CN114540482A - Analysis device and method for improving atrial fibrillation myocardial fibrosis by Qibo pulse-activating composition based on space transcriptome technology - Google Patents
Analysis device and method for improving atrial fibrillation myocardial fibrosis by Qibo pulse-activating composition based on space transcriptome technology Download PDFInfo
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Abstract
The invention relates to an analysis device and method for improving atrial fibrillation myocardial fibrosis by using a Qibo pulse-activating composition based on a space transcriptome technology, and the analysis device comprises a space transcription module, an expression change difference gene screening module, a dimensionality reduction processing module, a cluster analysis module, a cluster difference gene set screening module, a main difference cluster screening module and an analysis module; the method comprises the steps of performing spatial transcription sequencing on animal myocardial tissue slices of a normal group, a model group and a Qibo pulse-activating group by adopting a spatial transcription group technology, performing subsequent analysis by utilizing spatial transcription sequencing data, screening highly variant genes, reducing dimension and clustering, performing enrichment analysis on differential genes of each cluster, comparing enrichment analysis results to obtain main differential clusters, and finally performing enrichment analysis on characteristic differential genes of the main differential clusters. The device provided by the invention is used for analyzing key characteristic genes and related molecular signal channels of Qibo pulse-activating medicine intervention and atrial fibrillation treatment, and provides effective molecular theoretical basis and theoretical support for better understanding of medicine intervention and atrial fibrillation treatment.
Description
Technical Field
The invention belongs to the technical field of internal medicine and biological information of traditional Chinese medicine, and relates to an analysis method and device for analyzing drug improvement and intervening atrial fibrillation by using a space transcriptome technology.
Background
Complex heterogeneous tissues are composed of multiple cell types and spaces. The study of these complex heterogeneous tissues requires tools that can assess gene expression profiles as well as spatial organization of various cell types and states. Formal analysis of tissues at the cellular and molecular level provides biological information while preserving the organization of the tissue and cellular microenvironment.
There is no tissue dissociation bias in the transcriptome analysis, as the tissue is intact and cell types in heterogeneous tissues can be determined. The Visium spatial gene expression solution from 10X company provides a method for analyzing spatial ensemble transcriptomes. By overlaying total mRNA gene expression data with morphologically H & E stained images, researchers can obtain whole transcriptome high throughput gene expression analysis results from whole tissue sections and a wide variety of sample types using Visium. This solution enables researchers to reveal the biological structure of normal and diseased tissues and to discover new tissue biomarkers; moreover, by using existing laboratory equipment and tissue analysis tools, visium can be easily integrated into the workflow, completing the end-to-end workflow from slicing the sample to building the sequencing library within one working day.
Atrial fibrillation is the most common persistent arrhythmia in adults. The incidence and prevalence of disease have increased year by year in recent years. Epidemiological data indicate a total of about 3350 million patients with atrial fibrillation worldwide in 2010. The prevalence rate of atrial fibrillation of adults in China is 0.2 percent and increases with the increase of age, and the prevalence rate of atrial fibrillation above 80 years old reaches 8.3 percent. Atrial fibrillation increases the risk of stroke, heart failure and death, reduces the life burden of patients, increases the hospitalization rate of arrhythmia patients, and brings huge economic burden to society.
Structural remodeling characterized by interstitial fibrosis, which blocks or slows down local excitation in the atria and facilitates the formation of reentry loops, is the matrix for atrial fibrillation maintenance and recurrence. Previous researches show that the Qibo pulse-activating granules can effectively improve atrial fibrillation and atrial fibrosis, further deeply research the molecular mechanism of improving atrial fibrillation and atrial myofibrosis, and have important significance for clarifying the pharmacological action of the granules. The composition comprises radix astragali, radix Glehniae, radix Ophiopogonis, Succinum, fructus Schisandrae chinensis, semen Ziziphi Spinosae, Saviae Miltiorrhizae radix, radix scrophulariae, periostracum Cicadae, and Bombyx Batryticatus. In the prescription, astragalus, radix glehniae and radix ophiopogonis are taken as monarch drugs for supplementing qi and nourishing yin; the schisandra chinensis and the spina date seed are used as ministerial drugs for sour and sweet yin and calming heart and nerves; the salvia miltiorrhiza cools blood, clears heart and relieves restlessness, activates blood and promotes blood circulation, and the amber calms heart and recovers pulse, calms convulsion and calms the nerves are used as assistant and guide. The whole formula has the effects of tonifying qi and yin, calming heart and tranquilizing mind and stopping palpitation, and is clinically used for treating the qi-yin deficiency type atrial fibrillation.
In recent years, the field of transcriptomics has developed into a more general way of studying regulatory molecular pathways of cells even with mixed components. These methods, although informative, focus primarily on cells in homogenized tissues or single cell suspensions, thereby losing spatial specificity, and in situ hybridization techniques, while capable of revealing the spatial location of molecules, present throughput limitations.
Spatial transcriptomics are performed by exactly matching gene expression with immunohistochemical images of tissue sections, so that gene expression information of different cells in a tissue is positioned on the original spatial position of the tissue, and genes are distinguished to be active in the tissue, thereby visually detecting differences of gene expression of different parts in the tissue.
Spatial transcriptome technology combines histological imaging and single cell sequencing technologies to preserve positional information of each transcript by spatially fixing and barcoding cDNA synthesis primers. With spatially resolved mRNA data, we can focus on specific tissue regions. By comparing the transcriptome change of the lesion region of the model group, the pathological mechanism related to the atrial fibrillation fibrosis can be clearly explained; by comparing the transcriptome changes of the atrial myofibrosis tissue region before and after administration, the action target of the drug on the myocardial tissue can be clearly explained.
The Visium spatial gene expression solution is a scheme from preparation of spatial transcriptome samples to data analysis given by 10X company, and researchers draw spatial gene expression maps of complex tissue samples using a Visium spatial gene expression slide. The Visium Spatial Gene Expression Slide (also called chip, Visium Slide) is a Slide carrying poly (a) capture and novel Spatial barcode technology, and each Visium Spatial Gene Expression Slide comprises four capture regions (6.5 × 6.5mm), each capture region contains 1 single tissue slice, each capture region has 5000 barcode-labeled spots (spots), each spot contains millions of oligonucleotide chains containing poly (dT), the oligonucleotide chains can capture and identify mRNA, and each spot has a unique barcode sequence. When the RNA is released from the cells of the tissue section, the RNA that migrates to each spot is labeled with the corresponding barcode sequence, and then library construction and sequencing is performed. Next, the data is assigned according to its barcode information to determine which data comes from which location. Visualization of spatial gene expression is ultimately achieved, that is, the spatial gene expression slide can capture the recognition mrnas and the spatial locations of these mrnas can be determined.
The oligonucleotide chain has a unique molecular identifier or UMI (which counts the mRNA molecules) and a spatial barcode that allows one to view the source of the mRNA in the capture region. Each spot was 55 microns in diameter, so 1-10 cells could be detected per spot, depending on the tissue type.
Disclosure of Invention
The invention aims to solve the technical problems that the research method of complex heterogeneous tissues on the cellular and molecular level is complex in the process of medicine intervention and treatment of atrial fibrillation, and the information of the spatial positions of cells, genes and variant genes of a lesion part and a lesion improvement part after medicine application is undefined, and provides a device and a method for analyzing a Qibo pulse-generating composition to intervene in treatment of atrial fibrillation and improve atrial fibrillation based on a spatial transcriptome technology, so that the technical problems that the biological process and the signal path related to the improvement of the atrial fibrillation and the prognosis of medicines cannot be clearly analyzed in the existing research method are solved.
