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10X Genomics single cell transcriptomes methods

a , Optic lobe cross-section , with drawings of unicolumnar (orange shades) and multicolumnar (blue) neurons. Dashed lines: boundaries between layers. A: anterior, L: lateral, M: medial, P: posterior. b, Approach followed to produce the adult dataset. c, Pearson correlation between the average gene expression of the adult dataset clusters (x-axis) and the <t>transcriptome</t> of isolated Lawf1 neurons (Methods). d, tSNE visualization of the final adult dataset, using 120 principal components calculated on the log-normalized integrated gene expression. The 61 identified neuronal clusters are labeled by their standard abbreviation, G1–16: glial clusters, LQ: low-quality cells, G/LQ1–4: glial clusters with some features of low-quality cells, *: clusters with less confident annotations . e, Approximate time frames of different steps of optic lobe development, and tSNE visualizations of the pupal datasets. Colors match to the adult dataset as classified by the neural network. f, Multi-task neural network classifier used at each stage to sequentially match developing cells to the adult clusters, as detailed in .

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1) Product Images from "Neuronal diversity and convergence in a visual system developmental atlas"

Article Title: Neuronal diversity and convergence in a visual system developmental atlas

Journal: Nature

doi: 10.1038/s41586-020-2879-3

Figure Legend Snippet: a , Optic lobe cross-section , with drawings of unicolumnar (orange shades) and multicolumnar (blue) neurons. Dashed lines: boundaries between layers. A: anterior, L: lateral, M: medial, P: posterior. b, Approach followed to produce the adult dataset. c, Pearson correlation between the average gene expression of the adult dataset clusters (x-axis) and the transcriptome of isolated Lawf1 neurons (Methods). d, tSNE visualization of the final adult dataset, using 120 principal components calculated on the log-normalized integrated gene expression. The 61 identified neuronal clusters are labeled by their standard abbreviation, G1–16: glial clusters, LQ: low-quality cells, G/LQ1–4: glial clusters with some features of low-quality cells, *: clusters with less confident annotations . e, Approximate time frames of different steps of optic lobe development, and tSNE visualizations of the pupal datasets. Colors match to the adult dataset as classified by the neural network. f, Multi-task neural network classifier used at each stage to sequentially match developing cells to the adult clusters, as detailed in .

Techniques Used: Gene Expression, Isolation, Labeling

a. The proportions of UMIs from mitochondrial genes per cell (n = number of cells in each library, indicated on the right) and the total number of cells passing filters in each of the 15 libraries comprising the adult dataset. Names indicated correspond to the names in the Seurat object provided (Adult.rds, GSE142787). Boxplots display the first, second and third quartiles. Whiskers extend from the box to the highest or lowest values in the 1.5 inter-quartile range, and outlying datapoints are represented by a dot. b, Origin of the cells in the final adult clusters, colored as in (a). Green arrows: clusters whose unique library distribution can be explained by variable contamination from surrounding tissues (cluster 3 is photoreceptors, 112 is likely Kenyon Cells from the central brain) or the number of lamina neuropils dissociated (clusters 107, 108, 109 are lamina neurons). Red arrows: clusters likely enriched in low quality transcriptomes, as they are enriched in cells from libraries with high number of mitochondrial genes (38, 120, 192) or high number of cells sequenced (102, likely corresponding to multiplets). Brackets: Glial clusters, some of them enriched in libraries with high number of mitochondrial genes as ambient RNA is more similar to RNA from glial vs . neuronal cells . c, Number of clusters obtained with different pairs of clustering parameters. Red rectangle: pair of parameters used. d, Left: Legend as in . Right: Number of isolated neuronal type transcriptomes matching to 1–5 of our adult clusters, for each pair of parameters in (c), which we used as a measure of the biological relevance of our clusters. Matching was defined by the presence of a correlation gap above 0.05 (Methods). We took into account any correlation gap between the 6 best correlated clusters, since similar cell types or overclustering can affect the size of the first correlation gap as illustrated on the left graphs. Red rectangle: pair of parameters used. e, tSNE visualization of the adult optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cell colors indicate the cluster they belonged to before we merged artificially split clusters (red circles, Methods). f, Heatmap showing scaled log-normalized non-integrated expression of the top20 cluster markers between the merged clusters. Merged clusters had almost indistinguishable gene expression patterns, but often differed by their proportions of UMI from mitochondrial genes per cell or the expression levels of the genes highlighted in red, which are enriched in the “ambient RNA cluster” 192 (see also ).

