single cell rna sequencing scrna seq dataset gse267718  (10X Genomics)

Journal: medRxiv

Article Title: Multi-Omics Integrative Analysis of the Aspirin-Gut-Brain-Glioma Axis: Transcriptomic, Proteomic, Epigenetic, Mendelian Randomization, and Single-Cell Transcriptomic Evidence Converges on NEO1/Hepcidin Iron Reprogramming and Ferroptosis Vulnerability

doi: 10.64898/2026.06.01.26354602

Figure Lengend Snippet: (A) UMAP visualization of malignant GBM cells colored by inferred cell state (MES = mesenchymal, 76.4%; AC = astrocyte-like; OPC = oligodendrocyte progenitor-like; NPC = neural progenitor-like) based on Neftel 2019 meta-module gene signatures. (B) Dot plot showing the expression of 16 iron metabolism and ferroptosis genes across cell states. Dot size represents the percentage of cells expressing each gene; color intensity represents the mean log-normalized expression. (C) Ferroptosis vulnerability score (15 driver genes − 20 suppressor genes) across cell states. MES exhibited the highest vulnerability (−0.400 vs. AC −0.526, OPC −0.493, NPC −0.492; all P<0.001). Universally negative scores reflect dominant GPX4/SLC7A11 suppressor expression. (D) Single-cell NEO1 vs. HAMP correlation (Spearman ρ = +0.27, P = 0.135). The lack of significant correlation at the transcriptional level is consistent with the protein-level mode of NEO1/BMP/HAMP signaling regulation.

Article Snippet: To validate our bulk transcriptomic findings at single-cell resolution and assess the heterogeneity of iron metabolism gene expression across GBM malignant cell states, we analyzed single-cell RNA-seq data from 28 GBM patients (GSE131928, Neftel et al. [ ]), comprising 15,112 cells profiled by 10X Genomics.

Techniques: Expressing, Single Cell

Journal: Frontiers in Immunology

Article Title: Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation

doi: 10.3389/fimmu.2026.1705706

Figure Lengend Snippet: Single-cell transcriptomic analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts (nFeature_RNA), UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell RNA-seq data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.

Article Snippet: Single-cell RNA sequencing (scRNA-seq) datasets were obtained from GSE145086 and GSE233084 , both generated using the 10X Genomics platform ( , ).

Techniques: Single Cell, Control, RNA Sequencing, Cell Cycle Assay, Expressing

Journal: bioRxiv

Article Title: BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

doi: 10.64898/2026.05.16.720484

Figure Lengend Snippet: (a) Flowchart of BioGAIP analyzing RNA-seq data. The expected endpoint of this task is the generation of a list of differentially expressed genes (DEGs). During the successive process, human intervention can be introduced to prompt BioGAIP for specific downstream analyses. (b) Flowchart of BioGAIP analyzing ATTSS events. The endpoint of this task is to generate a list of differential ATTSS events. The LLM model employed in the analysis processes for (a) is qwen3-max and for (b) is grok-4-fast-reasoning. Titles in green boxes indicate normally executed steps. Titles in brown boxes indicates steps where BioGAIP encountered an error. Titles in blue boxes indicate troubleshooting steps performed by BioGAIP, with blue text showing the specific error cause(s) (when present). Titles in dark green boxes indicate steps that involved manual prompt input by the user. Arrows denote the direction of the workflow, troubleshooting flows are shown with dashed lines.

Article Snippet: The single-cell RNA-seq reference package (refdata-gex-GRCh38-2024-A.tar.gz) from the Cell Ranger website ( https://www.10xgenomics.com/support/software/cell-ranger/downloads ).