To achieve the object of the present invention, in one aspect, the present invention provides an analysis device for improving atrial fibrillation myocardial fibrosis by using a qi-perot pulse-activating composition based on a spatial transcriptome technology, comprising:
the expression change difference gene screening module is used for standardizing the spatial transcription data of animal myocardial tissue slice samples of a normal group, an atrial fibrillation model group and a Qibo pulse-engendering administration group, and screening highly-variant genes of the standardized data through a vst algorithm to obtain an expression change difference gene set with expression change difference from high to low;
the dimensionality reduction processing module is used for carrying out linear dimensionality reduction processing on the data obtained by carrying out standardization processing on the expression change difference gene set obtained by screening in normal group, model group and Qibo pulse-activating group samples by adopting a PCA method to obtain PCA dimensionality reduction data; then, carrying out nonlinear dimensionality reduction on the PCA dimensionality reduction data by adopting a nonlinear dimensionality reduction algorithm of the T-SNE to obtain T-SNE dimensionality reduction data;
the cluster analysis module is used for carrying out cluster analysis processing on the dimension reduction processing data by adopting an SNN (single nucleotide network) clustering algorithm to obtain expression change difference gene cluster subgroups of samples of a normal group, a model group and a Qibo pulse-activating group;
a cluster difference gene set screening module, which is used for respectively comparing the genes of each cluster subgroup of the normal group, the model group and the Qibo pulse-activating group sample with the genes of all other cluster subgroups of the tissue sample of the corresponding group by adopting the wilcox rank and test of Seurat software, and screening to obtain a difference gene set of each cluster (namely, a gene set with large expression difference change of each cluster);
the main difference group screening module is used for respectively carrying out difference gene enrichment analysis on the cluster difference gene sets of each cluster of the normal group, the model group and the Qibo pulse-engendering group samples by adopting an R language Cluster Profile packet, comparing the enrichment analysis results of the normal group with the enrichment analysis results of the model group and the Qibo pulse-engendering group, and screening to obtain a main difference group;
a main difference group analysis module for carrying out difference gene function enrichment analysis on the characteristic difference gene sets of the main difference group of the Qibo pulse-generating group and the main difference group of the model group to obtain the Qibo pulse-generating composition and improve the related biological processes and signal paths of the difference genes of the atrial fibrillation
Wherein, the expression change difference gene set screened by the expression change difference gene screening module is the first 3000 genes of which the expression change difference genes are from high to low.
Specifically, the spatial transcriptome sequencing data of each sample is respectively standardized by using a sctformat (sct) algorithm of the seruat software, so as to obtain standard processing data of the spatial transcription data of each sample.
Particularly, the system also comprises a spatial transcription data expression matrix conversion module which is used for comparing, quantifying genes and identifying sites of the spatial transcriptome sequencing data by using 10 XGenomics official software SpaceRange to obtain a spatial transcription data expression matrix.
In particular, the spatial transcription data expression matrix is a matrix of M × N, where rows are genes and columns are Spots.
And then, adopting an expression change difference gene screening module to screen highly variant genes for the spatial transcription data expression matrix so as to obtain an expression change difference gene set.
Particularly, the system also comprises a spatial transcription module which is used for respectively carrying out spatial transcription sequencing processing on myocardial tissue slice samples of the animals of the normal group, the atrial fibrillation model group and the Qibo pulse-engendering administration group by adopting a spatial transcription group technology to obtain spatial transcription data of the animal samples of the normal group, the model group and the Qibo pulse-engendering group.
In particular, the spatial transcription module is used for carrying out a spatial transcription sequencing test according to a spatial transcriptome sequencing method of 10 Xgenomics visium (spatial transcription sequencing of genes by 10 Xgenomics (TXG) company), and obtaining spatial transcription data.
The dimensionality reduction processing module is used for standardizing the expression change difference gene set data by adopting a sctformat (sct) algorithm in an R language source packet to obtain highly variant gene standardized data, and then performing linear dimensionality reduction processing by adopting a PCA method.
Wherein, the expression change difference genes of the cluster analysis module to the normal group samples are clustered into 3 clusters (cluster sub-groups); the expression change difference genes of the model group sample are 4 cluster; the expression change difference genes of the Qibo pulse-activating administration group sample are 4 clusters.
Particularly, the method further comprises the step of carrying out visualization processing on the clustering analysis processing result.
In particular, the standard of the cluster analysis processing in the cluster difference gene set screening module is that the Fold Change is more than or equal to 2 and the q-value is less than 0.1, namely, the screened genes of the Fold Change more than or equal to 2 and the q-value is less than 0.1 are genes with large characteristic expression difference Change.
Wherein the main difference group screening module is specifically configured to:
and respectively carrying out Biological Process enrichment analysis (namely Biological Process (BP) analysis) on the clustering difference gene sets of each cluster of the normal group, the model group and the Qibo pulse-generating administration group samples by adopting an R language Cluster Profile package, respectively comparing the enrichment analysis results of the normal group with the model group and the Qibo pulse-generating group, and screening to obtain main difference clusters (cluster) of the model group and the Qibo pulse-generating group.
Particularly, the obtained enrichment analysis result of the biological process is visualized by drawing a histogram, a bubble chart and the like, the biological processes of different clusters of different samples are compared, and a main difference cluster (main difference cluster) is obtained through screening.
In particular, the enrichment analysis of the biological process of each cluster in the samples of the normal group, the model group and the Qibo pulse-activating group shows that: compared with the normal group, cluster2 of the model group and the Qibo pulse-activating group is the main difference cluster.
In particular, the differential gene enrichment analysis is the Biological Process analysis (i.e., BP analysis) of the GO annotation system.
Wherein the main difference group analysis module is specifically configured to:
performing differential gene enrichment analysis on characteristic differential gene sets of the main differential group of the Qibo pulse-generating group and the main differential group of the model group, namely performing Biological Process enrichment analysis (namely Biological Process (BP analysis) of GO annotation system) on the characteristic differential genes of the main differential group of the Qibo pulse-generating group to obtain the Qibo pulse-generating composition and improve Biological processes such as atrial fibrillation related to aging, collagen fiber tissue, reaction to mechanical stimulation, reaction to peptide hormone, positive regulation and control of angiogenesis, reaction to selenium ions, cell adhesion, extracellular matrix tissue and the like;
KEGG pathway analysis is carried out on the characteristic difference gene sets of the main difference group of the Qibo pulse-generating group and the main difference group of the model group, namely the KEGG pathway analysis is carried out on the characteristic difference gene sets of the main difference group of the Qibo pulse-generating group, so that the Qibo pulse-generating composition can be used for improving the functions of atrioventricular fibrosis and ECM-receptor interaction, pertussis, complement and coagulation cascade, focal adhesion of cells, AGE-RAGE signal pathways in diabetic complications and the like.
In particular, the major difference group analysis module further comprises a characteristic difference gene set screening component of the major difference group: and the method is used for carrying out differential analysis on the genes of the main difference group of the Qibo pulse-generating group and the genes of the main difference group of the model group by adopting wilcox rank sum test of Seurat software to obtain a characteristic difference gene set of the main difference group of the Qibo pulse-generating group.
In particular, the characteristic difference gene sets of the main difference groups of the Qibo pulse group analyzed by the main difference group analysis module are Nppa, Myl7, Myl4, Apoe, Bgn, Reg3b, Ftl1, Col1a1 and Mgp.
The invention also provides an analysis method of the atrial fibrillation myocardial fibrosis, which comprises the following steps:
1) respectively carrying out spatial transcription sequencing treatment on myocardial tissue slices of animals of a normal group, an atrial fibrillation model group and a Qibo pulse-engendering administration group by adopting a spatial transcriptome technology to respectively obtain spatial transcriptome data of samples of the normal group, the model group and the Qibo pulse-engendering group;
2) respectively standardizing the transformation expression matrix data of the spatial transcriptome original data of each sample by using a sctformat (sct) algorithm in the R language source packet; then screening highly variant genes from the data after the standardization processing through a vst algorithm, and obtaining an expression change difference gene set;
3) respectively standardizing the expression change difference gene sets obtained by screening in each sample by using a Sctformat (SCT) algorithm in a R language Seurat packet; then, carrying out linear dimensionality reduction on the data subjected to the standardization processing by adopting a PCA method to obtain PCA dimensionality reduction data; then carrying out nonlinear dimensionality reduction processing on the PCA dimensionality reduction data by adopting a T-SNE nonlinear dimensionality reduction algorithm to obtain T-SNE dimensionality reduction data;
4) carrying out Cluster analysis on the T-SNE dimension reduction data based on an SNN clustering algorithm to obtain expression change difference gene Cluster subgroups (Cluster) of samples of a normal group, an atrial fibrillation model group and a Qibo pulse-activating administration group;
5) respectively carrying out difference comparison on the genes of each clustering subgroup of the normal group sample, the model group sample and the Qibo pulse-activating administration group sample and the genes of all other clustering subgroups of the corresponding group sample by adopting the wilcox rank sum test of Seurat software, and screening to obtain a clustering difference gene set (namely a gene set with large clustering expression difference change) of each cluster;
6) performing differential gene enrichment analysis on the clustered differential gene sets of each cluster of the normal group, the model group and the Qibo pulse-activating administration group samples by adopting an R language Cluster Profile package, respectively comparing the differential gene enrichment analysis results of the normal group with the model group and the Qibo pulse-activating administration group, and screening to obtain main differential groups of the model group and the Qibo pulse-activating administration group;
7) and (3) carrying out differential gene function enrichment analysis on the characteristic differential gene sets of the genes of the main differential group of the Qibo pulse-activating administration group sample and the genes of the main differential group of the model group sample to obtain a Qibo pulse-activating composition for interfering with and improving the related biological processes and signal paths of the characteristic differential genes of the atrial fibrillation.
