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: Cancers

Article Title: Heterogeneity of Circulating Tumor Cell Neoplastic Subpopulations Outlined by Single-Cell Transcriptomics

doi: 10.3390/cancers13194885

Figure Lengend Snippet: Flowchart depicting the strategy for selection and interrogation of Lin−/Lin+ cell populations from mBC patients. Patient blood samples were collected into sodium-EDTA tubes, and each sample was sub-divided. ( a ) One portion of the blood sample was processed for isolating Lin−/Lin+ cell populations via FACs, followed by RNA Sequencing or scRNA-Sequencing. See “ ” for details. ( b ) A 7.5 mL portion of the blood sample was transferred to an AccuCyte collection tube for RareCyte analysis.

Article Snippet: We addressed these issues by devising an experimental strategy for capturing all CTCs, then performing not only standard RNA Sequencing (RNA-Seq), but also, notably, comprehensive and unbiased single-cell RNA sequencing analyses (scRNA-Seq; 10x Genomics Chromium) on blood samples collected from mBC patients.

Techniques: Selection, RNA Sequencing, Sequencing

Journal: Cancers

Article Title: Heterogeneity of Circulating Tumor Cell Neoplastic Subpopulations Outlined by Single-Cell Transcriptomics

doi: 10.3390/cancers13194885

Figure Lengend Snippet: RNA-Seq of FACS Lin−/Lin+ cell populations. Blood was collected from normal human donors and 19 mBC patients. PBMCs were isolated and sorted as described, followed by RNA-Seq of collected cells. ( a ) A volcano graph showing genes with significant log2 fold change and −log10 ( p value) for the normal PBMCs (green dots) in comparison with the Lin+ population from 15 mBC patients (red dots). ( b ) Heat maps of gene expression showing significant differences between normal human PBMCs (left/ blue) and Lin+ cells (right/ yellow) from patients. Patient 8 provided two samples 3 months apart. ( c ) A volcano graph showing genes with significant log2 fold change and −log10 ( p value) for the Lin− populations (green dots) in comparison with the Lin+ population (red dots). ( d ) Heat maps of gene expression, indicating differences between the Lin+ (yellow) and Lin− (blue) populations from mBC patients. ( e – h ) Heat maps for patients grouped by hormone receptor status (shown above the map) ( e ) ER+/PR+/Her2+ ( f ) ER+/PR+/Her2− ( g ) ER+/PR−/Her2−, and ( h ) ER−/PR−/Her2+. Other subtypes had less than three patients in the overall set. All heat maps were done with unsupervised clustering.

Article Snippet: We addressed these issues by devising an experimental strategy for capturing all CTCs, then performing not only standard RNA Sequencing (RNA-Seq), but also, notably, comprehensive and unbiased single-cell RNA sequencing analyses (scRNA-Seq; 10x Genomics Chromium) on blood samples collected from mBC patients.

Techniques: RNA Sequencing, Isolation, Comparison, Gene Expression