Journal: Nature Communications

Article Title: Host-pathogen interactions in the Plasmodium -infected mouse liver at spatial and single-cell resolution

doi: 10.1038/s41467-024-51418-2

Figure Lengend Snippet: a Schematic representation of the experimental design of this study. Livers were collected at 12, 24, or 38 h post infection (hpi) with P. berghei parasites or salivary gland lysate of uninfected mosquitoes (SGC) (left). Immunofluorescence (IF) staining of the parasite, spatial transcriptomics (ST) or 10X visium spatial technology protocols, and droplet-based single-nuclei RNA sequencing (snRNA-seq) were performed (center). Both data were further analyzed computationally (right). IHS stands for immune hotspot. b After dimensionality reduction, the normalized and batch-corrected data were embedded in UMAP space and split by the original condition for visualization. Data from SGC sections are shown on the top from 12 to 38 hpi (left to right) and data from P. berghei - infected sections are shown on the bottom from 12 to 38 hpi (left to right). Clusters with an obvious association to infection conditions are highlighted with gray boxes. c For identified clusters ST10 and ST11, differential gene expression analysis (DGEA) was performed, followed by functional enrichment analysis for each cluster (see Methods for details). Overrepresented pathways of the KEGG database for ST10 are shown in rose and for ST11 in aquamarine. Scales for expression values of overrepresented genes belonging to the individual KEGG pathways are shown for ST11 (left) or ST10 (right), from high expression (dark) to lower expression (light). Selected gene names are shown at the bottom. Enrichment scores for the pathways are shown on the right. d Interaction analysis of clusters was performed to evaluate spatial enrichment expression programs as suggested by clustering analysis in space. Positive enrichment values (orange) indicate spots belonging to these clusters are more likely to be neighboring, while negative enrichment values (blue) indicate spots associated with these expression programs are less likely to be neighboring. Clusters without significant enrichment in each other’s neighborhoods are shown in white. e Visium clusters were imposed on spatial positions and annotated according to spatial expression features. Sections of the investigated conditions are divided for ease of inspection as in ( b ), with the top panel comprising SGC sections across 12–38 hpi and the bottom panel comprising P. berghei infected sections across 12–38 hpi.

Article Snippet: In this study, we use a combination of the original Spatial Transcriptomics 2K arrays , (henceforth referred to as ST) and Visium arrays (10X Genomics Inc.) .

Techniques: Infection, Immunofluorescence, Staining, RNA Sequencing, Gene Expression, Functional Assay, Expressing

Journal: Cellular Oncology (Dordrecht, Netherlands)

Article Title: Integrating multi-modal transcriptomics identifies cellular subtypes with distinct roles in PDAC progression

doi: 10.1007/s13402-025-01100-6

Figure Lengend Snippet: Workflow and overview of a multi-data atlas for human PDAC. ( A ). Graphic overview of the study design. Single-cell RNA sequencing (scRNA-seq) data related to pancreatic ductal adenocarcinoma (PDAC), along with spatial transcriptomics (ST), bulk RNA sequencing (BulkRNA-seq), and clinical information, were obtained from the GEO, GSA, TCGA, ICGC, HTAN and 10X Genomics databases for bioinformatics analysis. The identified biological markers and targets were further validated via multiplex immunofluorescence staining and Xenium high-resolution spatial transcriptomics. ( B ) UMAP plots of 187,520 cells from 88 PDAC patient samples, showing nine major cell clusters. Each cluster is represented in a different color. ( C ) UMAP density plots of the distribution of cells from normal pancreas, adjacent tissues, liver metastasis, and primary PDAC sites. ( D ) UMAP plots showing the expression levels of selected known marker genes. ( E ) Bar plots displaying the proportions of nine cell types across the four different sites. Statistics based on the Wilcoxon test. p < 0.05 was considered a statistically significant difference, p < 0.01**, p < 0.001***, p < 0.0001 ****

Article Snippet: We present an integrated transcriptomic atlas of the PDAC TME by combining single-cell RNA sequencing ( n = 88; 187,520 cells), Visium spatial transcriptomics ( n = 20; 67,933 spots), bulk RNA sequencing ( n = 1,383), and high-resolution Xenium spatial transcriptomics ( n = 2; 307,679 cells).