Wherein, the spatial transcription sequencing treatment in the step 1) comprises the following steps:
1A) respectively carrying out tissue permeabilization treatment on myocardial tissue slices of animals in a normal group, an atrial fibrillation model and a Qippe pulse-engendering group so as to release mRNA in cells, and combining the mRNA with corresponding capture probes on a spatially transcribed gene expression chip;
1B) carrying out mRNA reverse transcription on the mRNA combined on the capture probe of the gene expression chip to synthesize a complete cDNA chain; then synthesizing a cDNA double chain; then incubation is performed to denature the cDNA; finally, recovering the denatured cDNA, and carrying out cDNA amplification and purification treatment to obtain amplified cDNA;
1C) respectively carrying out fragmentation, end repairing and A tail adding, magnetic bead double-end fragment screening, joint connection, magnetic bead purification after joint connection, sample Index PCR and magnetic bead double-end fragment screening after PCR on the amplified cDNA to construct and obtain a Visium spatial gene expression library;
1D) sequencing samples of Visium space gene expression libraries of normal group, atrial fibrillation model animal group and Qibo pulse-activating group samples respectively by using an Illumina NovaSeq 6000 platform to obtain space transcriptome sequencing data of each sample.
In particular, the spatial transcriptional sequencing process was performed according to the 10 × Genomics visium spatial transcriptome sequencing method (10 × Genomics (TXG) gene spatial transcriptional sequencing), and spatial transcriptional data were obtained.
In particular, the time for the tissue permeabilization in step 1A) is between 6 and 9min, preferably 8 min.
In particular, the method comprises the following steps of completely adhering myocardial tissue slices on a visium space gene tissue optimized glass slide (also called a visium space gene tissue optimized chip) with 8 capture areas, respectively carrying out enzyme treatment on 6 different capture areas on the glass slide at different times according to the operation flow specification of a space transcriptome sequencing method (10 Xgenomics visium), recording the fluorescence labeled cDNA signal intensity of each capture area, and selecting the enzyme treatment time corresponding to the capture area with high fluorescence intensity as the tissue permeabilization treatment time.
In particular, tissue slices with RIN more than or equal to 7 and good integrity are selected in the step 1A) for tissue permeabilization treatment.
Wherein, the step 1B) also comprises the step of detecting the fragments of the amplified cDNA, and the size range of the total fragments of the amplified cDNA is between 200 and 9000bp, which indicates that the quality control of the cDNA synthesized by the sample is qualified.
In particular, in the sequencing process in the step 1D), the sequencing strategy is PE 150; the sequencing depth was 50000 reads/spot.
Wherein the animals in step 1) are selected from healthy male SD rats.
In particular, the method further comprises the following steps before the spatial transcription sequencing treatment in the step 1): preparing a Visium space Gene Expression chip, namely completely adhering left atrial muscle tissue slices of animals of a normal group and a model group to a Visium space Gene Expression Slide (a Visium space Gene Expression chip or chip) with 4 capture regions respectively, covering naked tissues with OCT (optical coherence tomography) and freezing the naked tissues to prepare the Visium space Gene Expression chip for space transcription sequencing.
In the step 1), mRNA which is captured by oligonucleotide chains in a capture area of the slide glass in each tissue section and is marked with a unique molecular identifier UMI and a special spatial barcode is sequenced through a spatial transcriptome technology to obtain spatial transcriptome data.
Wherein, the standardization processing of the spatial transcriptome sequencing data in the step 2) comprises the following steps:
filtering spatial transcriptome data by adopting Fastp software to obtain sequencing data which can be directly used for subsequent analysis;
calculating cell barcode information contained in the filtered statistical sequencing data and corresponding counts by using a barcode processing algorithm, so as to judge the actually detected dot matrix 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.
In particular, it also comprises a step 2A): performing data comparison, gene quantification and site recognition on the obtained space transcriptome sequencing data of each sample by using 10 Xgenomics official software SpaceRange to finish upstream analysis; the main role of the upstream analysis is to convert the fastq files obtained from the original sequencing into the expression matrix required for the downstream analysis process.
Then, the spatial transcriptome sequencing data transformation expression matrix data of each sample is respectively normalized by using a sctformat (sct) algorithm in a source packet of the R language.
The method is used for comparing, quantifying genes and identifying sites of sequencing data of a space transcriptome by using 10 XGenomics official software SpaceRange to obtain a space transcription data expression matrix, wherein the space transcription data expression matrix is an M x N matrix, rows are genes, and columns are Spots.
In particular, the expression change difference gene set in the step 2) is the first 3000 expression change difference genes with expression change difference from high to low.
Wherein, the expression change difference genes of the normal group animal samples in the step 4) are clustered into 3 cluster subgroups; the expression change difference genes of the model group animal samples are 4 clusters; the expression change difference genes of the Qibo pulse-activating group sample are 4 clusters.
Particularly, the method further comprises the step of carrying out visualization processing on the clustering analysis processing result.
The molecular mechanism of pathological change of the pathological change area after drug intervention can be known by comparing the difference gene analysis of the pathological change area and the Qibo pulse-activating improved area and the normal area after drug administration.
Wherein, the difference comparison standard in the screening process of the clustering difference gene set in the step 5) is that the Fold Change is more than or equal to 2 and the q-value is less than 0.1, namely, the screened genes with the Fold Change more than or equal to 2 and the q-value less than 0.1 are genes with large characteristic expression difference Change.
In particular, the criteria for screening in step 5) are: the genes expressed in more than 25% of the Spots in at least one comparison group were analyzed for differential comparison.
Wherein, the differential gene enrichment analysis in the step 6) is the enrichment analysis of biological processes of the clustering differential gene set.
Particularly, in the step 6), the Biological Process enrichment analysis of the difference genes is carried out on the clustering difference gene set of each cluster of the normal group, the model group and the Qibo pulse-activating group samples, namely, the Biological Process analysis (BP analysis) of the GO annotation system, the enrichment analysis results of the normal group, the model group and the Qibo pulse-activating group are respectively compared, and the main difference clusters (main difference clusters) of the model group and the Qibo pulse-activating group are obtained through screening. .
In particular, the method also comprises the step of visualizing the Term obtained by enrichment analysis of the biological process by drawing a histogram, a bubble chart and the like.
Particularly, the enrichment analysis of the biological process of each cluster in the samples of the normal group, the model group and the Qibo pulse-activating group shows that: compared with the normal group, cluster2 of the model group and the Qibo pulse-activating group is the main difference cluster.
Wherein, the characteristic difference gene set is obtained in the step 7) according to the following steps: and carrying out difference analysis on the genes of the main difference group of the Qibo pulse-generating group and the genes of the main difference group of the model group by adopting wilcox rank sum test of Seurat software to obtain a characteristic difference gene set of the main difference group of the Qibo pulse-generating group.
In particular, the characteristic difference genes of the main difference groups of the model groups in the step 7) are Nppa, Myl7, Myl4, Apoe, Bgn, Reg3b, Ftl1, Col1a1 and Mgp.
In particular, the characteristic differential genes of the main differential group of the Qibo pulse-activating group relate to aging, collagen fiber tissues, responses to mechanical stimuli, responses to peptide hormones, positive regulation of angiogenesis, responses to selenium ions, cell adhesion, extracellular matrix tissues and other biological processes, are related to ECM-receptor interaction, pertussis, complement and coagulation cascade, focal adhesion of cells, AGE-RAGE signal pathway in diabetic complications and the like, and indicate that the Qibo pulse-activating composition intervenes and improves atrial fibrillation of atrial fibrillation through the above biological processes and signal pathways.
In particular, the functional enrichment analysis of the differential genes of the main differential group in step 7) comprises the following steps:
performing Biological Process enrichment analysis (i.e. Biological Process (BP analysis) of GO annotation system) on characteristic difference gene sets of genes of main difference groups of the Qibo pulse-activating administration group and the model group to obtain Qibo pulse-activating composition intervention and improve Biological processes of atrial fibrillation, such as aging, collagen fiber tissue, response to mechanical stimulation, response to peptide hormone, positive regulation of angiogenesis, response to selenium ions, cell adhesion, extracellular matrix tissue and the like;
KEGG pathway analysis is carried out on the characteristic difference gene set of the genes of the main difference group of the Qibo pulse-activating administration group and the genes of the main difference group of the model group, so that the Qibo pulse-activating composition can be obtained and can improve the correlation between the atrial fibrillation and ECM-receptor interaction, pertussis, complement and coagulation cascade, focal adhesion of cells, AGE-RAGE signal pathway in diabetic complications and the like.
