The tutorials of Huanfeng Gene not only teach you how to use it, but also regularly analyze some related articles. Learning the tutorials is only the basis, but integrating the analysis results into the articles is the purpose. I think our tutorials are pretty good, and you can follow our If you get good results from the tutorial analysis and post an article, remember to let us know and thank us in the article!
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In this issue, I share an article published in Nature Communications (IF: 16.6) in August 2023. The author studied the correlation between the immunosuppressive tumor microenvironment and liver metastasis of pancreatic cancer based on scRNA-seq. This article can be realized using the bio-information sharing tutorial in the Huanfeng Gene public account. Teachers who need similar ideas can contact us!
Summary
Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease that is refractory to all targeted and immunotherapies. However, our understanding of the PDAC microenvironment, particularly the metastatic microenvironment, is very limited, in part due to the inaccessibility of metastatic tumor tissue. Here, we demonstrate the single-cell transcriptomic landscape of simultaneously resected PDAC primary tumors and matched liver metastases. We performed a comparative analysis of the cellular composition and functional phenotypes of primary and metastatic tumors. Tumor cells exhibit distinct transcriptome profiles in liver metastases with clear evolutionary pathways from primary tumor cells. We also identified specific subtypes of stromal cells and immune cells in metastatic lesions that are critical for the formation of a pro-tumor microenvironment, including RGS5 + cancer-associated fibroblasts, CCL18 + lipid-associated macrophages, S100A8 + neutral Granulocytes and FOXP3 + regulatory T cells. Cell interactome analysis further revealed that the lack of tumor-immune cell interactions in metastatic tissue contributes to the formation of an immunosuppressive microenvironment. Our study provides a comprehensive characterization of the transcriptional landscape of PDAC liver metastases.
Bioinformation analysis process
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Relevant dataset selection:
4 patients with pathological diagnosis of PDAC with liver metastasis, TCGA-PDAC bulk RNA-seq
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Gene set selection
Inflammation-related genes:IFNG, IFNGR1, IFNGR2, IL10, IL12A, IL12B, IL12RB1, IL12RB2, IL13, IL17A, IL17F, IL18, IL18R1, IL18RAP, IL1A, IL1B, IL2, IL21, IL21R, IL22 , IL23A, IL23R, IL2RG, IL4, IL4R, IL5, IL6, JUN, NFKB1, RELA, RORA, RORC, S100A8, S100A9, STAT1, STAT3, STAT4, STAT6, TGFB1, TGFB2, TGFB3, and TNF
Cytotoxicity-related genes: GZMA, GZMB, GZMH, GZMK, GZMM, GNLY, PRF1, and FASLG, IFNG, TNF, IL2R, and IL2
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Bioinformation analysis method:
We extracted all the analysis content from the analysis process of the article and compiled it into 17 analysis items. Each item includes the content of the analysis. These analyzes constitute the entire article. This article belongs to the single-cell sequencing bioinformatics analysis article. Below we Let’s take a look at which analyzes can be implemented using the tutorials on the Huanfeng Gene official account. Click on the analysis barcode and it will jump to the tutorial on the corresponding official account. Follow the tutorial and you can easily get high scores, as follows:
1. Single cell data comparison (CellRanger V2.1.1)
2. Single cell removal of double cells (DoubletFinder)
3. Single cell data charging and analysis (Seurat V4.0.6)
4. Intraductal single-cell CNV estimation (InferCNV)
5. GO annotation analysis (clusterProfiler)
6. Gene variant enrichment analysis (GSVA)
7. Time analysis of cell development trajectories (Monocle2)
8. Calculate RNA transcription velocity (velocity)
9. Cell-cell interaction analysis (CellPhoneDB)
10. Immune infiltration analysis of TCGA-PDAC expression data (CIBERSORTx)
11. TCGA data analysis (TCGAbiolinks)
12. Construct a Cox hazard ratio model (survival)
13. Survival analysis of marker genes (Kaplan–Meier)
14. Draw bar chart (Barplot)
15. Draw a violin plot (ViolinPlot)
16. Draw boxplot (BoxPlot)
17. Multiple group comparative analysis statistics (ANOVA)
Research result
1. Single-cell transcriptomic analysis of primary and matched metastatic PDAC tissues
a. Sample collection and data analysis process for this study.
