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10X Genomics cell transcriptomic sequencing dataset

<t>Transcriptomic</t> and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Cell Transcriptomic Sequencing Dataset, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cell transcriptomic sequencing dataset - by Bioz Stars, 2026-09

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1) Product Images from "Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis"

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

Journal: iScience

doi: 10.1016/j.isci.2026.115982

Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Figure Legend Snippet: Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Techniques Used: Activity Assay, Protein-Protein interactions, Labeling

$Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.$
Figure Legend Snippet: Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Techniques Used: Single Cell, RNA Sequencing, Expressing, Marker

Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Figure Legend Snippet: Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Techniques Used: Marker, Expressing

Expressing:

Article Title: TRPV1 inhibition suppresses non-small cell lung cancer progression by inhibiting tumour growth and enhancing the immune response.
Article Snippet: 1 Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Xiang Ya Road 110, Changsha 410000, Hunan, China 2 Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, 3 Hunan, Changsha, China 3 Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, P. R. China 4 Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Central South University, Changsha, P. R. China 5 National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China 6 Center for Precision Medicine, Utah Tech University, St George, UT, USA Abstract Purpose TRPV1 is a nonselective Ca2+ channel protein that is widely expressed and plays an important role during the occurrence and development of many cancers.. Activation of TRPV1 channels can affect tumour progression by regulating proliferation, apoptosis and migration.. Some studies have also shown that activating TRPV1 can affect tumour progression by modulating tumour immunity.

Countercurrent Chromatography:

Article Title: Comparative analysis of cell-cell communication at single-cell resolution.
Article Snippet: Analysis of spatial transcriptomic datasets with Scriabin To evaluate if Scriabin returns biologically meaningful CCC edges, we downloaded spatial coordinates and gene expression count matrices from 10 spatial transcriptomic datasets from the 10x Visium platform available at https://www.10xgenomics.com/resources/datasets. .. Analysis of spatial transcriptomic datasets with Scriabin To evaluate if Scriabin returns biologically meaningful CCC edges, we downloaded spatial coordinates and gene expression count matrices from 10 spatial transcriptomic datasets from the 10x Visium platform available at https://www.10xgenomics.com/resources/datasets. .. We also analyzed a spatial transcriptomic dataset published by Ma et al.41 of Nature Biotechnology Article https://doi.org/10.1038/s41587-023-01782-z a human leprosy granuloma.

Gene Expression:

Single Cell:

Article Title: SCAR: Single-cell and Spatially-resolved Cancer Resources.
Article Snippet: .. Finally, to visualize the cell stemness prediction results, the prediction result and the cell type information of the single cell were simul- t f S I m 1 ( s i d S r S s i w s ‘ C T m r m d l r d d a t t j i a C I C t ‘ p w C P w M w m t r c l ‘ ‘ S T c t c D ow nloaded from https://academ ic.oup.com /nar/article/52/D 1/D 1407/7280550 by guest on 15 January 2024 aneously applied as parameters using the ‘plotCytoTRACE’ unction. patial transcriptomic dataset treatment n total, 41 spatial transcriptomic datasets of 21 human, ouse and zebrafish tissues were obtained from GEO ( 29 ), 0x Genomics ( https:// www.10xgenomics.com/ ), SPASCER 44 ), STOmicsDB ( 45 ), and SODB ( 46 ). ..

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Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Activity Assay, Protein-Protein interactions, Labeling

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Techniques: Single Cell, RNA Sequencing, Expressing, Marker

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Techniques: Marker, Expressing

Overview of TiSMeD TiSMeD is a comprehensive resource that integrates 6,782 methylomes, 16,894 transcriptomes, and 241 proteomes obtained from multiple platforms. All datasets were processed using a standardized pipeline, resulting in the identification of 67,427 TSMs, 4,607 TSGs, and 2,833 TSPs based on tissue specificity and confidence score evaluation. In addition, TiSMeD includes 11,411,136 HKMs. TiSMeD features a user-friendly interface that enables data searching, browsing, downloading, and visualization.