Performing Biological Process enrichment analysis on characteristic difference genes of the main difference group of the Qibo pulse-generating group, namely performing difference gene enrichment analysis on a characteristic difference gene set of the genes of the main difference group of the Qibo pulse-generating group and the genes of the main difference group of the model group (namely performing Biological Process analysis (BP analysis) of a GO annotation system), obtaining a Qibo pulse-generating composition, performing intervention and improving the Biological processes of the fibrillation, such as aging, collagen fiber tissue, response to mechanical stimulation, response to peptide hormone, positive regulation and control of angiogenesis, response to selenium ions, cell adhesion, extracellular matrix tissue and the like;
KEGG pathway analysis is carried out on the characteristic difference gene set of the main difference group of the Qibo pulse-generating group, namely the KEGG pathway analysis is carried out on the characteristic difference gene set of the main difference group gene of the Qibo pulse-generating group and the main difference group gene of the model group, so that the Qibo pulse-generating composition is obtained, and correlation among the interaction between the atrial fibrillation and ECM-receptor, pertussis, complement and coagulation cascade, focal adhesion of cells, AGE-RAGE signal path in diabetic complications and the like is improved.
The invention overcomes the defects of the prior art, analyzes the mechanism of the atrial fibrillation fibrosis of the drug treatment by the space transcriptome technology, and explains the intervention of the Qibo pulse-activating composition and the mechanism of improving the atrial fibrillation by the cell and molecular level. The invention carries out sequencing on frozen sections of tissues of an atrial fibrillation rat and a medicine Qibo pulse-activating composition for treating the atrial fibrillation rat by means of a space transcriptome technology, carries out subsequent analysis by utilizing space transcriptome data obtained by sequencing, and then carries out clustering and difference analysis on all detected tissue transcriptome data so as to help to select Qibo pulse-activating particles to improve key genes and related molecular signal channels regulated and controlled by atrial fibrosis of the atrial fibrillation rat.
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 section, is beneficial to screening the transcription expression level of the myocardial tissue improvement area after atrial fibrillation myocardial tissue fibrosis and drug treatment, further screens the difference genes and molecular signal channels of the fibrosis area for intervening, improving and treating atrial fibrillation lesion, provides technical support for finally analyzing and understanding the intervening and treating after atrial fibrillation lesion and preventing the occurrence and development of the atrial fibrillation myocardial fibrosis, and provides research support of gene and molecular level.
The whole transcriptome in the whole tissue of the device obtains the complete transcriptome gene expression from the complete tissue, so the tissue does not need to be dissociated, the tissue dissociation bias is avoided, the clinical pathological information is combined, the pathological characteristics on the tissue are combined with the gene expression, and the used analysis software is combined with the histological gene expression data to simplify the data analysis.
Drawings
FIG. 1 is an experimental flow chart of the spatial transcriptome sequencing method (10X Genomics visium);
FIG. 2 is a HE staining chart and a sample spatial tissue distribution chart of rat tissue sections, wherein the upper is the HE staining chart, the lower is the spatial tissue distribution chart, and A is a normal group; b is a model group; c is a QPSM administration group;
FIGS. 3A-3C are volcano plots of gene reporter mutant genes in rat atrial tissue; wherein 3A is a normal group, 3B is a model group, and 3C is a QPSM administration group; in FIGS. 3A, 3B, and 3C, the abscissa represents the geometric mean of the gene expression level, and the ordinate represents the variance/variance statistic. The red-marked dots represent highly variant genes, 3000 in total, and mark highly variant genes of top 10.
FIG. 4 is a t-SNE plot of Spots clustering of rat atrial myotissue sections, wherein A is the normal group, B is the model group, and C is the QPSM administered group; each point in fig. 4 represents 1 spot, and different colors represent different clusters, and the distribution areas of the respective classes are labeled in detail.
FIGS. 5A, 5B and 5C are the clustering spatial distribution maps of the rat atrial muscle tissue sections of the normal group, the model group and the QPSM administration group, respectively;
FIGS. 6A-6C are graphs of enrichment analysis of the differential expression gene biological process of cluster0, cluster1 and cluster2 in the atrial muscle tissue of rats in the normal group, respectively;
FIGS. 6D-6G are graphs of enrichment analysis of the differential expression gene biological process of cluster0, cluster1, cluster2 and cluster3 of the atrial muscle tissue of rats in the model group, respectively;
FIGS. 6H-6K are respectively the enrichment analysis graphs of the differential expression gene biological process of cluster0, cluster1, cluster2 and cluster3 of the rat atrial muscle tissue of QPSM group;
Detailed Description
The invention will be further described with reference to specific embodiments, and the advantages and features of the invention will become apparent as the description proceeds. These examples are illustrative only and do not limit the scope of the present invention in any way. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention, and that such changes and modifications may be made without departing from the spirit and scope of the invention.
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.
Example 1
1. Experimental Material
1-1, Experimental animals
Healthy male SD rats (8, clean class II, beijing vindeli laboratory animal technology ltd), 4 weeks old, weight 180-: SCXK (Jing) 2014-0041.
The experimental animals (8 animals) were fed for 7 days in animal center of Guananmen hospital, Chinese academy of traditional Chinese medicine, and then molded, and the subsequent experiments were performed in groups seven days after molding.
1-2, laboratory instruments
BL-420F multichannel physiological signal acquisition and processing system (Chengdu Tai union software Co., China); a cryomicrotome Leica CM1850 (Leica, Germany).
1-3, reagents and medicaments
Acetylcholine chloride (Acetylcholine chloride, ACh; sigma, USA); anhydrous calcium chloride (sigma, USA); pentobarbital sodium (national drug group chemical agents limited, china); sodium chloride injection (agrimony, china);
the Qibo pulse-activating granule (QPSM, provided by Beijing Guangan Men Hospital preparation center) is prepared from radix astragali, radix Glehniae, radix Ophiopogonis, Succinum, fructus Schisandrae chinensis, semen Ziziphi Spinosae, Saviae Miltiorrhizae radix, radix scrophulariae, periostracum Cicadae, and Bombyx Batryticatus, and its preparation method comprises:
rinsing 30 parts of crude astragalus membranaceus, 12 parts of radix glehniae, 12 parts of radix ophiopogonis, 2 parts of amber, 6 parts of schisandra chinensis, 15 parts of spina date seeds, 15 parts of salvia miltiorrhiza, 15 parts of radix scrophulariae, 10 parts of stiff silkworms and 10 parts of periostracum cicada, uniformly mixing the rinsed materials in a multifunctional traditional Chinese medicine extraction kettle, adding tap water, and soaking the mixture for 30min, wherein the ratio of the weight of the added tap water to the weight (dry weight) of the mixture of the medicinal materials is 9: 1;
starting a power supply of the multifunctional extraction kettle to heat, heating to 100 ℃ (not limited to 100 ℃, and other temperatures are applicable), keeping the temperature for extracting for 3h at constant temperature, and then filtering to obtain a first extracting solution for later use; adding tap water into the filter residue, wherein the ratio of the weight of the added tap water to the weight (dry weight) of the medicinal material mixture is 5: 1, heating and extracting, keeping the heating temperature at 100 ℃ for 3 hours, and filtering to obtain a second extracting solution; mixing the extractive solutions obtained in 2 times, and vacuum evaporating and concentrating in a vacuum high-energy evaporation concentrator under reduced pressure to obtain concentrated extract, wherein the relative pressure of the reduced pressure concentration is-0.07 MPa (one atmosphere is 0, and the pressure during reduced pressure concentration is controlled to be-0.07 MPa relative to one atmosphere), the concentration temperature is 80 deg.C, and the extractive solutions obtained in 2 times are concentrated into extract with relative density of 1.2; drying the concentrated extract at 45-50 deg.C, pulverizing, and sieving with 180 mesh sieve to obtain dry extract powder; and mixing the dry extract powder with dextrin serving as an auxiliary material, using ethanol as an adhesive, granulating, and drying to obtain the qi and yin deficiency type atrial fibrillation traditional Chinese medicine composition (Qibo pulse-activating, QPSM) granules.
The Qibo pulse-activating composition for intervening and treating atrial fibrillation in the invention is illustrated by taking the Qibo pulse-activating particles as an example, and other Qibo pulse-activating compositions according to the following formula are all suitable for the invention: 5-30 parts of raw astragalus membranaceus, 5-12 parts of radix glehniae, 1.5-6 parts of schisandra chinensis, 9-15 parts of radix scrophulariae, 6-15 parts of spina date seeds, 3-10 parts of periostracum cicada, 9-15 parts of salvia miltiorrhiza, 6-12 parts of radix ophiopogonis, 1.5-3 parts of amber and 3-10 parts of stiff silkworm.