b. Box plot showing the scaled mean expression of inflammatory signatures (n = 42) in cells from different sample groups.
c. Unified manifold approximation and projection (UMAP) plot showing the integrated cell map consisting of 29 cell clusters from 12 annotated cell types. Cells are colored by clusters.
d. Dot plot showing representative marker genes across cell populations. Spot size is proportional to the proportion of cells expressing a specific gene. Color intensity corresponds to the relative expression of a specific gene.
e. Bar graph showing cell type abundance in different groups of samples, as measured by scRNA-seq data from this study or deconvolved bulk RNA-seq data from Yang et al.
f. Bar chart showing heterogeneity of cell types in different patients based on Jensen-Shannon divergence (JSD) score.
g. UMAP shows the distribution of major cell types (top panel) and the number of differentially expressed genes (DEGs) in each cell type.
2. Transcriptional characteristics and CNV heterogeneity of duct cells
a. UMAPs showing the distribution of duct cells expressing the indicated maker genes (top left), duct cell subtypes (n = 6; top right), percentage of cells from different sample groups (bottom left) or percentage of cells with different CNV levels (bottom right). ).
b. Heat map showing expression of marker genes in 6 duct cell subtypes.
c. Classify ductal cells into different categories based on CNV score. The game plot shows the distribution of CNV scores across different samples.
d. Infer CNVs in P3 patients through scRNA-seq and whole exome sequencing (WES) data.
e. The percentage of cells in HM samples is positively correlated with the proportion of cells with high levels of CNVs.
f. Heat map shows the proportional expression levels of differentially expressed genes between cells with different CNV levels.
g. Heat map showing functional pathways activated in cells with different CNV levels using GSVA analysis.
h. COX regression analysis of relative cell abundance (estimated by CIBERSORTx) and patient survival using the TCGA PDAC cohort (n = 178).
i. Kaplan-Meier curves of TCGA PDAC patients (n = 178) showing survival of cell abundance groupings in ductal cell clusters 2 and 4.
3. Pseudo-time trajectory analysis reveals the diversity of duct cell differentiation states
a. Violin plot showing expression of representative genes associated with PDAC proliferation or malignancy in ductal cell subtypes.
b. Unsupervised pseudo-time trajectories of ductal cell subtypes visualized by RNA velocity. The direction of the arrow indicates the pseudotemporal differentiation trend of cells.
c. Semi-supervised pseudo-time trajectory of inferred duct cell subtypes by Monocle2.
d. Box plot showing latency of patient P1-P3 ductal cell subtypes by RNA velocity. The left side of the boxplot shows the number of cells in each category.
e. Heat map showing the proportional expression of differentially expressed genes in (c) in pseudo-time trajectories.
f. The distribution of ductal cells in patients P1-P3 along the monocle2 estimated trajectory.
g. RNA velocity analysis of ductal cells in each patient (P1-P3).
h. Venn diagram showing overlap of deg in high-level CNV, cell state S5, and HM duct cells.
i. Kaplan-Meier curve of TCGA PDAC cohort patients (n = 178). p-values were calculated using a two-sided logrank test.
j. Immunohistochemical analysis (left) LITAF expression in PT group and HM group.
