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10X Genomics 10x genomics single cell transcriptomics

Data sources for advancing computational modeling “Not So Big” and “Big” data sources can be utilized collectively to build computational models of varying complexity. “Not So Big” data are usually disparate, require extensive data collation, and must be obtained individually from various literature sources in the form of data tables present in the supplementary material. The “Not So Big” data are derived from targeted and focused experiments and provides tissue-level detail for mechanistic models such as the first cell cycle gene regulatory network from Tyson (1991). Bulk and single-cell “Big” data are derived from targeted and unbiased assays, and are usually stored in annotated collections and compendiums such as GEO <t>(transcriptomics),</t> ArrayExpress (transcriptomics), MetaboLights (metabolomics), and PRIDE (proteomics). This “Big” data provides genome-scale detail for informing correlation networks and genome scale metabolic models. Single-cell “Big” data from reference atlases, including The Cancer Genome Atlas, the Human Cell Atlas, HuBMAP, and Tabula Sapiens , provide untargeted and unbiased assays at the whole-body physiological scale. These data can be utilized to inform future virtual human models at various scales, including the molecular level (i.e., Wnt/B-Catenin signaling pathway), single-cell level (i.e., gene correlation networks), and physiological level (i.e., multi-organ interactions). <xref ref-type=

Figure 1 adapted from Tyson (1991), Gustafsson et al. (2023), Park et al. (2016), Moss et al. (2021). Created using biorender.com . " width="250" height="auto" />
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Article Title: From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling

Journal: iScience

doi: 10.1016/j.isci.2024.111322

Figure 1 adapted from Tyson (1991), Gustafsson et al. (2023), Park et al. (2016), Moss et al. (2021). Created using biorender.com . " title="... in annotated collections and compendiums such as GEO (transcriptomics), ArrayExpress (transcriptomics), MetaboLights (metabolomics), and PRIDE (proteomics). This ..." property="contentUrl" width="100%" height="100%"/>
Figure Legend Snippet: Data sources for advancing computational modeling “Not So Big” and “Big” data sources can be utilized collectively to build computational models of varying complexity. “Not So Big” data are usually disparate, require extensive data collation, and must be obtained individually from various literature sources in the form of data tables present in the supplementary material. The “Not So Big” data are derived from targeted and focused experiments and provides tissue-level detail for mechanistic models such as the first cell cycle gene regulatory network from Tyson (1991). Bulk and single-cell “Big” data are derived from targeted and unbiased assays, and are usually stored in annotated collections and compendiums such as GEO (transcriptomics), ArrayExpress (transcriptomics), MetaboLights (metabolomics), and PRIDE (proteomics). This “Big” data provides genome-scale detail for informing correlation networks and genome scale metabolic models. Single-cell “Big” data from reference atlases, including The Cancer Genome Atlas, the Human Cell Atlas, HuBMAP, and Tabula Sapiens , provide untargeted and unbiased assays at the whole-body physiological scale. These data can be utilized to inform future virtual human models at various scales, including the molecular level (i.e., Wnt/B-Catenin signaling pathway), single-cell level (i.e., gene correlation networks), and physiological level (i.e., multi-organ interactions). Figure 1 adapted from Tyson (1991), Gustafsson et al. (2023), Park et al. (2016), Moss et al. (2021). Created using biorender.com .

Techniques Used: Derivative Assay

Figure Legend Snippet: Molecularly targeted methods for single-cell and spatial transcriptomics

Techniques Used:

Figure Legend Snippet: Examples of transcriptome-proteome multiomics technologies

Techniques Used: Single-cell Isolation, RNA Detection, Reverse Transcription, Mass Cytometry, Staining

Computational models informed by experimental data The components, interactions, correlations, and patterns extracted from “Big” data (multi-omics data including transcriptomics, proteomics, metabolomics, and spatial omics) and the components, interactions and mechanisms extracted from “Not So Big” data (i.e., western blots, immuno-staining, and qPCR) can be utilized to generate and inform molecular signaling networks, putative cellular networks, and gene regulatory networks. For instance, while the MAP kinase pathway was discovered using “Not So Big” data sources (solid line) many “Big” data sources (dashed line) have confirmed and further explained and complemented these initial findings. Similarly, while gene regulatory networks have been mainly developed using “Big” data (solid line), “Not So Big” data (dashed line) can also be informative when generating such networks. For example, Park et al., (2016) modeled neurons during the circadian cycle. First, five neuronal groups were identified according to their unique transcriptional landscapes with marker genes shown for each of the groups. A gene regulatory network was then developed based on the major molecular interactions between key neuropeptides (VIP, AVP, PROK2, and PACAP) and the neuronal groups. “Big” and “Not So Big” data (solid lines) have be analyzed in combination to identify putative cellular networks. Cell types can be identified within the “Big” data by using information from “Not So Big” data. Then, cell states within each cell type community can be determined by molecular markers. The cell types and states can then be used to infer cell state transitions, trajectories, and interactions. A greater influence of “Big” and “Not So Big” data on developing the various networks is shown with solid lines with lesser influence shown by dashed lines. GF: growth factor, GFR: growth factor receptor, VIP: Vasoactive Intestinal Peptide, AVP: Arginine Vasopressin, PROK2: Prokineticin 2, PACAP: Pituitary Adenylate Cyclase-Activating Polypeptide. Fig. adapted from Park et al., (2016). Created using biorender.com .