Figure Legend Snippet: a. The proportions of UMIs from mitochondrial genes per cell (n = number of cells in each library, indicated on the right) and the total number of cells passing filters in each of the 15 libraries comprising the adult dataset. Names indicated correspond to the names in the Seurat object provided (Adult.rds, GSE142787). Boxplots display the first, second and third quartiles. Whiskers extend from the box to the highest or lowest values in the 1.5 inter-quartile range, and outlying datapoints are represented by a dot. b, Origin of the cells in the final adult clusters, colored as in (a). Green arrows: clusters whose unique library distribution can be explained by variable contamination from surrounding tissues (cluster 3 is photoreceptors, 112 is likely Kenyon Cells from the central brain) or the number of lamina neuropils dissociated (clusters 107, 108, 109 are lamina neurons). Red arrows: clusters likely enriched in low quality transcriptomes, as they are enriched in cells from libraries with high number of mitochondrial genes (38, 120, 192) or high number of cells sequenced (102, likely corresponding to multiplets). Brackets: Glial clusters, some of them enriched in libraries with high number of mitochondrial genes as ambient RNA is more similar to RNA from glial vs . neuronal cells . c, Number of clusters obtained with different pairs of clustering parameters. Red rectangle: pair of parameters used. d, Left: Legend as in . Right: Number of isolated neuronal type transcriptomes matching to 1–5 of our adult clusters, for each pair of parameters in (c), which we used as a measure of the biological relevance of our clusters. Matching was defined by the presence of a correlation gap above 0.05 (Methods). We took into account any correlation gap between the 6 best correlated clusters, since similar cell types or overclustering can affect the size of the first correlation gap as illustrated on the left graphs. Red rectangle: pair of parameters used. e, tSNE visualization of the adult optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cell colors indicate the cluster they belonged to before we merged artificially split clusters (red circles, Methods). f, Heatmap showing scaled log-normalized non-integrated expression of the top20 cluster markers between the merged clusters. Merged clusters had almost indistinguishable gene expression patterns, but often differed by their proportions of UMI from mitochondrial genes per cell or the expression levels of the genes highlighted in red, which are enriched in the “ambient RNA cluster” 192 (see also ).

Techniques Used: Isolation, Gene Expression, Expressing

a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated Repo+ (glial marker) or Elav+ (neuronal marker) populations. LQ = clusters containing a proportion of cells with features of lower quality transcriptomes. b, Violin plots of features tending to be higher (proportions of UMI from mitochondrial genes) or lower (number of UMIs or genes per cell) in low quality cells , . c, Heatmap showing the scaled log-normalized non-integrated expression of the top5 cluster markers of the adult dataset. The first 5 neuronal adult clusters (1 to 6, cluster 1 and 2 having been merged) are plotted for reference as they clearly have specific gene expression patterns. Clusters 38, 85, 102 and 120 present much less defined gene expression patterns and likely contain low quality neuronal transcriptomes (see also ). Clusters 188 and 189 could be further separated in two groups with different gene expression patterns, as illustrated by the dashed line in the insert. Cluster 191 expresses several markers found in no other clusters and likely correspond to neither glia nor optic-lobe neuron. Cluster 192 expresses mainly low levels of glia-specific genes, without specific markers. It likely corresponds to ambient RNA, which would be enriched in RNA from burst glial cells.

