section of a patient suffering from periodontitis in an example of the present invention;
FIG. 3 is an exemplary presentation of counts number, dot matrix number, spatial transcriptome sequencing information in an embodiment of the invention;
FIG. 4 is an exemplary presentation after data normalization processing in an embodiment of the present invention;
FIG. 5 is a visual display of the clustered subpopulations in a spatially stained disc according to an embodiment of the present invention; FIG. 5A is a diagram showing that through UMAP analysis, all spots are clustered according to similarity to obtain 14 cluster clusters (cluster), and all the spots with the same color form one cluster; FIG. 5B is a diagram showing the restoration of the specific spatial positions corresponding to all the spots of the 14 clustered sub-groups onto the slice according to the spatial position information previously retained by each spot;
FIG. 6 shows the expression levels of CXCL6 gene at different sites in a periodontal tissue section in an example of the present invention; FIG. 6A is a sectional view of a periodontitis tissue; FIG. 6B shows the expression levels of CXCL6 gene at different sites in a periodontal tissue section, and the darker the color of the dot matrix in the figure, the higher the expression level of CXCL6 gene at that site; among them, CXCL6 was expressed in the highest amount in the lower right corner region of fig. 6B, and there was a greater accumulation of deeply stained inflammatory cells in the same region corresponding to the left figure, indicating that CXCL6 gene may be a periodontitis-associated gene.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention discloses a method for analyzing periodontitis immune microenvironment by a spatial transcriptome technology, which comprises the following steps of:
(1) obtaining periodontal tissue slices of different periodontitis patients and normal control by frozen section technology
In step (1), periodontal tissues of healthy controls, gingivitis and periodontitis patients were collected clinically. All subjects had good general health and were not taking antibacterial or anti-inflammatory drugs within 3 months prior to drawing the material. Inclusion of age-matched healthy controls, gingivitis and peridentitis patients prior to treatment all ensured that at least 16 teeth were present in the oral cavity and that periodontal treatment was not performed within 6 months prior to drawing the material.
1) The inclusion criteria for healthy controls were: gingival health without inflammation (no bleeding on probing, and probing depth <4mm), no loss of clinical attachment or bone loss, healthy periodontal tissue samples collected during coronal prolongation due to denture or orthodontic needs;
2) gingivitis inclusion criteria: gingival inflammation (exploratory bleeding, but no loss of clinical attachment or bone loss) exists in teeth that need to be extracted due to malposition, crowding, or pericoronitis, periodontal tissue samples are collected at the time of tooth extraction (gingivitis is an early stage in the development of periodontitis disease and is therefore also included in the sequencing samples);
3) inclusion criteria prior to periodontitis treatment: affected teeth requiring extraction for periodontitis (probing bleeding, with probing depth >5mm, bone loss > 60% of root length), collecting periodontal tissue samples during tooth extraction;
the collected samples were immediately placed in sterile tubes containing α MEM medium and transferred to the laboratory for study within 2 hours;
(2) and (3) freezing and storing isopentane: freezing the collected sample by using precooled isopentane (refrigerant), so that the space form of the periodontal tissue is maintained and the quality of RNA is protected;
(3) OCT embedding: the frozen periodontal tissue is placed in a mould, and a precooled OCT frozen section embedding medium is used for completely embedding the tissue block, so that bubbles are avoided in the embedding process. The embedding medium is beneficial to keeping the complete structure of the tissue and providing structural support for the tissue in the process of freezing and slicing so as to increase the continuity of the tissue and reduce wrinkles and fragmentation, thereby preparing for preparing subsequent slicing;
(4) preparing a slice: a shallow incision (about 1mm deep) is cut on the surface of the embedded tissue block by a blade to avoid damaging tissues by too deep incision, and then the tissue block is cut along the incision to keep the thickness of the section between 10 and 20 mu m, as shown in figure 2.
(2) Sequencing each tissue sample by a spatial transcriptome technology to obtain spatial transcriptome data
The step (2) comprises the following specific steps:
(i) data filtering
In order to make the data more effective, the embodiment of the invention needs to filter the data.
Low-quality reads (sequencing fragments), undetected reads, reads containing linkers, and the like in raw data (raw data) are filtered by adopting Fastp software, so that clean reads (clean data) which can be directly used for subsequent analysis are obtained, wherein the clean reads comprise cell barcode information and gene counts (counts).