Figure Legend Snippet: Computational models informed by experimental data The components, interactions, correlations, and patterns extracted from “Big” data (multi-omics data including transcriptomics, proteomics, metabolomics, and spatial omics) and the components, interactions and mechanisms extracted from “Not So Big” data (i.e., western blots, immuno-staining, and qPCR) can be utilized to generate and inform molecular signaling networks, putative cellular networks, and gene regulatory networks. For instance, while the MAP kinase pathway was discovered using “Not So Big” data sources (solid line) many “Big” data sources (dashed line) have confirmed and further explained and complemented these initial findings. Similarly, while gene regulatory networks have been mainly developed using “Big” data (solid line), “Not So Big” data (dashed line) can also be informative when generating such networks. For example, Park et al., (2016) modeled neurons during the circadian cycle. First, five neuronal groups were identified according to their unique transcriptional landscapes with marker genes shown for each of the groups. A gene regulatory network was then developed based on the major molecular interactions between key neuropeptides (VIP, AVP, PROK2, and PACAP) and the neuronal groups. “Big” and “Not So Big” data (solid lines) have be analyzed in combination to identify putative cellular networks. Cell types can be identified within the “Big” data by using information from “Not So Big” data. Then, cell states within each cell type community can be determined by molecular markers. The cell types and states can then be used to infer cell state transitions, trajectories, and interactions. A greater influence of “Big” and “Not So Big” data on developing the various networks is shown with solid lines with lesser influence shown by dashed lines. GF: growth factor, GFR: growth factor receptor, VIP: Vasoactive Intestinal Peptide, AVP: Arginine Vasopressin, PROK2: Prokineticin 2, PACAP: Pituitary Adenylate Cyclase-Activating Polypeptide. Fig. adapted from Park et al., (2016). Created using biorender.com .

Techniques Used: Biomarker Discovery, Western Blot, Immunostaining, Marker

Computational models informed by single-cell omics (A) Single-cell omics, including transcriptomics, proteomics, and metabolomics can be used for modeling tumor cell differentiation dynamics. The specific cell types of interest that were identified within the tumor tissue include stem, progenitor, and differentiated cell types. State transitions, trajectories, and interactions between these cell types can then be inferred such that a network model can be generated. The tumor cell differentiation model can then be simulated to determine how the individual cell populations within the tumor change over time. (B) Single-cell omics experiments can be performed on the liver following resection to elucidate liver-specific cell types including Kupffer cells, Stellate cells and hepatocytes. For simplicity, we only show the hepatocyte cell states (replicating, quiescent, and primed), which are informed by molecular markers from the single-cell data. State transitions, trajectories and interactions can then be inferred from the cell states. A systems network model of liver regeneration can then be developed using the features extracted from the single-cell data and the model can be simulated for liver mass recovery and cellular dynamics. The total mass recovery as well as the populations of primed and replicating hepatocytes populations during regeneration are shown. Additionally, the populations of pro- and anti-regenerative stellate cell populations during regeneration are shown. <xref ref-type=

Figure 4 A adapted from Nazari et al., (2018). Figure 4 B adapted from Cook et al., (2018). Created using biorender.com . " title="... informed by single-cell omics (A) Single-cell omics, including transcriptomics, proteomics, and metabolomics can be used for modeling ..." property="contentUrl" width="100%" height="100%"/>
Figure Legend Snippet: Computational models informed by single-cell omics (A) Single-cell omics, including transcriptomics, proteomics, and metabolomics can be used for modeling tumor cell differentiation dynamics. The specific cell types of interest that were identified within the tumor tissue include stem, progenitor, and differentiated cell types. State transitions, trajectories, and interactions between these cell types can then be inferred such that a network model can be generated. The tumor cell differentiation model can then be simulated to determine how the individual cell populations within the tumor change over time. (B) Single-cell omics experiments can be performed on the liver following resection to elucidate liver-specific cell types including Kupffer cells, Stellate cells and hepatocytes. For simplicity, we only show the hepatocyte cell states (replicating, quiescent, and primed), which are informed by molecular markers from the single-cell data. State transitions, trajectories and interactions can then be inferred from the cell states. A systems network model of liver regeneration can then be developed using the features extracted from the single-cell data and the model can be simulated for liver mass recovery and cellular dynamics. The total mass recovery as well as the populations of primed and replicating hepatocytes populations during regeneration are shown. Additionally, the populations of pro- and anti-regenerative stellate cell populations during regeneration are shown. Figure 4 A adapted from Nazari et al., (2018). Figure 4 B adapted from Cook et al., (2018). Created using biorender.com .