2. Experimental methods
2-1, constructing model animal
According to the experimental method (Aster tung, Huyuan, teacher commander and the like, the establishment of a vagal atrial fibrillation rat model and the study on atrial electrophysiology [ J ]. Heart journal, 2019,01:8-11) in the early stage of the subject group, the specific method is as follows:
after 7 days of adaptive feeding of healthy male SD rats, 4 animals were randomly selected for acetylcholine Ach (99ug/mL) -calcium chloride (CaCl) injection through tail vein210mg/mL), the administration volume is 0.1mL/100g, i.e. the volume of the acetylcholine-calcium chloride mixture is 0.1mL/100g, after 7 days of continuous administration, the electrocardiogram is measured immediately after the tail vein injection on the seventh day, and the typical AF wave of atrial fibrillation electrocardiogram appears as the electrocardiogram: p wave disappears, R-R intervals are unequal, and f waves with different sizes are substituted for the P wave disappears as a model making success mark, and detection results show that typical atrial fibrillation electrocardiograms appear in 4 rats, which indicates that an atrial fibrillation model rat is successfully constructed.
2-2, grouping experiments and administration
4 successfully modeled rats are randomly divided into 2 model groups and 2 Qipo pulse-generating groups according to an Excel random digital table method; wherein, the model group rats are administered with purified water by intragastric administration, and the Qibo pulse-activating group rats are administered with Qibo pulse-activating particle solution by intragastric administration. Gavage was performed for 4 weeks. All rats were first gazed in the morning of the 4 weeks, and the tail vein was injected with acetylcholine-calcium chloride mixture (0.1 mL/100g injection) within one hour after gavage.
The remaining 2 experimental rats which were not molded were used as a normal control group, and the rats in the normal group were first gavaged with purified water every morning; injecting normal saline (the injection amount is 0.1mL/100g) into tail vein within one hour after the intragastric administration, namely not injecting acetylcholine-calcium chloride mixed solution intravenously, and administering the normal saline after intragastric administration for 4 weeks;
the experimental rats were divided into 3 groups in total, i.e., normal control group (2); model group (2); experimental groups (qippe pulse-generating group (QPSM), 2 rats), rats in each group were dosed as follows:
1) normal group (Sham): purified water 1.0m1/100g, gavage, qd (once daily) 4W;
2) model group (Model): purified water 1.0m1/100g, gavage, qd 4W;
3) qibo pulse-generating group (QPSM): 1.0m1/100g of Qibo pulse-activating particle solution is perfused into the stomach, and qd 4W (0.2 g/ml); wherein: the Qibo pulse-activating particle solution is prepared by dissolving the prepared Qibo pulse-activating particles in purified water and mixing, and is 0.2 g/ml.
After 4 weeks of continuous dosing of the rats in each group, the following experimental tests were carried out:
2-3 electrocardiography
SD rats were weighed on an empty stomach in the morning and recorded after 4 weeks of continuous dosing; then, the SD rat is maintained anaesthetized by using low-dose isoflurane gas, the electrocardiogram of the SD rat is measured, and the standard II-lead electrocardiogram is recorded.
The recorded electrocardiogram was observed to appear as a typical AF wave: the P wave disappears, and the replaced f waves with different sizes are the marks of the occurrence of atrial fibrillation; to restore sinus rhythm, f wave disappears, and P wave appears as AF wave is stopped; the AF wave duration is recorded.
The electrocardiogram measurement result is as follows: the electrocardiogram of atrial fibrillation is not recorded in normal rats; the duration of atrial fibrillation of the rats in the model group is 11.735s, and the rats have typical atrial fibrillation electrocardiogram (AF) waves; the duration of atrial fibrillation of the Qibo pulse-activating granule group rats was: 4.253 s. The normal group of rats did not detect atrial fibrillation.
2-4, preparing atrial muscle tissue section sample
Drug dry prognosis, rat atrial muscle samples were collected at the end of week 4. Carrying out intraperitoneal injection anesthesia on each group of rats by using 3% sodium pentobarbital (30mg/kg), cutting the abdominal wall along the edge of the abdomen rapidly under the xiphoid process after the abdominal aorta is subjected to blood extraction, opening the diaphragm, cutting the chest wall along the anterior axillary lines at two sides, turning upwards towards the head side, cutting off the heart at the root of the heart, and cutting the left atrial musculature; placing the tissue into a culture dish, and sucking blood water around the tissue;
marking the freezing mould to mark the direction of the tissue, and filling the mould with precooled OCT embedding medium to avoid generating bubbles; placing the atrial muscle tissue into a mold containing a precooled OCT embedding medium, covering the surface of any bare tissue with the OCT embedding medium, and confirming that no air bubbles exist, particularly near the tissue; placing the mold into isopentane with forceps, but not allowing isopentane to soak into the mold until the tissue freezes; freezing time may vary depending on tissue type and size; after freezing, the tissue is transferred to a precooled freezing tube and then placed on dry ice; storing the OCT-embedded tissue block in a sealed container at-80 ℃ for long-term storage or immediately performing frozen sectioning; the sections were frozen at-70 ℃ and 20 sections of 10 μm in thickness were cut per tissue to prepare atrial muscle tissue sections of normal rats, model rats and QPSM rats.
2-5 rat atrial myocyte space transcriptome sequencing process
Rat atrial muscle tissue spatial transcriptome sequencing a spatial transcriptome sequencing test was performed on the left atrial muscle tissue of each group of rats (one randomly selected from each group) according to the experimental procedure of the spatial transcriptome sequencing method (10 × Genomics visium) as shown in fig. 1, specifically as follows:
2-5-1 tissue quality testing
RNA extraction was performed on atrial muscle tissue sections (10 per tissue) and quality control was performed. Completely adhering the atrial muscle tissue section samples with RIN value more than or equal to 7 and good integrity to a visium space gene tissue optimized glass slide (also called visium space gene tissue optimized chip) with 8 capture areas respectively, and performing tissue permeabilization time optimization treatment; completely adhering to a Visium space Gene Expression Slide glass (also called a Visium space Gene Expression chip) with 4 capture regions, immediately freezing the section, covering the naked tissue with OCT (optical coherence tomography) and freezing, storing in a sealed container at-80 ℃, and subsequently performing space transcription sequencing treatment.
2-5-2 tissue permeabilization time optimization
Before constructing the library, optimizing the optimal time for the mRNA permeabilization of the target tissue; the optimal permeabilization time, i.e., optimal permeabilization conditions of the mRNA in library construction, is determined by the time gradient of the enzymatic treatment and the intensity of the fluorescently labeled cDNA signal.
Each tissue-optimized slide contained 8 capture zones, each zone being 8mm by 8mm in size; 6 tissue sections (time-graded) can be applied, and the remaining 2 regions are provided with a positive control and a negative control; wherein 6 tissue slices are used for determining the time gradient distribution of the optimal permeabilization time, and the different permeabilization times of the 6 tissue slices are 3min,6min,9min,12min,18min and 24 min.
The experimental process is as follows: firstly, sticking a glass slide according to the area, and carrying out fixation, HE dyeing and name field imaging on the stuck glass slide; then carrying out time gradient permeabilization and cDNA synthesis of fluorescence labeling; finally, the tissue is removed and fluorescence imaged, and the optimal permeabilization time is determined by the intensity of the fluorescence signal.
After different permeabilization time gradients are carried out on the SD rat atrial muscle tissue slices, fluorescence scanning is carried out, and the fluorescence intensity of slices with the permeabilization time of 6min and 9min is stronger, so that the permeabilization time in the range of 6-9min can be used as the optimal tissue permeabilization time, the tissue permeabilization time with longer time is preferentially selected, and finally 8min is selected as the optimal time for permeabilization of the atrial muscle tissue; the permeabilization time is 6-9min, preferably 8 min.
2-5-3, fixation, staining and imaging
Surface mounting: completely adhering a tissue slice with a quality inspection RIN value of more than or equal to 7 and good integrity to a Visium space Gene Expression Slide (Visium space Gene Expression chip, Visium Spatial Gene Expression Slide) with 4 capture regions, immediately freezing the slice, covering the naked tissue with OCT, freezing the tissue, and storing the frozen tissue in a sealed container at-80 ℃;
fixing: taking out the slide, placing the slide on an adapter immediately with the tissue surface facing upwards, and incubating for 1min at 37 ℃; after 1min, the slides were completely immersed in pre-cooled methanol at-20 ℃ and incubated for 30 min.
