Journal: Nature Communications

Article Title: Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis

doi: 10.1038/s41467-023-40727-7

Figure Lengend Snippet: a Workflow of sample collection and data analysis in this study. b Boxplots showing the scaled mean expression of inflammation signatures ( n = 42) in cells from different sample groups. The boxes indicate the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). The points indicate individual signatures. Similar patterns were observed in the PDAC scRNA-seq dataset from Peng et al. . c Uniform Manifold Approximation and Projection (UMAP) plot displaying the integrated cell map, which consists of 29 cell clusters from 12 annotated cell types. Cells are colored by clusters. d Dot plot showing representative marker genes across cell clusters. Dot size is proportional to the fraction of cells expressing specific genes. Color intensity corresponds to the relative expression of specific genes. e Bar plot showing the cell type abundance for samples from different groups, as measured by scRNA-seq data in this study or deconvoluted bulk RNA-seq data from Yang et al. . The error bar indicates standard error of the mean (s.e.m.). The p values are calculated using two-sided Wilcoxon rank-sum test. * p < 0.05; ** p < 0.01. The boxes indicate the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). f Bar plot displaying the heterogenicity of cell types among different patients based on Jensen-Shannon divergence (JSD) score. g UMAP showing the distribution of major cell types (above) and the number of differentially expressed genes (DEGs) in each cell type.

Article Snippet: The 10x Chromium single-cell RNA sequencing (scRNA-seq) data were processed using CellRanger (v3.1.0; 10x Genomics) for alignment, barcode assignment and unique molecular identifier (UMI) counting (using the genome reference set GRCh38-3.0.0).

Techniques: Expressing, Marker, RNA Sequencing

Journal: Nature Communications

Article Title: Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis

doi: 10.1038/s41467-023-40727-7

Figure Lengend Snippet: a UMAP showing the subtypes of myeloid cells, colored by subtypes. b Distribution of myeloid cells in different sample groups on the UMAP. Pie chart showing the proportion of three sample groups in each cell subcluster. c Dot plot illustrating the average expression and frequency of representative marker genes in each myeloid cell subcluster. d Feature plots showing the expression of selected cluster-specific genes. Cells with the highest expression level are colored red. e Dot plot illustrating DEGs in neutrophils and Lipid-associated macrophages (LAMs) from three sample groups (left). Boxplots showing the expression patterns of S100A8 , CXCL8 , SPP 1 , and APOC1 using the bulk RNA-seq dataset from Yang et al. . The number of samples in each group is in the legend. The boxes showing the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). f Immunofluorescent staining showing co-localization of CD68 (green), CCL18 (red), PanCK (yellow), and DAPI (blue) in PT and HM samples. Scale bars, 50 μm (left) and 20 μm (right). The bar plots show the quantification results, n = 3 patients with paired PT and HM samples. The error bar indicates standard error of the mean (s.e.m.). The p value is calculated with one-sided Wilcoxon rank-sum test. g Boxplot (top) showing the metabolic score of metabolic pathways in four LAM subclusters (LAM1-LAM4). The points indicate individual pathways ( n = 76). Dot plot (bottom) showing the metabolic activity analysis of all LAM subclusters by scMetabolism. The circle size and color darkness both represent the scaled metabolic score. The number of pathways in each category is indicated below the boxplot. The boxes showing the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). h Heatmap showing the scaled expression levels of a series of immune checkpoint genes in myeloid cell subtypes. Subtypes are grouped by sample source and myeloid cell type annotations (DC, LAM, macrophage, monocyte and neutrophil). Genes are grouped as receptor or ligand, inhibitory or stimulatory status and expected major lineage cell types known to express the gene (lymphocyte and myeloid).

Article Snippet: The 10x Chromium single-cell RNA sequencing (scRNA-seq) data were processed using CellRanger (v3.1.0; 10x Genomics) for alignment, barcode assignment and unique molecular identifier (UMI) counting (using the genome reference set GRCh38-3.0.0).

Techniques: Expressing, Marker, RNA Sequencing, Staining, Activity Assay

Journal: EMBO Reports

Article Title: TMPRSS11B promotes an acidified microenvironment and immune suppression in squamous lung cancer

doi: 10.1038/s44319-025-00631-1

Figure Lengend Snippet: ( A ) RNA sequencing analysis of tumors from various mouse models of lung cancer. The y-axis represents the normalized counts for Tmprss11b . One-way ANOVA with Dunnett’s multiple comparisons test was used for the statistical analysis (RPR2 mice n = 5; RPM mice n = 15; SNL mice n = 4; LP mice n = 6; SL mice n = 9; KP mice n = 8, biological replicates), **** P < 0.0001 (RPR2, RPM, KP), * P = 0.0133 (LP). Plot represents mean ± SD. ( B ) Schematic representation of Ad-Cre mediated tumor induction in SNL mice. Figure created in BioRender. ( C ) Representative MRI images of the mice in ( B ), 3- & 4-months post infection. Red outlines denote tumors (Biological replicates n > 3). ( D ) Representative H&E images of SNL mouse lung, 7 months post infection with Ad-Cre showing distinct regions of LUSC and mucinous LUAD (Biological replicates n > 3). Scale bar, 100 μm. ( E ) H&E image of SNL mouse lung (11 months post infection with Ad-Cre) and RNAscope of Tmprss11b on a serial section. Left, red outline denotes squamous tumors based on H&E staining. Right, yellow outline denotes regions with Tmprss11b expression (red) corresponding to the regions of squamous tumors. The staining was repeated three times with serial sections (technical replicates) and with lung sections from different mice ( n = 4, biological replicates). Scale bar, 2 mm. ( F ) Zoom-in of ( E ) showing Tmprss11b expression by RNAScope in squamous tumors (top panel) and normal lung (bottom panel). Scale bar, 200 μm. ( G ) Representative H&E image with annotations and RNAscope analysis of Tmprss11b (red) and Sox2 (green) in SNL lung sections. Scale bar, 400 μm. .

Article Snippet: We obtained reference single-cell RNA sequencing (scRNA-seq) data from The Tabla Muris Consortium ( Nature 2018) (Schaum et al, ) and spatial transcriptomics (ST) data from relevant datasets.

Techniques: RNA Sequencing, Infection, RNAscope, Staining, Expressing

Journal: EMBO Reports

Article Title: TMPRSS11B promotes an acidified microenvironment and immune suppression in squamous lung cancer

doi: 10.1038/s44319-025-00631-1

Figure Lengend Snippet: ( A ) Top downregulated Keratin genes from differential gene expression analysis of control shRNA versus Tmprss11b shRNA bulk RNA sequencing from the KLN205 syngeneic experiment in Fig. . The log2FC change depicts the reduction in expression of the indicated genes in the Tmprss11b knockdown tumors compared to the control. ( B ) Top Keratin genes from the differential gene expression (DEG) analysis of the Tmprss11b -high versus low in LUSC spatial data from SNL lung tumors. ( C ) Top keratin genes from the differential gene expression (DEG) analysis of the Tmprss11b -high LUSC versus LUAD spatial data from SNL lung tumors. ( D ) Top Keratin genes from the differential gene expression (DEG) analysis of TMPRSS11B -high versus low LUSC human tumors from TCGA. ( E ) Venn diagram depicting overlapping Keratin genes from the gene lists in ( A – D ).

Article Snippet: We obtained reference single-cell RNA sequencing (scRNA-seq) data from The Tabla Muris Consortium ( Nature 2018) (Schaum et al, ) and spatial transcriptomics (ST) data from relevant datasets.

Techniques: Gene Expression, Control, shRNA, RNA Sequencing, Expressing, Knockdown