Figure Legend Snippet: a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated Repo+ (glial marker) or Elav+ (neuronal marker) populations. LQ = clusters containing a proportion of cells with features of lower quality transcriptomes. b, Violin plots of features tending to be higher (proportions of UMI from mitochondrial genes) or lower (number of UMIs or genes per cell) in low quality cells , . c, Heatmap showing the scaled log-normalized non-integrated expression of the top5 cluster markers of the adult dataset. The first 5 neuronal adult clusters (1 to 6, cluster 1 and 2 having been merged) are plotted for reference as they clearly have specific gene expression patterns. Clusters 38, 85, 102 and 120 present much less defined gene expression patterns and likely contain low quality neuronal transcriptomes (see also ). Clusters 188 and 189 could be further separated in two groups with different gene expression patterns, as illustrated by the dashed line in the insert. Cluster 191 expresses several markers found in no other clusters and likely correspond to neither glia nor optic-lobe neuron. Cluster 192 expresses mainly low levels of glia-specific genes, without specific markers. It likely corresponds to ambient RNA, which would be enriched in RNA from burst glial cells.

Techniques Used: Expressing, Isolation, Marker, Gene Expression

Figure Legend Snippet: a, Pearson correlation between the average log-normalized non-integrated expression of the top10 cluster markers of the adult dataset clusters (x-axis) and the transcriptome of isolated neurons , . We represented Dm3, Tm9, T4 and T5 before their split into Dm3a/b, Tm9v/d, T4/T5ab and T4/T5cd. When two transcriptomes were published for a given neuronal type, the one presenting the highest correlation gap is displayed in this figure. R1–8: average gene expression of all photoreceptors . KC: Kenyon Cells, cluster 112 therefore likely corresponds to contamination from the central brain. b, Legend as in (a). We indicated several matching clusters to highlight the high similarity between LC cells transcriptomes, which explains the lower correlation gaps observed for these neurons. c, Left: Legend as in (a). Right: mixture modelling of Pm3 markers (y axis). Clusters are spread on the x-axis, with the probability of expression of the markers figured by the size of the black dots.

Techniques Used: Expressing, Isolation, Gene Expression

a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells colors indicate the clusters they belonged to according to unsupervised clustering (a), or the adult clusters they were classified as by the neural network (b, same as in ). Black circles indicate high granularity regions, where less frequent cell types were grouped together by unsupervised clustering but could be resolved accurately by the neural network (b). c, Same as in (a-b) but cells are named and colored by the adult cluster they were classified as by Seurat label transfer (Methods). d, tSNE visualizations (same as c) including only the cells that were assigned inconsistent identities by Seurat and the neural network. Highest rates of inconsistencies were observed in the center (LQ cells), in L1 and L2 clusters (red ellipses), in most glia clusters (green ellipses), the TE neurons and a glia-like cluster (identity 214, ) with no adult correspondence (blue ellipses). e-f, tSNE visualizations of 56,902 cells sequenced from whole fly brains , using 120 principal components calculated on the log-normalized gene expression. e, Cells are named and colored by the clusters they were classified as by our neural network. f, Cells are named by the cluster identities from the original study and colored by the confidence score they received from our neural network. Black circles mark the following central brain clusters (from left to right): Poxn, OPN, clock neurons and dopaminergic neurons, that all received low scores from the neural network. Kenyon cells (red circles) were assigned with high confidence as our adult dataset was contaminated by them (cluster 112).