(ii) Dot matrix judgment
The mRNA released from the tissue cells migrates to the spot (lattice) marked with a specific tag sequence (cell barcode) on the sequencing chip. The cell barcode information contained in the filtered statistical sequencing data (clean data) and the corresponding gene counting counts are counted by adopting a barcode processing algorithm, wherein the counts represent the occurrence frequency of the same reads/sequencing fragments detected in the sequencing process, namely the abundance corresponding to the reads, and reflect the transcription frequency of the gene, namely the expression quantity of the gene. And judging the actually detected lattice number (spots) in the sequencing sample according to the counts number to obtain the real space transcriptome sequencing information. An example of the results is shown in FIG. 3.
(iii) Data comparison
Comparing reads (limited by the current sequencing level, when the genome is sequenced, the genome needs to be firstly broken into DNA fragments, then library is built for sequencing, the reads are the read length/the length of the base sequence of the corresponding sequencing fragment after each sequencing) corresponding to the cell barcode in the sequencing data to the genome corresponding to the known species (human). Through comparison, the similarity and difference between the detected unknown sequence and the known sequence can be found, and a bam file compared with the unknown sequence can be obtained (the bam file is a binary plate of a sam file, stores various information after map/genome comparison after sequencing and greatly compresses the size of the file);
(iv) sequencing data normalization
For sequencing data, differences in sequencing depth, gene length, occur due to differences in the technology level. Therefore, in order to compare the expression levels of different genes in different samples, it is necessary to standardize the raw sequencing data.
In the embodiment of the present invention, the data normalization processing method is to transform the bam file containing the various information after genome alignment, merge the Unique Molecular Identifiers (UMIs) aligned to the same gene in the file, remove the repeated UMI sequences in the file, obtain the number of UMIs corresponding to each gene (each mRNA is labeled with a Unique Molecular Identifier UMI, which can be used to count mRNA molecules), and further obtain the basis factors (which reflect how many genes are detected in a spot (the number of genes that can be expressed in a cell is larger, the differentiation degree of the cell is lower)) and the number of mRNA molecules detected in each spatial lattice (spot) by statistics.
Staining of the spatial section, the deeper the staining at a point, the greater the number of genes expressed at that point, which may reflect a lower degree of differentiation of the cells at that point (relative to the number of cell types contained at that point), as shown, for example, in FIG. 4.
(3) Performing standard data analysis according to the space transcriptome data, grouping, identifying and effectively positioning cell types in immune microenvironment of different regions of periodontal tissue, and further clustering and differential analysis on types and expression conditions of original genes of different regions to select key genes and related molecular signal pathways in periodontitis immune regulation mechanism
In the embodiment of the invention, the step (3) comprises the following specific steps:
(v) lattice clustering
And performing dimensionality reduction on the spatial sequencing data based on a PCA algorithm to obtain two-dimensional/low-dimensional information. And clustering the spots with similarity in thousands of space lattices (spots) by utilizing a t-SNE algorithm and a UMAP algorithm to further obtain dozens or twenty clustering subgroups (cluster), visually displaying in a space staining sheet, and obtaining a result as shown in figure 5.
In the embodiment of the invention, the clustering aims at clustering thousands of spots according to the gene expression condition based on the t-SNE algorithm and the UMAP algorithm to obtain dozens or twenty clusters, and performing cell type identification and gene function analysis on the spots in the clusters according to the clustering condition by combining a public database at the later stage.
(vi) Marker analysis
Different cluster are obtained after the dot matrix clustering, and then a Marker gene can be searched for each cluster. Welch t-tests are carried out on the log expression quantity of each gene between every two clustering clusters (cluster), so that a differential gene of top10 is obtained to be used as a Marker gene candidate, and the Marker gene of each cluster is finally identified by combining the verified Marker genes in a cellMarker database. The expression distribution of the Marker gene is visualized on the section in a way of a gene expression space diagram, as shown in fig. 6, fig. 6 reflects the expression quantity of the CXCL6 gene at different parts of the periodontal tissue section, and the darker the color of the dot matrix in the diagram, the higher the expression quantity of the Marker gene at the position is.
The Marker gene can be used for subsequent judgment or identification of cell types through identification of the Marker gene.
(vii) Cell type identification
By means of a clusterProfiler (a software package of R language), the Marker gene of the cluster (cluster) and the gene in the cellMarker database are subjected to enrichment analysis, and the cell type enriched in a specific cluster is observed, so that the identification of the cell type is completed.
