scatac seq profiles  (10X Genomics)

Journal: Journal of Translational Medicine

Article Title: mTOR inhibition promotes ATRA-induced cancer cell differentiation by overcoming a metabolic hyperactive state

doi: 10.1186/s12967-026-08218-7

Figure Lengend Snippet: Single-cell multiomic characterization of a differentiation-stalled state in ATRA-treated HL-60 cells. A . UMAP visualization of scATAC-seq data from HL-60 cells treated with ATRA for 0, 1, 3, or 6 days, colored by three identified clusters. C1 represents naïve cells; C2 and C3 represent ATRA-induced cell clusters. B . Heatmap showing the relative proportion of cells from each treatment time point within clusters C1–C3. C . Bar plot showing predicted lineage identity of each cluster, as determined by projection onto a reference atlas of normal human hematopoiesis. Each color represents a distinct hematopoietic lineage. D . Heatmap of scores of peak-to-gene (P2G) linkages derived from the integrated scATAC-seq and scRNA-seq data across the three clusters. E . GO enrichment analysis of featured P2G-linked genes. F . Feature plots from integrated scATAC-seq and scRNA-seq data. Top panels show gene activity from scATAC-seq; bottom panels show matched gene expression from scRNA-seq

Article Snippet: Integration of scATAC-seq with scRNA-seq data (HL-60 cells under ATRA time course, from National Omics Data Encyclopedia, project ID OEP00001921) was performed using a constrained integration strategy in ArchR, in both scRNA-seq and scATAC-seq data, clusters corresponding to the time of collection (i.e., ATRA_day0, ATRA_day1, ATRA_day3, or ATRA_day6) were specified.

Techniques: Single Cell, Derivative Assay, Activity Assay, Gene Expression

Journal: bioRxiv

Article Title: Systematic identification of chromatin organizers as tuners of intratumoral heterogeneity

doi: 10.64898/2026.04.18.719392

Figure Lengend Snippet: (A) Schematic of the Perturb-seq workflow. MDA-MB-231 cells were transduced with CRISPRi or CRISPRa sgRNAs targeting the 16 in vivo -validated COs, followed by 10X 3 ′ scRNA-seq. Genome-wide CV was computed for each perturbation and compared to size-matched random control groupings. (B) Mean and standard error of mean barplots for genome-wide CV change upon CRISPRi (left) or CRISPRa (right) perturbation of RNF8 and MIS18A. A value of 1 indicates no change relative to non-targeting control. P -values by one-sample two-tailed Student’s t -test ( μ = 1). (C, D) Per-gene transcript CV in CRISPRi cells versus non-targeting controls for RNF8 (C) and MIS18A (D) . Each horizontal line represents a gene; P -values by two-sided paired Student’s t -test. Percentages indicate proportion of genes that decrease (top) or increase (bottom) in CV relative to non-targeting control. (E) CV analysis in an independent patient tumor scRNA-seq cohort . Patients were stratified into quartiles by RNF8 or MIS18A expression ( n = 4 per quartile). P -values by two-sided Wilcoxon signed-rank test.

Article Snippet: For scATAC-seq library generation, we used the 10X scATAC-seq library v2 kit (10X Genomics, PN-1000390).

Techniques: Transduction, In Vivo, Genome Wide, Control, Two Tailed Test, Expressing

Journal: bioRxiv

Article Title: Systematic identification of chromatin organizers as tuners of intratumoral heterogeneity

doi: 10.64898/2026.04.18.719392

Figure Lengend Snippet: (A) Experimental schematic. RNF8 and MIS18A CRISPRi/a MDA-MB-231 lines were hash-pooled and pro-filed by 10X scATAC-seq. (B) Genome-wide open-chromatin CV change per genomic locus upon CRISPRi or CRISPRa perturbation of RNF8 and MIS18A. Accessibility was filtered for gene regions; a value of zero indicates no change relative to non-targeting control. P -values by two-sided Wilcoxon signed-rank test. (C, D) Left: binarized open/closed chromatin scores (ArchR) across cells (rows) and genomic position (columns) for representative loci. Yellow boxes highlight reduced accessibility dispersion upon knockdown (C) and increased dispersion upon overexpression (D) . Right: CV for the highlighted locus in each perturbation condition. P -values by Fisher’s exact test.

Article Snippet: For scATAC-seq library generation, we used the 10X scATAC-seq library v2 kit (10X Genomics, PN-1000390).

Techniques: Genome Wide, Control, Dispersion, Knockdown, Over Expression

Journal: Nature Communications

Article Title: Cell neighborhood topology directs rare cell population identification

doi: 10.1038/s41467-026-71180-x

Figure Lengend Snippet: a Benchmarking RareQ against MarsGT in the paired Sim-PBMC 1, 2, and 3 datasets via F 1 score. RareQ was applied to each modality independently (RareQ_RNA for RNA data, RareQ_ATAC for ATAC data, RareQ_WNN for WNN-integrated data), while MarsGT integrates both modalities. b Comparative results of RareQ against MarsGT on the four human PBMC paired scRNA-seq datasets (PBMC-bench-1, 2, 3 and 4) with scATAC-seq data via F 1 scores. c Magnified view of Supplementary Fig. showing the progenitor cell types identified by RareQ on ATAC modality. d Heatmaps of cell-type-specific markers of identified cell subsets in expression (left) and accessibility (right). Benchmarking RareQ against MarsGT under optimal parameter settings using 10 paired scRNA-seq and scATAC-seq datasets on ( e ) rare cell detection via F 1 score, precision and recall, and ( f ) global clustering via NMI. Boxes extend from the first to the third quartile (Q1 – Q3) with a line in the middle denoting the median. Whisker lines extending from both ends of the box indicate variability outside Q1 and Q3, whose minimum/maximum values are calculated as Q1 − 1.5 × IQR and Q3 + 1.5 × IQR. Source data are provided as a file.