Figure Legend Snippet: a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells colors indicate the clusters they belonged to according to unsupervised clustering (a), or the adult clusters they were classified as by the neural network (b, same as in ). Black circles indicate high granularity regions, where less frequent cell types were grouped together by unsupervised clustering but could be resolved accurately by the neural network (b). c, Same as in (a-b) but cells are named and colored by the adult cluster they were classified as by Seurat label transfer (Methods). d, tSNE visualizations (same as c) including only the cells that were assigned inconsistent identities by Seurat and the neural network. Highest rates of inconsistencies were observed in the center (LQ cells), in L1 and L2 clusters (red ellipses), in most glia clusters (green ellipses), the TE neurons and a glia-like cluster (identity 214, ) with no adult correspondence (blue ellipses). e-f, tSNE visualizations of 56,902 cells sequenced from whole fly brains , using 120 principal components calculated on the log-normalized gene expression. e, Cells are named and colored by the clusters they were classified as by our neural network. f, Cells are named by the cluster identities from the original study and colored by the confidence score they received from our neural network. Black circles mark the following central brain clusters (from left to right): Poxn, OPN, clock neurons and dopaminergic neurons, that all received low scores from the neural network. Kenyon cells (red circles) were assigned with high confidence as our adult dataset was contaminated by them (cluster 112).

Techniques Used: Gene Expression

tSNE visualizations of all optic lobe single-cell transcriptomes acquired for this study, using 120 principal components calculated on the log-normalized integrated gene expression. The cells are named and colored consistently at all stages by the neural network classifications with manual adjustments as detailed in . Blue ellipses: Dm3 and Tm9 neuronal subtypes, which could only be resolved at P50 and earlier.

Figure Legend Snippet: tSNE visualizations of all optic lobe single-cell transcriptomes acquired for this study, using 120 principal components calculated on the log-normalized integrated gene expression. The cells are named and colored consistently at all stages by the neural network classifications with manual adjustments as detailed in . Blue ellipses: Dm3 and Tm9 neuronal subtypes, which could only be resolved at P50 and earlier.

Techniques Used: Gene Expression

Figure Legend Snippet: a-b, tSNE visualization of the P70 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network (NN) or by (b) the log-normalized non-integrated expression of Fs (green), dimm (blue), and skl (red), which are co-expressed in TE neurons (red ellipses). c , Violin plot of log-normalized non-integrated prt expression in all clusters at P50. TE neuron clusters are indicated by circle. d , R10D10-Gal4 co-expression with anti-Prt staining in a P50 optic lobe (n=15 neurons). Scale bar: 10 μm. e , FLEXAMP memory cassette labeling of R10D10-Gal4 in an adult optic lobe (n=28 brains) with anti-NCad staining. Scale bar: 30 μm. f, R10D10-Gal4 expression pattern in L3 optic lobe (n=15 brains), with anti-NCad, anti-Bsh and anti-Hth staining. Arrow: Bsh + , Hth - neurons labeled by R10D10-Gal4 . Scale bar: 30 μm. g-h, R10D10-Gal4 sparse expression at P30 (n=40 neurons), with anti-NCad, anti-Bsh and anti-Hth staining. Scale bars = 5 μm (g) and 15 μm (h). d/pMe: distal/proximal Medulla, Lo: Lobula, Lp: Lobula plate. I , Co-labeling of R10D10-LexA expression and bsh-Gal4 FLEXAMP memory cassette with anti-nCad staining in a P50 optic lobe (n=13 brains). Dashed ellipses: TE neurons. Scale bar: 20 μm.

Techniques Used: Gene Expression, Expressing, Staining, Labeling

a-b, tSNE visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network or by (b) the log-normalized non-integrated expression of dpn (green), ase (blue), and grim (red). Circles match to those of . c, UMAP visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are colored by the log-normalized non-integrated expression of nerfin-1 (green), Hey (blue), and vfl (red) d, UMAP visualization of Tm3 and T1 cells (above and below the dashed line, respectively) from all stages sequenced in this study, using 25 principal components calculated on the log-normalized non-integrated gene expression. Cells are colored by their developmental stage. e, Ventral and dorsal Transient Extrinsic (TE) neurons as well as transient photoreceptors (PRs) line the edges of all optic lobe neuropils and express Follistatin ( Fs ). Moreover, TE and at least 3 other neuronal types express Wnt4 in the ventral medulla/lobula but express Wnt10 in the dorsal part of these neuropils. f, The transcriptome of neurons from the same neuronal type but produced days apart converge towards a similar transcriptomic state, which they reach by P30. Moreover, the inter-neuronal type transcriptomic diversity is highest during P40-P70.