HE staining: and (3) carrying out HE (high-intensity eosin) staining on the tissue after methanol is fixed for 30min, and respectively carrying out incubation, cleaning, drying and other processes on the visium spatial gene expression glass slide in isopropanol, hematoxylin, kalium and eosin Mix to complete HE staining.
Open field imaging experiment: the name imaging experiment was completed by using a Metafer slide scanning platform (metassystems) to shoot at 20 times magnification, and using a Visium space gene expression slide to image 4 reference points per area clearly, with clear morphological structure. Raw images were stitched with Vslide software (metassystems). The observation results are shown in FIG. 2.
The HE staining patterns of the atrial myotissue sections of the 3 groups (normal, model, QPSM) are listed above in fig. 2. As can be seen from fig. 2: compared with the normal group, the model group has inflammatory cell infiltration (namely lesion areas) locally; inflammatory cell infiltration was significantly reduced in the QPSM group compared to the model group, wherein the inflammatory cell infiltration areas are shown in the model group of fig. 2, the QPSM group is darker.
2-5-4, cDNA amplification
Firstly, respectively carrying out tissue permeabilization on a visium space gene expression chip of each sample to release mRNA in cells, and combining the mRNA to a corresponding capture probe on the chip, wherein the tissue permeabilization time is 8min which is determined by a tissue optimization experiment; then reverse transcription of mRNA is carried out to obtain a complete cDNA chain; then synthesizing a cDNA double chain; then, at the end of incubation, incubating with 0.08M KOH for 10min to denature the two chains; finally, the denatured double strand is recovered and cDNA amplification is performed.
2-5-5, cDNA quantification and quality control
Respectively taking 1 mu l of cDNA amplified from each sample to dilute the cDNA to 10 mu l, and then carrying out fragment detection by using a High Sensitivity DNA chip (Agilent Bioanalyzer), wherein the size range of the cDNA total fragments of 3 samples (a normal group, a model group and a QPSM group) in the experiment is between 200 and 9000bp, which indicates that the quality control of the cDNA synthesized by the 3 samples is qualified;
2-5-6, construction of Gene expression library
Respectively taking 10uL of cDNA of each of 3 amplified and purified samples, performing fragmentation, end repair and A tail addition, screening magnetic bead double-end fragments, connecting joints, purifying the magnetic beads after connecting the joints, performing sample Index PCR (namely Barcode, identifying which sample the obtained data belongs to from the 3 samples), and screening the magnetic bead double-end fragments after PCR to complete library construction;
2-5-7, library quantification and quality control
Determining the concentration of the library by utilizing the Qubit 4.0; the Qseq400 was used for fragment detection, the general library distribution was 800bp at 300-. The cDNA libraries of 3 samples of the invention are equally distributed between 300-800bp, and the average fragment is 400-500 bp. The quality control of the library is qualified.
2-5-8, spatial transcriptome sequencing
Sequencing each sample in the Visium spatial gene expression library of 3 samples by utilizing an Illumina NovaSeq 6000 platform to obtain spatial transcriptome sequencing data of each sample, wherein the sequencing strategy is PE150, and the sequencing depth is 50000 reads/spot.
The spatial transcription data may be externally input. The external treatment process can be performed according to the experimental procedure of the above-described space transcriptome sequencing method (10 × Genomics visium).
2-6, data analysis
2-6-A upstream analysis
The 10 XGenomics official software SpaceRange (v1.2.2) is used for data comparison, gene quantification and site recognition to complete upstream analysis, namely, a fastq file obtained by original sequencing is converted into an expression matrix required by downstream analysis processing.
2-6-B downstream analysis
The expression matrix (upstream analysis results) was normalized, clustered and marker gene screened using the R language Seurat (v4.0.1) package, and functional enrichment analysis was performed using the R language clusterioprofiler (v3) package. The data was filtered using default parameters for analysis. And the visual display of the processing result is completed through the R language ggplot2 package.
3. Results and analysis
3.1 upstream analysis results
Table 1 raw data statistics
Note: and (5) reading: the total number of pair-end Reads in Raw Data; total _ base (mbases): raw Data total base number (Mb); GC (%): the GC content of Raw Data, namely the percentage of G and C bases in the Raw Data to the total bases; n (%): n accounts for the ratio in Raw Data; q20 (%): percentage of bases with a Raw Data quality value greater than or equal to 20; q30 (%): percentage of bases with Raw Data quality value greater than or equal to 30.
As can be seen from Table 1: the total spot (spot marked on chip) number for the 3 groups is: 4458, 3791, 3820; the median basis factor of each spot is 1252, 2094 and 1530.5, which shows that the capture effect is good and the permeabilization condition is optimized. The sequencing saturation degrees are respectively 90.4, 86.2 and 82.5. Median UMI of 5,666, 13,020, 9,117 for each spot; the spot average reads number is 86,532.33; 147,189.65, respectively; 88,098.41, respectively; the sequencing depth is qualified.
3.2 downstream analysis results
3-2-1, screening of highly mutated genes
The transformation expression matrix data of the original sequencing data of the spatial transcriptome sequencing data of each sample is normalized by using a sct algorithm in an R language source package, then a gene set with the largest expression change in the normalized data is screened out by using a vst algorithm to serve as high-variance genes (HVGs), and the first 3000 genes with large expression change differences (namely, the genes with large expression changes and ranking top 3000) are selected to obtain a volcanic image of the high-variance genes, wherein the volcanic images of the normal group, the model group and the QPSM group are shown in fig. 3A, 3B and 3C.
The gene set with large gene expression change shows that the gene set is meaningful for judging pathological mechanism, medicine intervention and improvement of pathological changes after treatment, and subsequent treatment is carried out based on the development of the 3000 genes. The highly variant genes in the volcano map are above the non-highly variant genes, and the 10 genes with the largest variation in each group are respectively marked with the gene names in the corresponding volcano map.
3-2-2, dimensionality reduction and clustering
The 3000 highly variant gene data screened in each sample (normal group, model group, QPSM group) were further normalized by using the sctformat (sct) algorithm in the R language source packet, to obtain highly variant gene normalized data.
The purpose and function of the normalization process of the 3000 highly variant gene data obtained by using the sctformat (sct) algorithm in the R language source package is to remove the variation caused by the difference of different samples and different spot sequencing depths.
Expression data from 10 × Genomics, quantified by the spacerange software, is a matrix of M × N (rows are genes and columns are Spots). The number of Spots can typically be hundreds or thousands. Clustering such matrices is extremely computationally intensive. Therefore, before clustering the Spots, the data needs to be subjected to dimensionality reduction.
Firstly, carrying out linear dimensionality reduction on an expression matrix by adopting a PCA method to obtain PCA linear dimensionality reduction data, and then carrying out dimensionality reduction on the PCA linear dimensionality reduction data by adopting a T-SNE nonlinear dimensionality reduction algorithm to obtain T-SNE nonlinear dimensionality reduction data; and finally, clustering the T-SNE nonlinear dimension reduction data based on an SNN clustering algorithm to obtain expression change difference gene cluster subsets, a Spots clustered T-SNE graph (shown in figure 4) and a clustering space distribution diagram (shown in figures 5A-5C).
Wherein A, B, C in FIG. 4 are t-SNE maps after dimension reduction and clustering of gene data in normal group, model group and QPSM group slices, respectively, different colors represent different classes, and then are mapped onto the slices so as to visually see the relative positions of different clusters. In the t-SNE plot of the normal group in FIG. 4, red represents cluster0, green represents cluster1, and blue represents cluster 2; in the t-SNE graphs of the model group and the QPSM administration group, red represents cluster0, green represents cluster1, blue represents cluster2, and purple represents cluster 3.
The space transcriptome data of the atrial muscle tissue of the rats in the normal group are aggregated into 3 clusters (shown as A in figure 4) by using an SNN clustering algorithm after the dimensionality reduction treatment; the clustering space distribution diagram is shown in FIG. 5A; model group, QPSM group rat atrial muscle tissue spatial transcriptome data were respectively clustered into 4 clusters (as B, C in FIG. 4) by SNN clustering algorithm after dimensionality reduction treatment; the clustering space distribution maps of the model group and the QPSM group are respectively shown in FIGS. 5B and 5C. Note: each point in FIGS. 5A-5C represents a spot, detailing the distribution area of each class.
Each cluster represents the gene expression condition of the region where the cluster is located, the gene expression difference between the two regions can be known by comparing the gene expression difference between different clusters, and a gene set related to pathological changes of the pathological change region can be obtained by the differential gene analysis of the normal region and the pathological change region; the differential gene analysis of the lesion area of the special model group and the lesion improvement area after the pharmaceutical intervention can obtain the related gene sets of the lesion area improvement after the pharmaceutical intervention and the treatment.