Techniques: RNA Sequencing, Multiplex Assay, Immunofluorescence, Staining, Expressing, Marker

Journal: Cellular Oncology (Dordrecht, Netherlands)

Article Title: Integrating multi-modal transcriptomics identifies cellular subtypes with distinct roles in PDAC progression

doi: 10.1007/s13402-025-01100-6

Figure Lengend Snippet: Characterization of POSTN + fibroblasts and SPP1 + macrophages and their associations in PDAC TME. ( A ). UMAP plots displaying the composition of fibroblasts colored by sub cluster. Red dashed circles highlight POSTN + fibroblasts. ( B ). The percentage of each fibroblast subclusters across different tissue sites in scRNA-seq. ( C ). UMAP plots displaying the composition of macrophages/monocytes, colored by subtype. Red dashed circles highlight SPP1 + macrophages. ( D ). The percentage of each Macro/Mono subclusters in different tissue sites in scRNA-seq. ( E ). A dot plot illustrates the Spearman correlation coefficients (R) and their corresponding p-values between POSTN + fibroblasts and SPP1 + macrophages across 12 independent cohorts. ( F ) to ( G ). Kaplan-Meier curves showing that patients with higher infiltration of POSTN + fibroblasts ( F ), and SPP1 + macrophages ( G ) are associated with worse outcomes. ( H ). Spatial plots of POSTN + fibroblasts (left) and SPP1 + macrophages (middle) identified using BayesSpace, alongside scatter plots showing the spatial correlation between POSTN + fibroblasts and SPP1 + macrophages in spatial transcriptomics (ST). ( I ). GSEA of 10 hallmark pathways between POSTN/SPP1 high and POSTN/SPP1 low groups in TCGA-PAAD cohort, with genes ranked by fold change in expression between the two groups. NES, normalized enrichment score

Article Snippet: We present an integrated transcriptomic atlas of the PDAC TME by combining single-cell RNA sequencing ( n = 88; 187,520 cells), Visium spatial transcriptomics ( n = 20; 67,933 spots), bulk RNA sequencing ( n = 1,383), and high-resolution Xenium spatial transcriptomics ( n = 2; 307,679 cells).

Techniques: Expressing

Journal: Cellular Oncology (Dordrecht, Netherlands)

Article Title: Integrating multi-modal transcriptomics identifies cellular subtypes with distinct roles in PDAC progression

doi: 10.1007/s13402-025-01100-6

Figure Lengend Snippet: Decoding the Immune Signature of CCL4 + CD8 + effector T and IGHG1 + IgG Plasma cells in PDAC TME. ( A ). UMAP plots showing the composition of T/NK colored by cell subtype. ( B ). The percentage of each T or NK cell subtypes in different tissue sites in the scRNA-seq data. ( C ). UMAP plots displaying the composition of B/Plasma cells colored by subtype. ( D ). The percentage of each B or Plasma cell subtypes across different tissue sites in scRNA-seq data. ( E ). A dot plot illustrates the Spearman correlation coefficients (R) and their corresponding p-values between CCL4 + CD8 + T cells and IGHG1 + plasma cells across 11 independent cohorts. ( F ) to ( G ). The Kaplan-Meier curves show that patients with higher infiltration of CCL4 + CD8 + T EFF ( F ), and IGHG1 + IgG Plasma ( G ) are associated with better outcomes. ( H ). Spatial plots of CCL4 + CD8 + T EFF (left) and IGHG1 + IgG Plasma (mid) using BayesSpace, alongside scatter plots of the spatial correlation between CCL4 + CD8 + T EFF and IGHG1 + IgG Plasma in spatial transcriptomics (ST). ( I ). GSEA of 10 GO BP terms comparing the CCL4 & IGHG1 high and CCL4 & IGHG1 low groups in the TCGA-PAAD cohort. Genes are ranked by fold change in expression between these two groups. NES, normalized enrichment score. BP, biological process

Article Snippet: We present an integrated transcriptomic atlas of the PDAC TME by combining single-cell RNA sequencing ( n = 88; 187,520 cells), Visium spatial transcriptomics ( n = 20; 67,933 spots), bulk RNA sequencing ( n = 1,383), and high-resolution Xenium spatial transcriptomics ( n = 2; 307,679 cells).

Techniques: Clinical Proteomics, Expressing

Journal: Communications Biology

Article Title: Single-cell sequencing uncovers disrupted stromal-macrophage communication as a driver of intrauterine adhesion progression

doi: 10.1038/s42003-025-08634-3

Figure Lengend Snippet: a Haematoxylin and eosin staining of the slides in the Visium arrays from Alonso et al. Two individuals were selected: proliferative phase A13 and secretory phase A30. b , c Estimated amount of mRNA (color intensity) contributed by each stromal cell population ( b ), macrophage subsets ( c ) to each spot (color) shown over the H&E image of proliferative (A13, 152810 slide and 152806 slide) and secretory (A30, 152811 slide and152807 slide) endometrium.

Article Snippet: Fig. 9 Spatial transcriptomics reveals regional distribution of stromal and macrophage subpopulations in human endometrium. a Haematoxylin and eosin staining of the slides in the Visium arrays from Alonso et al. Two individuals were selected: proliferative phase A13 and secretory phase A30. b , c Estimated amount of mRNA (color intensity) contributed by each stromal cell population ( b ), macrophage subsets ( c ) to each spot (color) shown over the H&E image of proliferative (A13, 152810 slide and 152806 slide) and secretory (A30, 152811 slide and152807 slide) endometrium.

Techniques: Staining