Journal: Molecular Therapy. Nucleic Acids

Article Title: TiSMeD: A tissue-specific methylation and expression database for biomarker and translational applications

doi: 10.1016/j.omtn.2026.102884

Figure Lengend Snippet: Overview of TiSMeD TiSMeD is a comprehensive resource that integrates 6,782 methylomes, 16,894 transcriptomes, and 241 proteomes obtained from multiple platforms. All datasets were processed using a standardized pipeline, resulting in the identification of 67,427 TSMs, 4,607 TSGs, and 2,833 TSPs based on tissue specificity and confidence score evaluation. In addition, TiSMeD includes 11,411,136 HKMs. TiSMeD features a user-friendly interface that enables data searching, browsing, downloading, and visualization.

Article Snippet: The transcriptome datasets were obtained from GEO, , the Genotype-Tissue Expression (GTEx) database, the Human Protein Atlas (HPA), and the RNA-seq Atlas.

Techniques:

STNvac induces antigen-specific tumor killing (A) UMAP visualization of single-cell transcriptomes from STNvac- and PBS-treated tumors showing nine major cell populations and a bar plot of their relative proportion. (B) Identification of tumor cells using SCEVAN. (C) Distribution changes of neoantigen expression after STNvac treatment. (D) Changes in the proportion of tumor cells expressing specific neoantigens after STNvac treatment. Subclones carrying highly immunogenic neoantigens ( Ptpn2_I383T and Traf7_C403W ) were significantly eliminated, whereas weakly immunogenic ones ( Samd91_K752M , Dtnb_K40T ) showed minimal change. (E) Bar plot showing the proportion changes in tumor cells expressing various numbers of neoantigens after STNvac treatment. (F) Bubble plot of cell-cell interaction showing ligand-receptor pairs between tumor cells and immune cells (T cells, NK cells, macrophages, DCs, and B cells).

Journal: Cell Reports Medicine

Article Title: Spleen-targeted neoantigen mRNA vaccine induces ISG15 + CD8 + T cell-mediated tertiary lymphoid structure formation in hepatocellular carcinoma

doi: 10.1016/j.xcrm.2026.102754

Figure Lengend Snippet: STNvac induces antigen-specific tumor killing (A) UMAP visualization of single-cell transcriptomes from STNvac- and PBS-treated tumors showing nine major cell populations and a bar plot of their relative proportion. (B) Identification of tumor cells using SCEVAN. (C) Distribution changes of neoantigen expression after STNvac treatment. (D) Changes in the proportion of tumor cells expressing specific neoantigens after STNvac treatment. Subclones carrying highly immunogenic neoantigens ( Ptpn2_I383T and Traf7_C403W ) were significantly eliminated, whereas weakly immunogenic ones ( Samd91_K752M , Dtnb_K40T ) showed minimal change. (E) Bar plot showing the proportion changes in tumor cells expressing various numbers of neoantigens after STNvac treatment. (F) Bubble plot of cell-cell interaction showing ligand-receptor pairs between tumor cells and immune cells (T cells, NK cells, macrophages, DCs, and B cells).

Article Snippet: Spatial transcriptome dataset from Liu’s research , Mendeley Data , http://www.doi.org/10.17632/skrx2fz79n.1 , https://data.mendeley.com/datasets/skrx2fz79n/1.

Techniques: Single Cell, Expressing, Immunopeptidomics

STNvac-induced enhancement of tumor infiltration, antigen-presenting capacity, and cytotoxic activity of ISG15 + CD8 + T cells (A) UMAP visualization of single-cell transcriptomes from CD45 + immune cells. (B) Relative proportions of major immune cell populations in PBS and STNvac groups. (C) UMAP visualization of T cell clusters from PBS and STNvac groups. (D) Relative proportions of different T cell clusters in PBS and STNvac groups. (E) Violin plots showing expression levels of T cell function markers across T cell clusters. (F) Representative multicolor immunofluorescence images of tumor sections showing co-localization of ISG15 + CD8 + T cells with GZMB and IFN-γ in PBS and STNvac groups. Scale bars, 50 μm. (G) Quantification of ISG15 + CD8 + T cell density co-expressing GZMB and IFN-γ, corresponding to (F). Unpaired two-tailed t test; ∗p < 0.05. Mean ± SD ( n = 3 biological replicates). (H) Comparative GO and KEGG pathway enrichment analyses of ISG15 + CD8 + T cells between PBS and STNvac groups. (I) Kaplan-Meier curves showing 5-year overall survival (OS) and progression-free survival (PFI) for HCC patients in TCGA: LIHC cohort stratified by ISG15 + CD8 + T cell signatures. See also and .