4. Transcriptional profiling of fibroblasts in the tumor microenvironment of primary and metastatic PDAC tissues
a. UMAP shows subtypes of cancer-associated fibroblasts (CAFs), including iCAF, apCAF, and myCAF, colored by subcluster.
b. Violin plot (left) shows representative expression patterns of different subtypes of fibroblasts.
c. Distribution of CAFs on UMAP for different sample groups. Pie charts show the proportions of the three sample groups in each CAF subcluster.
d. Feature plot showing expression of selected cluster-specific genes. Cells with the highest expression levels are shown in red.
e. Venn diagram (below) showing the deg overlap between subclusters of caf and sample groups.
f. Immunofluorescence staining shows the colocalization of RGS5 (green), a-SMA (red), PanCK (purple) and DAPI (blue) in PT and HM samples.
g. Box plot showing CAF isoform latency by RNA velocity.
h. Use Monocle2 to observe semi-supervised pseudo-time trajectories of CAF subtypes.
i. Heatmap showing the proportional expression of differentially expressed genes over pseudo time starting from h.
j. Heat map showing enriched functional pathways in 5 cell states (S1-S5) by GSVA analysis.
5. Transcriptional panorama of myeloid cells in the tumor microenvironment of primary tumors and liver metastases
a. UMAP shows the subtypes of bone marrow cells, labeled by subtype.
b. Distribution of bone marrow cells in different sample groups on UMAP. Pie chart shows the proportion of the three sample groups in each cell subcluster.
c. Dot plot represents the average expression and frequency of representative marker genes in each myeloid cell subpopulation.
d. Feature plot showing expression of selected cluster-specific genes. Cells with the highest expression levels are shown in red.
e. Dot plot showing deg in neutrophils and lipid-associated macrophages (LAM) in three sample groups (left).
f. Immunofluorescence staining shows the colocalization of CD68 (green), CCL18 (red), PanCK (yellow) and DAPI (blue) in PT and HM samples.
g. Boxplot (top) showing the metabolic scores of metabolic pathways in four LAM subclusters (LAM1-LAM4).
h. Heatmap showing proportional expression levels of a range of immune checkpoint genes among myeloid cell subtypes. Subtypes are grouped by sample source and myeloid cell type annotation (DC, LAM, macrophages, monocytes, and neutrophils).
6. Transcriptional panorama of lymphoid cells in the tumor microenvironment of primary tumors and liver metastases
a. UMAP projection of subclustered lymphoid cells, labeled with different colors.
b. Feature plot showing expression of selected cluster-specific genes. Cells with the highest expression levels are shown in red.
c. Dot plot showing average expression and frequency of representative marker genes in lymphoid cell subsets.
d. Distribution of lymphoid cells on UMAP in different sample groups.
e. Box plot of proportions of CD4 + T, CD8 + T and NK cells in 3 sample groups.
f. Heat map showing expression of selected gene sets in each lymphoid cell subset, including naive, resident, cytotoxic, exhausted, costimulatory, transcription factors (TFs) and cell type.
g. Immunofluorescence staining shows colocalization of CD4 (green), CD8 (yellow), GNLY (red) and DAPI (blue) in PT and HM samples (n = 3).
h. Bar graph showing quantitative results for g, n = 3 paired PT and HM samples.
i. Heatmap showing proportional expression levels of a range of immune checkpoint genes across lymphoid cell subtypes. Subtypes are grouped according to sample source and lymphocyte type annotation (CD4+T, CD8+T and NK cells). Genes are grouped by receptor or ligand, inhibitory or stimulatory state, and the expected major lineage cell type (lymphocytes and myeloid cells) known to express the gene.
j. Dot plot shows the expression levels of five checkpoint genes (CTLA4, TIGIT, ICOS, TNFRSF4 and TNFRSF9) in regulatory T cells in the three sample groups.
k. Heat map showing expression of selected gene sets in regulatory T cells from three sample groups, including cytotoxic, na?ve, transcription factor (TF), resident, cell type, exhaustion, and costimulation.
7. Dynamics of cell-cell interaction networks in the tumor microenvironment of primary and metastatic PDAC tissues
a. Heat map illustrating cell-cell interaction patterns in NT, PT, and HM samples.
b. Heat map showing the ligand-receptor interaction scores of 464 pairs of NT, PT and HM groups respectively.
c. Bar graph showing group-specific ligand-receptor pairs in three sample groups.
d. Heat map showing enrichment of sample-specific ligand-receptor pairs among cell subtypes.
e. Boxplots showing cell-cell interaction counts between TFH (CXCL13) and ductal cells and between Tregs (FOXP3) and ductal cells.
f. Immunofluorescence staining shows the colocalization of CD4 (green), FOXP3 (red), PanCK (yellow) and DAPI (blue) in PT and HM samples.
g. Dot plot showing expression and frequency of representative pro-inflammatory and cytotoxic mediator genes in NT, PT and HM samples.