Figure Legend Snippet: a-b, tSNE visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are named by the unsupervised cluster they were assigned to and colored by (a) the confidence score they received from the neural network or by (b) the log-normalized non-integrated expression of dpn (green), ase (blue), and grim (red). Circles match to those of . c, UMAP visualization of the P15 optic lobe single-cell transcriptomes, using 120 principal components calculated on the log-normalized integrated gene expression. Cells are colored by the log-normalized non-integrated expression of nerfin-1 (green), Hey (blue), and vfl (red) d, UMAP visualization of Tm3 and T1 cells (above and below the dashed line, respectively) from all stages sequenced in this study, using 25 principal components calculated on the log-normalized non-integrated gene expression. Cells are colored by their developmental stage. e, Ventral and dorsal Transient Extrinsic (TE) neurons as well as transient photoreceptors (PRs) line the edges of all optic lobe neuropils and express Follistatin ( Fs ). Moreover, TE and at least 3 other neuronal types express Wnt4 in the ventral medulla/lobula but express Wnt10 in the dorsal part of these neuropils. f, The transcriptome of neurons from the same neuronal type but produced days apart converge towards a similar transcriptomic state, which they reach by P30. Moreover, the inter-neuronal type transcriptomic diversity is highest during P40-P70.

Techniques Used: Gene Expression, Expressing, Produced

Image Search Results

Single‐cell transcriptome landscape in aging cohort. (A) UMAP visualization of cell‐type‐specific annotation among the aging cohort, showing 9 cell groups in different colors. (B) UMAP visualization of immune cell subpopulation annotation across different age groups, displaying 21 subpopulations in different colors. (C) The proportion of 21 different cell types across age groups.

Journal: Aging Cell

Article Title: The Immune Cell Atlas of “Longevity Molecular Tag”: Identification of Principal Immune Cell Subsets and Their Underlying Molecular Regulatory Mechanisms

doi: 10.1111/acel.70431

Figure Lengend Snippet: Single‐cell transcriptome landscape in aging cohort. (A) UMAP visualization of cell‐type‐specific annotation among the aging cohort, showing 9 cell groups in different colors. (B) UMAP visualization of immune cell subpopulation annotation across different age groups, displaying 21 subpopulations in different colors. (C) The proportion of 21 different cell types across age groups.

Article Snippet: Single‐cell transcriptomic data from 56 healthy individuals aged from birth to over 90 years were acquired from the Shanghai Pudong Cohort ( NCT05206643 ) (Synapse: syn61609846) (Wang, Li, et al. ).

Techniques: Single Cell

$Centenarian phenotype‐associated immune cell type analysis at single‐cell resolution. (A) UMAP visualization of cell‐type‐specific annotation among immune cells, showing 9 cell groups in different colors. (B) UMAP visualization of subcellular annotation among immune cell subpopulations, showing 21 subpopulations in different colors. (C) UMAP visualization of Scissor + and Scissor − cells. (D, E) Proportional fractions of identified cell types across Scissor +/− conditions among extracted immune cells.$

Journal: Aging Cell

Article Title: The Immune Cell Atlas of “Longevity Molecular Tag”: Identification of Principal Immune Cell Subsets and Their Underlying Molecular Regulatory Mechanisms

doi: 10.1111/acel.70431

Figure Lengend Snippet: Centenarian phenotype‐associated immune cell type analysis at single‐cell resolution. (A) UMAP visualization of cell‐type‐specific annotation among immune cells, showing 9 cell groups in different colors. (B) UMAP visualization of subcellular annotation among immune cell subpopulations, showing 21 subpopulations in different colors. (C) UMAP visualization of Scissor + and Scissor − cells. (D, E) Proportional fractions of identified cell types across Scissor +/− conditions among extracted immune cells.