3-3, differential expression Gene analysis
Screening differential expression genes for different clusters by adopting wilcox rank sum test of Seurat software, namely comparing the genes of each cluster of 3 groups of rat atrial muscle tissues with the genes of all other clusters, screening genes expressed in more than 25% of Spots in at least one comparison group, and performing differential comparison analysis to obtain a differential gene set of each cluster.
The differential screening standard of the invention is that the gene with the Fold Change more than or equal to 2 and the q-value less than 0.1 is the gene with large characteristic expression difference Change (namely the clustered differential gene set).
For example, the cluster2 gene in the model group sample is compared with all genes of other clusters (0, 1, 3) on the same section, and the differential gene set of cluster2 is selected.
3-4, functional enrichment analysis
With the development of high-throughput technology, the related research field of biomedicine enters the omics era, and the research of a single gene cannot meet the needs of researchers. However, such huge data brings new challenges to the efficient extraction and analysis of information. Taking sequencing data as an example, sequencing result analysis will often result in a list of genes or proteins that are differentially expressed. However, it is difficult for many researchers to relate this long string of genes or proteins to a biological phenomenon under study and its underlying mechanisms. One way to address this challenge is to divide a gene or protein list into multiple parts, thereby reducing the complexity of the analysis. Researchers have developed multiple annotation databases in order to resolve which classes to classify. In order to solve how to classify genes into different classes, researchers often perform enrichment analysis on gene functions, and it is expected to discover biological pathways that play key roles in biological processes, thereby revealing and understanding the basic molecular mechanisms of biological processes, in which various kinds of software are developed.
Functional enrichment assays can separate hundreds or thousands of genes, proteins, or other molecules into different pathways to reduce the complexity of the assay. Under two different experimental conditions, the pathway of activation is clearly more convincing than a simple list of genes or proteins.
3-4-1, differential Gene GO enrichment analysis
The GO database (Gene Ontology database) is a structured standard biological annotation system constructed by GO organization (Gene Ontology Consortium) in 2000, aims to establish a standard vocabulary system of Gene and product knowledge, and is suitable for various species. The GO annotation system is a directed acyclic graph, containing three main branches, namely: biological processes (Biological processes), Molecular functions (Molecular functions) and Cellular components (Cellular components).
Respectively carrying out Biological Process (Biological Process) enrichment analysis (namely BP analysis of GO) on the cluster differential expression gene set of each cluster of three groups of rat atrial muscle tissue samples by adopting an R language cluster Profile package to obtain a Biological Process enrichment analysis result; and then, visualizing the enrichment analysis result by adopting a drawing histogram, a bubble chart and the like. And comparing the biological processes of the clusterings of different samples, and selecting the clusterings with the largest difference in the biological processes according to the difference in the biological processes to serve as a basis for further screening the different clusterings.
The enrichment analysis results of the biological process of the differentially expressed genes of 3 clusters (cluster) of atrial muscle tissues of rats in the normal group are shown in FIGS. 6A-6C; the enrichment analysis results of the biological process of the differentially expressed genes of 4 clusters of the atrial muscle tissues of the rats in the model group are shown in FIGS. 6D-6G; the enrichment analysis results of the biological process of the differentially expressed genes of 4 clusters of the QPSM group rat atrial muscle tissue are shown in FIGS. 6H-6K.
The biological process analysis of each cluster in 3 groups of rat atrial muscle tissue samples shows that: cluster2 was the major differential cluster between the model group and the QPSM-administered group compared to the normal group, and was consistent with the pathologically diagnosed abnormal site (i.e., with HE stained images showing abnormal sites of atrial muscle in atrial fibrillation rats), so cluster2 was further determined to be the major differential group (cluster) associated with atrial fibrillation, in both the model group and the QPSM-administered group. Through biological process analysis and analysis combined with HE staining results, the cluster2 of the model group and the QPSM administration group is the main differential cluster related to myocardial fibrosis,
the differential gene analysis is carried out on the gene of the main differential class cluster2 of the QPSM administration group and the gene of the main differential class cluster2 of the model group, so that a characteristic differential gene set of the medicine Qibo pulse-activating granules interfering atrial fibrillation muscle fibrosis can be obtained.
The difference analysis is carried out on the gene of cluster2 of the QPSM administration group and the gene of cluster2 of the model group by adopting wilcox rank sum test of Seurat software, and a characteristic difference gene set of the QPSM group cluster2 expression gene and the model group cluster2 expression gene is obtained by screening, namely the characteristic difference gene set of the QPSM composition intervening atrial fibrillation is obtained.
The difference gene analysis results of the QPSM group show that the characteristic difference genes between cluster2 of the QPSM administration group and the model group mainly comprise Nppa, Myl7, Myl4, Apoe, Bgn, Reg3b, Ftl1, Col1a1, Mgp and the like, namely the characteristic difference gene set for obtaining the QPSM composition to improve the atrial fibrillation (namely Nppa, Myl7, Myl4, Apoe, Bgn, Reg3b, Ftl1, Col1a1, Mgp and the like).
The difference analysis standard of the invention is that the gene with the Fold Change more than or equal to 2 and the q-value less than 0.1 is the difference gene set of the main difference cluster 2.
For example, the gene of cluster2 of the QPSM group sample is compared with all the genes of cluster2 of the model group section sample, and the feature difference gene set of main cluster2 of the QPSM group for improving the atrial fibrillation is screened.
Biological Process analysis of GO annotation system (i.e. BP analysis in GO analysis) is carried out on characteristic difference gene sets of the QPSM composition of the main difference class cluster2 in the QPSM group for improving the atrial fibrillation, and the discovery shows that the characteristic difference gene sets of the QPSM composition for improving the atrial fibrillation relate to aging, collagen fiber tissues, reactions to mechanical stimulation, reactions to peptide hormones, positive regulation of angiogenesis, reactions to selenium ions, cell adhesion, extracellular matrix tissues and other Biological processes, namely the QPSM composition achieves the purpose of improving the atrial fibrillation by regulating the aging, collagen fiber tissues, reactions to mechanical stimulation, reactions to peptide hormones, positive regulation of angiogenesis, reactions to selenium ions, cell adhesion, extracellular matrix tissues and other Biological processes.
The feature difference gene set for improving the atrial fibrillation myocardial fibrosis by the QPSM composition of the main difference group cluster2 in the QPSM group is further analyzed by KEGG pathway, and the feature difference gene set for improving the atrial fibrillation myocardial fibrosis of the main difference group cluster2 in the QPSM group is found to be related to ECM-receptor interaction, pertussis, complement and coagulation cascade, focal adhesion of cells, AGE-RAGE signal pathway in diabetic complications and the like, namely, the feature difference gene set for improving the atrial fibrillation myocardial fibrosis is related to ECM-receptor interaction, pertussis, complement and coagulation cascade, focal adhesion of cells, AGE-RAGE signal pathway in diabetic complications and the like.
Claims (10)
1. An analytical equipment for improving atrial fibrillation myocardial fibrosis by using Qipo pulse-activating composition based on space transcriptome technology is characterized by comprising:
the expression change difference gene screening module is used for standardizing the spatial transcription data of animal myocardial tissue slice samples of a normal group, an atrial fibrillation model group and a Qibo pulse-engendering administration group, and screening highly-variant genes of the standardized data through a vst algorithm to obtain an expression change difference gene set with expression change difference from high to low;
the dimensionality reduction processing module is used for carrying out linear dimensionality reduction processing on the data obtained by carrying out standardization processing on the expression change difference gene set obtained by screening in normal group, model group and Qibo pulse-activating group samples by adopting a PCA method to obtain PCA dimensionality reduction data; then, carrying out nonlinear dimensionality reduction on the PCA dimensionality reduction data by adopting a nonlinear dimensionality reduction algorithm of the T-SNE to obtain T-SNE dimensionality reduction data;
the cluster analysis module is used for carrying out cluster analysis processing on the dimension reduction processing data by adopting an SNN (single nucleotide network) clustering algorithm to obtain expression change difference gene cluster subgroups of samples of a normal group, a model group and a Qibo pulse-activating group;
the cluster difference gene set screening module is used for respectively carrying out difference comparison on the genes of each cluster subgroup of the normal group, the atrial fibrillation model group and the Qibo pulse-activating group sample with the genes of all other cluster subgroups of the tissue sample of the corresponding group by adopting the wilcox rank sum test of Seurat software, and screening to obtain a difference gene set of each cluster;
the main difference group screening module is used for respectively carrying out difference gene enrichment analysis on the cluster difference gene sets of each cluster of the normal group, the model group and the Qibo pulse-activating group samples by adopting an R language Cluster Profile packet, comparing the enrichment analysis results of the normal group with the model group and the Qibo pulse-activating group, and screening to obtain a main difference group;
and the main difference group analysis module is used for carrying out difference gene function enrichment analysis on the main difference group of the Qibo pulse-generating group and the characteristic difference gene set of the main difference group of the model group to obtain the Qibo pulse-generating composition and improve the related biological process and signal path of the difference genes of the atrial fibrillation.