Article Snippet: The paired B lymphoma scRNA-seq and scATAC-seq dataset, Xenium spatial datasets of mouse brain and human RCC were obtained from the 10X Genomics website.

Techniques: Expressing, Whisker Assay

Journal: Nature Communications

Article Title: Single-cell multiomics uncovers an endothelial mechanosensitive PIEZO1-IL-33 axis driving pulmonary fibrosis

doi: 10.1038/s41467-026-70193-w

Figure Lengend Snippet: A Schematic diagram of the SiO 2 mouse model. Mice were divided into control (saline, n = 3) and experimental (SiO 2 , n = 3) groups by tracheal injection. After 20 weeks, mice were euthanized, and lung single-cell suspensions were prepared for sequencing. B Validation of the SiO 2 mouse model. H&E and Masson staining showed increased inflammation and fibrosis in the lungs of SiO 2 treated mice ( n = 3 mice per group). C UMAP plots of lung tissue scRNA-seq data from SiO 2 -induced and control mice. D UMAP plots of lung tissue scATAC-seq data from SiO 2 -induced and control mice. E Violin plots showed expression of marker genes of scRNA-seq data from SiO 2 -induced and control mice. F In scATAC-seq data from SiO 2 -induced and control mice, peaks for marker genes were identified for each subcelltype. G The UMAP plot showed endothelial subpopulations in scRNA-seq data from SiO 2 -induced and control mice. UMAP plot ( H ) and violin plot ( I ) showed the MS scores of endothelial cells in scRNA-seq data of Silica-induced ( n = 784 cells) and control ( n = 816 cells) mice. J The UMAP plot showed the endothelial subpopulations of scATAC-seq data. UMAP plot ( K ) and violin plot ( L ) showed the MS scores of endothelial cells in scRNA-seq data of Silica-induced ( n = 387 cells) and control ( n = 689 cells) mice. M GO enrichment analysis of motifs showed that signaling pathways associated with mechanical stress were enriched in ECs. Statistical significance of GO terms was calculated using the hypergeometric test. N Table showing TF motifs of endothelial cells enriched in mechanical stress-related pathways by scATAC-seq data. P values were calculated using the hypergeometric test. I, L: Violin plot displayed the mean (yellow diamond) the median (center line), the interquartile range, and the whiskers (1.5 × interquartile range). P value was calculated using the two-tailed unpaired t-test. Source data are provided as a Source Data file.

Article Snippet: We collected single-cell suspensions from lung tissues to prepare scRNA-seq and scATAC-seq libraries using 10X Genomics kits to measure RNA and chromatin accessibility (Fig. ).

Techniques: Control, Saline, Injection, Single Cell, Sequencing, Biomarker Discovery, Staining, Expressing, Marker, Protein-Protein interactions, Two Tailed Test

Journal: Nature Communications

Article Title: Single-cell multiomics uncovers an endothelial mechanosensitive PIEZO1-IL-33 axis driving pulmonary fibrosis

doi: 10.1038/s41467-026-70193-w

Figure Lengend Snippet: A BLM intratracheal model scheme (n = 3 per group). B H&E and Masson validation of inflammation/fibrosis (n = 3 per group). C Lung scRNA-seq UMAP. D scATAC-seq UMAP subclusters. E Venn showing Piezo1 and Piezo2 as co-regulated genes across silica and BLM bulk, scRNA-seq and scATAC-seq data. F scATAC-seq coverage peaks of Piezo1 and Piezo2 . G CD31 and CD45-sorted EC bulk-RNA-seq heatmap and bar chart (n = 3 per group). Gene expression levels were Z-score normalized. P values were calculated using the two-tailed unpaired t-test. H PIEZO1 (green) and Ve-cad (red) co-staining and quantification in fibrotic mouse lungs (n = 4). UMAP and bar charts (mean ± SEM) of PIEZO1 + ( I ) and PIEZO2 + ( J ) EC counts in normal controls (n = 5) versus IPF (n = 4) patients. P values were calculated using the two-tailed unpaired t-test. K PIEZO1 (green) and CD31 (red) co-staining and quantification in human IPF vs normal lungs (n = 10). L Meta-analysis of five GEO datasets showing elevated PIEZO1 in IPF ECs. Standardized mean difference (SMD) were calculated as Hedges’ g with corresponding 95% confidence intervals. Data were shown as mean ± SEM, H and K: two-tailed unpaired t-tests, Source data are provided as a Source Data file.

Article Snippet: We collected single-cell suspensions from lung tissues to prepare scRNA-seq and scATAC-seq libraries using 10X Genomics kits to measure RNA and chromatin accessibility (Fig. ).

Techniques: Biomarker Discovery, RNA Sequencing, Gene Expression, Two Tailed Test, Staining