Journal: Cell Reports Medicine

Article Title: Spleen-targeted neoantigen mRNA vaccine induces ISG15 + CD8 + T cell-mediated tertiary lymphoid structure formation in hepatocellular carcinoma

doi: 10.1016/j.xcrm.2026.102754

Figure Lengend Snippet: STNvac-induced enhancement of tumor infiltration, antigen-presenting capacity, and cytotoxic activity of ISG15 + CD8 + T cells (A) UMAP visualization of single-cell transcriptomes from CD45 + immune cells. (B) Relative proportions of major immune cell populations in PBS and STNvac groups. (C) UMAP visualization of T cell clusters from PBS and STNvac groups. (D) Relative proportions of different T cell clusters in PBS and STNvac groups. (E) Violin plots showing expression levels of T cell function markers across T cell clusters. (F) Representative multicolor immunofluorescence images of tumor sections showing co-localization of ISG15 + CD8 + T cells with GZMB and IFN-γ in PBS and STNvac groups. Scale bars, 50 μm. (G) Quantification of ISG15 + CD8 + T cell density co-expressing GZMB and IFN-γ, corresponding to (F). Unpaired two-tailed t test; ∗p < 0.05. Mean ± SD ( n = 3 biological replicates). (H) Comparative GO and KEGG pathway enrichment analyses of ISG15 + CD8 + T cells between PBS and STNvac groups. (I) Kaplan-Meier curves showing 5-year overall survival (OS) and progression-free survival (PFI) for HCC patients in TCGA: LIHC cohort stratified by ISG15 + CD8 + T cell signatures. See also and .

Article Snippet: Spatial transcriptome dataset from Liu’s research , Mendeley Data , http://www.doi.org/10.17632/skrx2fz79n.1 , https://data.mendeley.com/datasets/skrx2fz79n/1.

Techniques: Activity Assay, Single Cell, Expressing, Cell Function Assay, Immunofluorescence, Two Tailed Test

Spatial localization of ISG15 + CD8 + T cells and APCs and the formation of TLS following STNvac treatment (A–D) Spatial transcriptomics analysis of tumor samples isolated from PBS- and STNvac-treated mice. (A) H&E staining and corresponding spatial clustering of transcriptome spots; 19 clusters were identified, with clusters 4 and 7 showing marked enrichment of immune cells (CD45 + ). (B) Spatial expression of Ptprc (CD45) mapped on H&E-stained tissues, highlighting immune-cell-dense regions. (C) Region of interest annotation based on TESLA, illustrating colocalization of ISG15 + CD8 + T cells (approximated by Pclaf / Birc5 ), B cells, DCs, and CD4 + T cells; CXCL13 expression overlapped with these immune clusters, and TLS-like regions were identified using a TLS score (co-localization of B cells, CD4 + T cells, DCs, and CXCL13). (D) Magnified views of regions I and II from (A), showing compact lymphoid aggregates (white arrows) and smaller, loosely organized foci (black arrows) at the tumor margin corresponding to TLS. (E–G) Multicolor immunofluorescence staining analysis of TLSs and ISG15 + CD8 + T cells in tumors from different groups. (E) Representative images showing CD20 + B cells and CD23 + follicular regions within TLSs, together with ISG15 + CD8 + T cells localized around TLS structures. Scale bars: 500 μm; inset, 50 μm. (F) Magnified views of Region I from (E), including H&E and additional multicolor staining for CD20, CD21, CD23, and CD34, highlighting organized FDC networks and HEV-like microvessels within TLS boundaries. Scale bars, 100 μm (left) and 50 μm (right). (G) Quantitative analysis of TLS density in tumors from different treatment groups ( n = 3 biological replicates). (H) Spatial transcriptomic mapping of human HCC tissues from Liu et al.’s study showing ISG15 + CD8 + T cells located near or within TLS-like regions in immune-inflamed tumors. See also .