Reference:
1. Zhang, S., Fang, W., Zhou, S. et al. Single cell transcriptomic analyzes implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis. Nat Commun 14, 5123 (2023). https://doi.org /10.1038/s41467-023-40727-7
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Single cell bioinformatics analysis tutorial
The Huanfeng Gene official account has launched single-cell bioinformatics analysis tutorials with online video tutorials. The current catalog of relevant tutorials is as follows:
Topic 6. Canopy of Clone Evolution
Topic 7. Clone Evolution – Cardelino
Topic 8. RobustClone of Clone Evolution
SCS [1] starts a single-cell journey today and tells the past and present of single-cell sequencing
SCS【2】Single cell transcriptome cellranger
SCS【3】Single cell transcriptome data GEO download and read
SCS【4】Visual analysis of single cell transcriptome data (Seurat 4.0)
SCS【5】Visual analysis of single cell transcriptome data (scater)
SCS【6】Automatic cell type annotation of single cell transcriptome (SingleR)
SCS【7】Single cell transcriptome trajectory analysis (Monocle 3) clustering, classifying and counting cells
SCS【8】Single cell transcriptome screening marker genes (Monocle 3)
SCS【9】Single cell transcriptome construction cell trajectory (Monocle 3)
SCS【10】Differential expression analysis of single cell transcriptome (Monocle 3)
SCS【11】Single cell ATAC-seq visual analysis (Cicero)
SCS【12】Single cell transcriptome assessment of differentiation potential of different single cell subpopulations (Cytotrace)
SCS [13] Single-cell transcriptome identifies cell response to “gene set” (AUCell)
SCS [14] Single Cell Regulation Network Inference and Clustering (SCENIC)
SCS【15】Cell Interaction: Cell Communication Database of Receptors-ligands and Their Interactions (CellPhoneDB)
SCS [16] Infer copy number changes (inferCNV) from tumor single-cell RNA-Seq data
SCS [17] Inferring CNVs and subclones of tumors from single-cell transcriptomes (copyKAT)
SCS [18] Cellular Interaction: Cellular Communication Database of Receptors-ligands and Their Interactions (iTALK)
SCS【19】Single cell automatic annotation of cell types (Symphony)
SCS [20] Single cell data estimates cell types in tissues (Music)
SCS【21】Single cell spatial transcriptome visualization (Seurat V5)
SCS【22】RNA velocity estimation of single cell transcriptome (Velocyto.R)
SCS【23】Single cell transcriptome data integration (Harmony)
SCS [24] Computational method for quantifying metabolism in single cell data (scMetabolism)
SCS【25】Single cell intercellular communication Part 1 Cell communication visualization (CellChat)
SCS【26】Single cell intercellular communication Part 2 Systematic analysis of communication network (CellChat)
SCS [27] Single-cell transcriptome identification marker gene (scran)
SCS [28] Single-cell transcriptome weighted gene co-expression network analysis (hdWGCNA)
SCS【29】Single cell gene enrichment analysis (singleseqgset)
SCS【30】Single cell spatial transcriptomics database (STOmics DB)
SCS [31] Reduce obstacles and accelerate single cell research database (Single Cell PORTAL)
SCS [32] infers single-cell eQTLs (eQTLsingle) based on scRNA-seq data
SCS [33] Fully automatic and ultra-fast cell type identification of single cell transcription (ScType)
SCS【34】Single cell/T cell/antibody immune library data analysis (immunarch)
SCS【35】Single cell transcriptome removal of double cells (DoubletFinder)
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In the future, the Huanfeng Gene official account will continuously launch a series of single cell bioinformatics analysis tutorials.
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