Techniques: Single Cell

PTHrP expression in IPF and BLM-induced PF in humans. a Procedure for bioinformatics-based transcriptome analysis. b Identification of 714 commonly up- or downregulated genes in human IPF lungs using publicly available transcriptome datasets. c Top 9 activated gene sets identified by KEGG pathway analysis based on 714 common genes. d Identification of 5 genes through the intersection of genes related to soluble mediators, PTH synthesis, secretion, and action and 714 common genes. e Heatmap of PTHLH expression in normal and IPF samples. f PTHLH mRNA in normal and IPF samples. g Representative images of IF staining of PTHrP and quantification of the intensity of expression of PTHrP in human pulmonary interstitial fibrosis tissue microarrays from patients with IPF ( n = 23) and healthy donors ( n = 4). A magnified view of the region highlighted in the red box is shown. Scale bar: 50 μm and 100 μm (low magnification). a , b , d were created with BioRender.com. Data are shown as the mean ± SEM. P values were determined by two-tailed Student’s t test ( f , g ). *** P < 0.001

Journal: Signal Transduction and Targeted Therapy

Article Title: Parathyroid hormone–related protein is a therapeutic target in idiopathic pulmonary fibrosis

doi: 10.1038/s41392-026-02578-8

Figure Lengend Snippet: PTHrP expression in IPF and BLM-induced PF in humans. a Procedure for bioinformatics-based transcriptome analysis. b Identification of 714 commonly up- or downregulated genes in human IPF lungs using publicly available transcriptome datasets. c Top 9 activated gene sets identified by KEGG pathway analysis based on 714 common genes. d Identification of 5 genes through the intersection of genes related to soluble mediators, PTH synthesis, secretion, and action and 714 common genes. e Heatmap of PTHLH expression in normal and IPF samples. f PTHLH mRNA in normal and IPF samples. g Representative images of IF staining of PTHrP and quantification of the intensity of expression of PTHrP in human pulmonary interstitial fibrosis tissue microarrays from patients with IPF ( n = 23) and healthy donors ( n = 4). A magnified view of the region highlighted in the red box is shown. Scale bar: 50 μm and 100 μm (low magnification). a , b , d were created with BioRender.com. Data are shown as the mean ± SEM. P values were determined by two-tailed Student’s t test ( f , g ). *** P < 0.001

Article Snippet: To confirm the predominance of tissue-specific expression of PTHLH in human tissues, we reanalyzed publicly available single-cell transcriptome data provided by The Human Protein Atlas.

Techniques: Expressing, Staining, Two Tailed Test

Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and transcriptomic profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.

Journal: Frontiers in Immunology

Article Title: Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

doi: 10.3389/fimmu.2026.1739660

Figure Lengend Snippet: Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and transcriptomic profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.

Article Snippet: For single-cell transcriptomic data, we accessed the SCP1303 project from the Broad Institute ( https://singlecell.broadinstitute.org/single_cell ), which includes raw scRNA-seq data from failing human hearts with dilated and hypertrophic cardiomyopathy.

Techniques: Derivative Assay, Gene Expression, Expressing

Five heart failure transcriptomic datasets were integrated with a macrophage-trained immunity model to identify immune-related biomarkers. Through DEGs analysis, WGCNA, CIBERSORT, and six machine learning algorithms, hub genes were prioritized with MTURN emerging as the top candidate. Its potential was further validated by scRNA-seq analysis, which confirmed MTURN enrichment in cardiac macrophages. Finally, MTURN expression was validated using previously published heart failure transcriptomic data and in vitro experiments.

Journal: Frontiers in Immunology

Article Title: Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

doi: 10.3389/fimmu.2026.1739660

Figure Lengend Snippet: Five heart failure transcriptomic datasets were integrated with a macrophage-trained immunity model to identify immune-related biomarkers. Through DEGs analysis, WGCNA, CIBERSORT, and six machine learning algorithms, hub genes were prioritized with MTURN emerging as the top candidate. Its potential was further validated by scRNA-seq analysis, which confirmed MTURN enrichment in cardiac macrophages. Finally, MTURN expression was validated using previously published heart failure transcriptomic data and in vitro experiments.