Techniques Used: Cell Differentiation, Generated

Figure Legend Snippet: Highlighted algorithms for computational modeling informed by single-cell and spatial omics data

Techniques Used: Expressing, Gene Expression, Spatial Proteomics

Patient-specific models informed by omics data (A) Metabolomics, transcriptomics, and proteomics data can be collected from a patient’s liver sample. A patient-specific genome scale metabolic model (GEM) of the liver can then be generated by integrating the transcriptomics and proteomics data with a generic GEM (i.e., Human1 or Recon2 ). Metabolic fluxes are constrained using the metabolomics data and predicted by flux balance analysis. (B) Bulk and single-cell RNA-seq data can be utilized to generate context-specific metabolic models in health and disease (i.e., liver disease). Metabolic fluxes can be predicted by flux balance analysis and significantly perturbed metabolic pathways/subsystems can be identified in health vs. disease. For example, our analysis of liver transcriptomics data from alcoholic liver disease identified significant metabolic dysregulation in the glutathione (GSH) metabolic pathway. Specifically, the metabolic flux activity of specific solute transporters (LAT1, BAT1, OATP1A2) within the GSH pathway decreased with liver disease, while healthy livers showed an increase in flux along the pathway. (C) Zone-specific hepatocyte populations can be elucidated from single-cell omics data sources. The metabolic expression for genes in the B-oxidation and gluconeogenesis pathways decreases from zone 3 to zone 1, while it increases from zone 3 to zone 1 for genes in the glycolysis and lipogenesis pathways. Marker expression for each of the zonated hepatocyte populations within the “Big” data can be utilized in conjunction with “Not So Big” experimental data (i.e., neural tracings, calcium imaging, and glycogenolytic distribution analyses) to parameterize and structure a computational model of liver innervation, calcium signaling, and glycogenolysis. Additionally, the extent of innervation to the liver can be tuned in the model to the species of interest based on physiological evidence from the literature. <xref ref-type=

Figure 7 B adapted from Manchel et al., (2022). Figure 7 C adapted from Verma, Manchel et al., (2021). Created using biorender.com . " title="Patient-specific models informed by omics data (A) Metabolomics, transcriptomics, and proteomics data can be collected from a ..." property="contentUrl" width="100%" height="100%"/>
Figure Legend Snippet: Patient-specific models informed by omics data (A) Metabolomics, transcriptomics, and proteomics data can be collected from a patient’s liver sample. A patient-specific genome scale metabolic model (GEM) of the liver can then be generated by integrating the transcriptomics and proteomics data with a generic GEM (i.e., Human1 or Recon2 ). Metabolic fluxes are constrained using the metabolomics data and predicted by flux balance analysis. (B) Bulk and single-cell RNA-seq data can be utilized to generate context-specific metabolic models in health and disease (i.e., liver disease). Metabolic fluxes can be predicted by flux balance analysis and significantly perturbed metabolic pathways/subsystems can be identified in health vs. disease. For example, our analysis of liver transcriptomics data from alcoholic liver disease identified significant metabolic dysregulation in the glutathione (GSH) metabolic pathway. Specifically, the metabolic flux activity of specific solute transporters (LAT1, BAT1, OATP1A2) within the GSH pathway decreased with liver disease, while healthy livers showed an increase in flux along the pathway. (C) Zone-specific hepatocyte populations can be elucidated from single-cell omics data sources. The metabolic expression for genes in the B-oxidation and gluconeogenesis pathways decreases from zone 3 to zone 1, while it increases from zone 3 to zone 1 for genes in the glycolysis and lipogenesis pathways. Marker expression for each of the zonated hepatocyte populations within the “Big” data can be utilized in conjunction with “Not So Big” experimental data (i.e., neural tracings, calcium imaging, and glycogenolytic distribution analyses) to parameterize and structure a computational model of liver innervation, calcium signaling, and glycogenolysis. Additionally, the extent of innervation to the liver can be tuned in the model to the species of interest based on physiological evidence from the literature. Figure 7 B adapted from Manchel et al., (2022). Figure 7 C adapted from Verma, Manchel et al., (2021). Created using biorender.com .

Techniques Used: Generated, RNA Sequencing, Activity Assay, Expressing, Marker, Imaging

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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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