Techniques: RNA Sequencing

Journal: bioRxiv

Article Title: BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

doi: 10.64898/2026.05.16.720484

Figure Lengend Snippet: (a) Volcano plot of DEGs in bulk RNA-seq data of met-associated primary SCLC and never-met primary SCLC. (b) UMAP of cell annotation and FOXA2 expression level in each component based scRNA-seq. (c) Correlation plot of the top 100 highly expressed genes in FOXA2 + vs. FOXA2 - cells from Kawasaki et al. gene set, relative to FOXA2 expression defined by Kawasaki et al . (d,e) Density plot of ASCL1 ChIP-seq gene loci at the FOXA2 and PROX1 gene loci in two SCLC cell lines (H1836 and SHP-77). (f) Density plot at the FOXA2 locus of ATAC-seq data derived from ASCL1 + FOXA2 + PDX vs ASCL1 + FOXA2 - PDX tumors.

Article Snippet: The single-cell RNA-seq reference package (refdata-gex-GRCh38-2024-A.tar.gz) from the Cell Ranger website ( https://www.10xgenomics.com/support/software/cell-ranger/downloads ).

Techniques: RNA Sequencing, Expressing, ChIP-sequencing, Derivative Assay

Journal: bioRxiv

Article Title: BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

doi: 10.64898/2026.05.16.720484

Figure Lengend Snippet: (a) Volcano plot of differential ATTSS events. (b) RNA-seq density plots showing the high distal TSS usage of RAB35 in met-associated primary tumors and never-met primary tumors. (c) The Venn diagram between different ATTSS events and differential expression genes. (d) The effect of different ATTSS events on gene coding region. (e) The comparison of distal TSS usage of RAB35 between never-met primary and met-associated primary tumor. (f) The comparison of gene expression of RAB35 between never-met primary and met-associated primary tumor. Statistical significance of distal TSS usage was assessed using the DATTS , statistical significance of gene expression was assessed using the DESeq2 . The analysis of (c) was completed by human experts based on the output of BioGAIP.

Article Snippet: The single-cell RNA-seq reference package (refdata-gex-GRCh38-2024-A.tar.gz) from the Cell Ranger website ( https://www.10xgenomics.com/support/software/cell-ranger/downloads ).

Techniques: RNA Sequencing, Quantitative Proteomics, Comparison, Gene Expression

Journal: bioRxiv

Article Title: BioGAIP: A Scalable, User-Friendly and Robust LLM-Powered Multi-Agent System for Automated Bioinformatics Tasks

doi: 10.64898/2026.05.16.720484

Figure Lengend Snippet: (a) Heatmap of differential ATTSS events based on DTUI values. (b) RNA-seq density plots show the high distal TSS usage of AP4E1 in two met-associated primary tumors and two never-met primary tumors. (c) The comparison of distal TSS usage of AP4E1 between never-met primary and met-associated primary tumor. (d) The comparison of gene expression of AP4E1 between never-met primary and met-associated primary tumor. (e-f) The comparison of gene expressions of RAB35 (e) and AP4E1 (f) between normal and sample tumor in GES60052. Statistical significance of distal TSS usage was assessed using the DATTS , statistical significance of gene expression was assessed using the DESeq2 .

Article Snippet: The single-cell RNA-seq reference package (refdata-gex-GRCh38-2024-A.tar.gz) from the Cell Ranger website ( https://www.10xgenomics.com/support/software/cell-ranger/downloads ).

Techniques: RNA Sequencing, Comparison, Gene Expression

Journal: bioRxiv

Article Title: Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus

doi: 10.64898/2026.05.14.724487

Figure Lengend Snippet: A) A UMAP plot is generated from the single-cell data, processed by the STAR-MAPS framework. Cells are colored by dissected brain region. B) The same UMAP, colored by cell-type label from the initial description of the dataset. C) A heatmap showing the degree of enrichment for 185 ASD-associated genes in the cells from Panels A and B, in data corrected for sample, batch, and developmental stage. Shade represents the magnitude of positive odds ratios; stars represent the P-value.

Article Snippet: Bhaduri et al . also describes 10X Genomics Chromium single-cell RNA-seq data but from eleven individual brains in the second trimester (12-23 PCW).

Techniques: Generated, Single Cell