2. The analysis device of claim 1, further comprising a spatial transcription module for performing spatial transcription sequencing processing on the atrial muscle tissue slice samples of the normal group animals, the atrial fibrillation model group animals and the Qipo pulse-engendering administration group animals respectively by using a spatial transcriptome technology to obtain spatial transcription data of the normal group animals, the model group animals and the Qipo pulse-engendering group animals.
3. The analysis apparatus according to claim 1 or 2, wherein the cluster analysis module has 3 cluster (cluster subpopulations) of the expression change difference genes for the normal group samples; the expression change difference genes of the model group samples are clustered into 4 clusters; the expression change difference genes of the Qibo pulse-activating administration group sample are 4 clusters.
4. The analysis device as claimed in claim 1 or 2, wherein the standard for comparing the differences in the cluster difference gene set screening module is that the Fold Change is more than or equal to 2 and the q-value is less than 0.1, i.e. the genes with the Fold Change more than or equal to 2 and the q-value is less than 0.1 are screened as the genes with large characteristic expression difference Change.
5. The analysis device according to claim 1 or 2, wherein the major difference group screening module is specifically configured to: and (2) respectively carrying out Biological Process enrichment analysis (namely, Biological Process analysis (BP analysis) of a GO annotation system) on the clustering difference gene sets of each cluster of the samples of the normal group, the model group and the Qibo pulse-activating administration group by adopting an R language Cluster Profile packet, comparing the Biological Process enrichment analysis results of the normal group with the model group and the Qibo pulse-activating administration group, and screening to obtain the main difference groups.
6. The analysis device according to claim 1 or 2, wherein the characteristic difference gene set of the main difference group analyzed by the main difference group analysis module is Nppa, Myl7, Myl4, Apoe, Bgn, Reg3b, Ftl1, Col1a1, Mgp.
7. The analysis device according to claim 1 or 2, wherein the major difference group analysis module is specifically configured to:
performing differential gene enrichment analysis on a characteristic differential gene set for intervening and improving atrial fibrillation myocardial fibrosis of a main differential group of a Qibo pulse-activating administration group, namely performing Biological Process enrichment analysis (namely Biological Process (BP) analysis of a GO annotation system) on the characteristic differential genes of the main differential group of the Qibo pulse-activating administration group and the main differential group of a model group to obtain the Qibo pulse-activating composition for intervening and improving the atrial fibrillation myocardial fibrosis, wherein the aging is involved, the collagen fiber tissue reacts on mechanical stimulation, the peptide hormone, the angiogenesis is positively regulated, the selenium ion, the cell adhesion, the extracellular matrix tissue and other Biological processes;
KEGG pathway analysis is carried out on the characteristic difference gene set for improving the atrial fibrillation by the Qibo pulse-activating composition of the main difference group of the Qibo pulse-activating administration group, and KEGG pathway analysis is carried out on the characteristic difference gene set for improving the atrial fibrillation of the main difference group of the Qibo pulse-activating administration group and the main difference group of the model group, so that the Qibo pulse-activating composition is obtained and is used for improving the correlation among the interaction between the atrial fibrillation and an ECM-receptor, pertussis, complement and coagulation cascade, focal adhesion of cells, AGE-RAGE signal pathways in diabetic complications and the like.
8. An analysis method for improving atrial fibrillation myocardial fibrosis by using a Qibo pulse-activating composition based on a spatial transcriptome technology is characterized by comprising the following steps of:
1) respectively carrying out spatial transcription sequencing treatment on myocardial tissue slices of animals of a normal group, an atrial fibrillation model group and a Qibo pulse-engendering administration group by adopting a spatial transcriptome technology to respectively obtain spatial transcriptome data of samples of the normal group, the model group and the Qibo pulse-engendering administration group;
2) respectively standardizing the spatial transcriptome data conversion expression matrix data of each sample by using a sctformat (sct) algorithm in an R language source packet; then screening highly variant genes from the data after the standardization processing through a vst algorithm, and obtaining an expression change difference gene set;
3) respectively standardizing the expression change difference gene sets obtained by screening in each sample by using a sctransform (sct) algorithm in an R language seruat packet; then, carrying out linear dimensionality reduction on the data subjected to the standardization processing by adopting a PCA method to obtain PCA dimensionality reduction data; then carrying out nonlinear dimensionality reduction processing on the PCA dimensionality reduction data by adopting a T-SNE nonlinear dimensionality reduction algorithm to obtain T-SNE dimensionality reduction data;
4) carrying out Cluster analysis on the T-SNE dimension reduction data based on an SNN clustering algorithm to obtain expression change difference gene Cluster subgroups (Cluster) of samples of a normal group, a model group and a Qibo pulse-activating administration group;
5) respectively carrying out difference comparison on the genes of each clustering subgroup of the normal group sample, the model group sample and the Qibo pulse-activating administration group sample and the genes of all other clustering subgroups of the corresponding group sample by adopting the wilcox rank sum test of Seurat software, and screening to obtain a clustering difference gene set (namely a gene set with large clustering expression difference change) of each cluster;
6) performing differential gene enrichment analysis on the clustered differential gene sets of each cluster of the normal group, the model group and the Qibo pulse-activating administration group samples by adopting an R language Cluster Profile package, respectively comparing the differential gene enrichment analysis results of the normal group with the model group and the Qibo pulse-activating administration group, and screening to obtain main differential groups of the model group and the Qibo pulse-activating administration group;
7) and (3) carrying out differential gene function enrichment analysis on the characteristic differential gene sets of the genes of the main differential group of the Qibo pulse-activating administration group sample and the genes of the main differential group of the model group sample to obtain a Qibo pulse-activating composition for interfering with and improving the related biological processes and signal paths of the characteristic differential genes of the atrial fibrillation.
9. The method of claim 8, wherein the spatial transcriptional sequencing process of step 1) comprises the steps of:
1A) respectively carrying out tissue permeabilization treatment on myocardial tissue slices of animals in a normal group, an atrial fibrillation model group and a Qippe pulse-engendering administration group so as to release mRNA in cells, and combining the mRNA with corresponding capture probes on a spatially transcribed gene expression chip;
1B) carrying out mRNA reverse transcription on the mRNA combined on the capture probe of the gene expression chip to synthesize a complete cDNA chain; then synthesizing a cDNA double chain; then incubation is performed to denature the cDNA; finally, recovering the denatured cDNA, and carrying out cDNA amplification and purification treatment to obtain amplified cDNA;
1C) respectively carrying out fragmentation, end repairing and A tail adding on the amplified cDNA, screening magnetic bead double-end fragments, connecting joints, purifying magnetic beads after connecting joints, carrying out Index PCR on a sample, screening the magnetic bead double-end fragments after PCR, and constructing to obtain a Visium spatial gene expression library;
1D) sequencing samples of Visium space gene expression libraries of animal samples of a normal group, an atrial fibrillation model group and a Qibo pulse-engendering administration group respectively by using an Illumina NovaSeq 6000 platform to obtain space transcriptome sequencing data of each sample.
10. The method of claim 8, wherein said differential gene function enrichment analysis of step 7) comprises:
performing Biological Process enrichment analysis (namely, Biological Process (BP) analysis of GO annotation system) on the characteristic difference gene set of the genes of the main difference group of the Qibo pulse-activating administration group and the genes of the main difference group of the model group to obtain the Qibo pulse-activating composition for improving the Biological processes of the atrial fibrillation, such as aging, collagen fiber tissues, reaction on mechanical stimulation, reaction on peptide hormone, positive regulation of angiogenesis, reaction on selenium ions, cell adhesion, extracellular matrix tissues and the like;
KEGG pathway analysis is carried out on the characteristic difference gene set of the genes of the main difference group of the Qibo pulse-activating administration group and the genes of the main difference group of the model group, and the obtained Qibo pulse-activating composition improves the correlation between the atrial fibrillation and ECM-receptor interaction, pertussis, complement and coagulation cascade, focal adhesion of cells, AGE-RAGE signal pathway in diabetic complications and the like.
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