(viii) Difference analysis
And (3) taking the genes with obvious differential expression in each cluster obtained by enrichment analysis as a candidate gene group, and analyzing by combining with each large database. Analyzing cellular components, molecular functions and biological processes of the candidate genes through a GO (Gene ontology) database; performing Pathway Analysis on the candidate gene by using a KEGG database, and observing the signal paths on which the candidate gene is mainly enriched; in addition, the candidate gene is combined with a DisGeNET database, and the candidate gene is closely related to diseases through enrichment analysis. And combining the results to comprehensively and completely describe the related information of the differential expression genes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A method of analyzing the immune microenvironment of periodontitis by spatial transcriptome techniques, the method comprising the steps of:
(1) obtaining periodontal tissue slices of different periodontitis patients and normal control by a frozen section technology,
(2) sequencing each tissue sample by a spatial transcriptome technology to obtain spatial transcriptome data;
(3) and performing standard data analysis according to the spatial transcriptome data, grouping, identifying and effectively positioning cell types in immune microenvironment of different regions of the periodontal tissue, and further clustering and differential analysis on types and expression conditions of original genes of different regions to select key genes and related molecular signal pathways in a periodontitis immune regulation and control mechanism.
2. The method for analyzing the microenvironment of periodontitis through the spatial transcriptome technique of claim 1, wherein in the step (2), the mRNA captured by the oligonucleotide chains in the capture region of the slide glass and labeled with the unique molecular identifier UMI and the specific spatial barcode in each tissue section is sequenced through the spatial transcriptome technique to obtain the spatial transcriptome data.
3. The method of analyzing a periodontitis immune microenvironment by a spatial transcriptome technique of claim 1, wherein in the step (3), the performing of the standard data analysis according to the spatial transcriptome data comprises the steps of:
filtering spatial transcriptome data by adopting Fastp software to obtain sequencing data which can be directly used for subsequent analysis;
counting cell barcode information and corresponding counts contained in the filtered statistical sequencing data by adopting a barcode processing algorithm, so as to judge the actually detected lattice number in the sequencing sample and obtain real space transcriptome sequencing information;
comparing reads corresponding to cellbarcode in sequencing data with a genome corresponding to a known species, analyzing similarity and difference between a detected unknown sequence and a known sequence, and obtaining a bam file compared with the unknown sequence;
transforming the bam file containing the various information after genome comparison, merging the monomolecular labels compared to the same gene in the file, removing repeated UMI sequences in the monomolecular labels to obtain the number of UMI corresponding to each gene, counting to obtain the number of genes detected by each space lattice, and performing visual display in a space staining sheet.
4. The method of claim 3, wherein in step (3), said grouping, identifying and efficiently mapping the cellular constituents of the immune microenvironment in different regions of the periodontal tissue detected comprises the steps of:
based on PCA algorithm, performing dimensionality reduction on spatial sequencing data to obtain two-dimensional/low-dimensional information, clustering spots with similarity in thousands of spatial lattices by using t-SNE algorithm and UMAP algorithm to further obtain a cluster subset (cluster), and performing visual display in a spatial staining sheet;
and finally identifying the Marker gene of each cluster by combining verified Marker genes in a cellMarker database through Welcht-tests on the log expression quantity of each gene between every two clusters to obtain the differential gene of top10 as a Marker gene candidate.
5. The method for analyzing the microenvironment of periodontitis through the spatial transcriptome technology of claim 4, wherein in the step (3), the further clustering and difference analysis of the species and expression of the original genes of different regions comprises the following steps:
performing enrichment analysis on the Marker gene of the cluster and the gene in the cellMarker database by using a clusterProfiler software package, and observing the cell type in which a specific cluster is enriched so as to complete the identification of the cell type;
taking the genes with obvious differential expression in each cluster obtained by enrichment analysis as candidate gene groups, and analyzing by combining with each large database;
analyzing cell components, molecular functions and biological processes of the candidate genes through a Gene Ontology database;
performing Pathway Analysis on the candidate gene by using a KEGG database, and observing the signal paths on which the candidate gene is mainly enriched;
and combining the candidate gene with a DisGeNET database, and observing the close correlation between the candidate gene and certain diseases through enrichment analysis.
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| WO2022220385A1 (en) * | 2021-04-15 | 2022-10-20 | 주식회사 포트래이 | Apparatus and method for predicting cell type enrichment from tissue images using spatially resolved gene expression data |
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