Techniques: Expressing, In Vitro

Platelet-Driven CAF Activation and ECM Barriers in the Tumor Microenvironment. A. Expression levels of TGFB1 and PDGFB were assessed using pan-tissue single-cell RNA-sequencing data from the Human Protein Atlas. Normalized counts (nCPM) were aggregated at the cell-type level. Among all surveyed human cell types, platelets showed the highest expression of TGFB1 and were among the top expressors of PDGFB, highlighting their distinct capacity as a concentrated source of these exclusion-related factors. B. Activated platelets engage CAFs via CLEC-2–podoplanin interaction and release TGF-β, PDGF, and SDF-1, inducing fibroblast, epithelial cell, and MSC differentiation into CAFs. MSCs activate platelets via PAF, forming a feedback loop. CAFs (α-SMA/FAP + ) remodel the ECM and promote desmoplasia, creating a barrier to T cell infiltration and sustaining immune suppression in the TME. Abbreviations: CAF: Cancer-Associated Fibroblast, CLEC-2: C-type Lectin-like Receptor 2, PDPN: Podoplanin, TGF-β: Transforming Growth Factor Beta, PDGF: Platelet-Derived Growth Factor, SDF-1: Stromal Cell-Derived Factor 1, MSC: Mesenchymal Stem Cell, PAF: Platelet-Activating Factor, α-SMA: Alpha-Smooth Muscle Actin, FAP: Fibroblast Activation Protein, ECM: Extracellular Matrix, TME: Tumor Microenvironment

Journal: Cellular Oncology (Dordrecht, Netherlands)

Article Title: Platelets in the tumor microenvironment: potential mediators of immune exclusion and resistance to immune checkpoint inhibitor therapy

doi: 10.1007/s13402-025-01129-7

Figure Lengend Snippet: Platelet-Driven CAF Activation and ECM Barriers in the Tumor Microenvironment. A. Expression levels of TGFB1 and PDGFB were assessed using pan-tissue single-cell RNA-sequencing data from the Human Protein Atlas. Normalized counts (nCPM) were aggregated at the cell-type level. Among all surveyed human cell types, platelets showed the highest expression of TGFB1 and were among the top expressors of PDGFB, highlighting their distinct capacity as a concentrated source of these exclusion-related factors. B. Activated platelets engage CAFs via CLEC-2–podoplanin interaction and release TGF-β, PDGF, and SDF-1, inducing fibroblast, epithelial cell, and MSC differentiation into CAFs. MSCs activate platelets via PAF, forming a feedback loop. CAFs (α-SMA/FAP + ) remodel the ECM and promote desmoplasia, creating a barrier to T cell infiltration and sustaining immune suppression in the TME. Abbreviations: CAF: Cancer-Associated Fibroblast, CLEC-2: C-type Lectin-like Receptor 2, PDPN: Podoplanin, TGF-β: Transforming Growth Factor Beta, PDGF: Platelet-Derived Growth Factor, SDF-1: Stromal Cell-Derived Factor 1, MSC: Mesenchymal Stem Cell, PAF: Platelet-Activating Factor, α-SMA: Alpha-Smooth Muscle Actin, FAP: Fibroblast Activation Protein, ECM: Extracellular Matrix, TME: Tumor Microenvironment

Article Snippet: Our analysis of pan-tissue single-cell transcriptomic data from the Human Protein Atlas identifies platelets as a dominant cellular source of TGF-β and among the highest expressors of PDGFB across human cell types (Fig. A), providing a strong molecular basis for their impact on CAF activation and differentiation.

Techniques: Activation Assay, Expressing, RNA Sequencing, Derivative Assay

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