Journal: Cell Reports Medicine

Article Title: Spleen-targeted neoantigen mRNA vaccine induces ISG15 + CD8 + T cell-mediated tertiary lymphoid structure formation in hepatocellular carcinoma

doi: 10.1016/j.xcrm.2026.102754

Figure Lengend Snippet: Spatial localization of ISG15 + CD8 + T cells and APCs and the formation of TLS following STNvac treatment (A–D) Spatial transcriptomics analysis of tumor samples isolated from PBS- and STNvac-treated mice. (A) H&E staining and corresponding spatial clustering of transcriptome spots; 19 clusters were identified, with clusters 4 and 7 showing marked enrichment of immune cells (CD45 + ). (B) Spatial expression of Ptprc (CD45) mapped on H&E-stained tissues, highlighting immune-cell-dense regions. (C) Region of interest annotation based on TESLA, illustrating colocalization of ISG15 + CD8 + T cells (approximated by Pclaf / Birc5 ), B cells, DCs, and CD4 + T cells; CXCL13 expression overlapped with these immune clusters, and TLS-like regions were identified using a TLS score (co-localization of B cells, CD4 + T cells, DCs, and CXCL13). (D) Magnified views of regions I and II from (A), showing compact lymphoid aggregates (white arrows) and smaller, loosely organized foci (black arrows) at the tumor margin corresponding to TLS. (E–G) Multicolor immunofluorescence staining analysis of TLSs and ISG15 + CD8 + T cells in tumors from different groups. (E) Representative images showing CD20 + B cells and CD23 + follicular regions within TLSs, together with ISG15 + CD8 + T cells localized around TLS structures. Scale bars: 500 μm; inset, 50 μm. (F) Magnified views of Region I from (E), including H&E and additional multicolor staining for CD20, CD21, CD23, and CD34, highlighting organized FDC networks and HEV-like microvessels within TLS boundaries. Scale bars, 100 μm (left) and 50 μm (right). (G) Quantitative analysis of TLS density in tumors from different treatment groups ( n = 3 biological replicates). (H) Spatial transcriptomic mapping of human HCC tissues from Liu et al.’s study showing ISG15 + CD8 + T cells located near or within TLS-like regions in immune-inflamed tumors. See also .

Article Snippet: Spatial transcriptome dataset from Liu’s research , Mendeley Data , http://www.doi.org/10.17632/skrx2fz79n.1 , https://data.mendeley.com/datasets/skrx2fz79n/1.

Techniques: Spatial Transcriptomics, Isolation, Staining, Expressing, Multicolor Immunofluorescence Staining

Journal: Cell Reports Medicine

Article Title: Spleen-targeted neoantigen mRNA vaccine induces ISG15 + CD8 + T cell-mediated tertiary lymphoid structure formation in hepatocellular carcinoma

doi: 10.1016/j.xcrm.2026.102754

Article Snippet: TCGA: LIHC dataset was obtained from GDC Data Portal and patients were stratified according to the ssGSEA scores of the signature containing CD8A, CD8B and ISG15, the survival analysis was then further conducted using survival packages in R. Mendeley Data: http://www.doi.org/10.17632/skrx2fz79n.1 (the spatial transcriptome dataset from Liu’s research ) was downloaded with the link provided in that publication.

Techniques: Single Cell, Expressing, Immunopeptidomics

Journal: Cell Reports Medicine

Article Title: Spleen-targeted neoantigen mRNA vaccine induces ISG15 + CD8 + T cell-mediated tertiary lymphoid structure formation in hepatocellular carcinoma

doi: 10.1016/j.xcrm.2026.102754

Techniques: Activity Assay, Single Cell, Expressing, Cell Function Assay, Immunofluorescence, Two Tailed Test

Journal: Cell Reports Medicine

Article Title: Spleen-targeted neoantigen mRNA vaccine induces ISG15 + CD8 + T cell-mediated tertiary lymphoid structure formation in hepatocellular carcinoma

doi: 10.1016/j.xcrm.2026.102754

Techniques: Spatial Transcriptomics, Isolation, Staining, Expressing, Multicolor Immunofluorescence Staining

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cell transcriptomic sequencing dataset (10X Genomics)

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