Review

chromium next gem single cell multiome atac gex kit (10X Genomics)

About

News

Press Release

Team

Advisors

Partners

Contact

Bioz Stars

Bioz vStars

Buy from Supplier

Structured Review

10X Genomics chromium next gem single cell multiome atac gex kit

Longitudinal single-cell RNA and <t>ATAC</t> atlas of pediatric high-grade glioma (pHGG) (A) Overview of the multiomics studies on patient-matched longitudinal pHGG specimens. (B and C) Uniform manifold approximation and projection (UMAP) of (B) snRNA-seq data (401,253 cells) and (C) snATAC-seq data (118,736 cells) annotated by major cell type category (left) and stacked bar plot of cell-type proportions across dataset comparing initially resected pHGG samples with post-therapy samples. (D) Cell-type proportions in snRNA-seq data across each patient and therapeutic time point, along with a summary of patient demographics and molecular subtype. (E and F) Shifts in cell-type proportions for each patient between initial resection and post-therapy time points in (E) snRNA-seq ( n = 14 initial/post-therapy paired samples) and (F) snATAC-seq ( n = 11 paired samples). Post-therapy samples were merged for one patient with three longitudinal samples. A two-sided Wilcoxon signed-rank test for paired samples was used.

Chromium Next Gem Single Cell Multiome Atac Gex Kit, 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
https://www.bioz.com/result/chromium next gem single cell multiome atac gex kit/product/10X Genomics
Average 86 stars, based on 1 article reviews

chromium next gem single cell multiome atac gex kit - by Bioz Stars, 2026-06

86/100 stars

Images

1) Product Images from "A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma"

Article Title: A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma

Journal: Cell Reports Medicine

doi: 10.1016/j.xcrm.2026.102766

Longitudinal single-cell RNA and ATAC atlas of pediatric high-grade glioma (pHGG) (A) Overview of the multiomics studies on patient-matched longitudinal pHGG specimens. (B and C) Uniform manifold approximation and projection (UMAP) of (B) snRNA-seq data (401,253 cells) and (C) snATAC-seq data (118,736 cells) annotated by major cell type category (left) and stacked bar plot of cell-type proportions across dataset comparing initially resected pHGG samples with post-therapy samples. (D) Cell-type proportions in snRNA-seq data across each patient and therapeutic time point, along with a summary of patient demographics and molecular subtype. (E and F) Shifts in cell-type proportions for each patient between initial resection and post-therapy time points in (E) snRNA-seq ( n = 14 initial/post-therapy paired samples) and (F) snATAC-seq ( n = 11 paired samples). Post-therapy samples were merged for one patient with three longitudinal samples. A two-sided Wilcoxon signed-rank test for paired samples was used.

Figure Legend Snippet: Longitudinal single-cell RNA and ATAC atlas of pediatric high-grade glioma (pHGG) (A) Overview of the multiomics studies on patient-matched longitudinal pHGG specimens. (B and C) Uniform manifold approximation and projection (UMAP) of (B) snRNA-seq data (401,253 cells) and (C) snATAC-seq data (118,736 cells) annotated by major cell type category (left) and stacked bar plot of cell-type proportions across dataset comparing initially resected pHGG samples with post-therapy samples. (D) Cell-type proportions in snRNA-seq data across each patient and therapeutic time point, along with a summary of patient demographics and molecular subtype. (E and F) Shifts in cell-type proportions for each patient between initial resection and post-therapy time points in (E) snRNA-seq ( n = 14 initial/post-therapy paired samples) and (F) snATAC-seq ( n = 11 paired samples). Post-therapy samples were merged for one patient with three longitudinal samples. A two-sided Wilcoxon signed-rank test for paired samples was used.

Techniques Used: Single Cell

$Transcriptional regulation of pHGG neoplastic cell states (A) Stacked bar plot of cell-type proportions of neoplastic cell states across dataset comparing initial resection and post-therapy samples. AC, astrocyte; GPC, glial progenitor cell; MES, mesenchymal; OC, oligodendrocyte; OPC, oligodendrocyte progenitor cell; NPC, neural progenitor cell; NEU, neural. (B) Representative ATAC signal tracks of FGFR1 locus and gene expression across cell states and time points. The link track represents the predicted enhancer-promoter interactions colored by the regression coefficient, and the TF motifs present at the enhancer peaks are indicated. Differentially accessible peaks between time points are highlighted. ∗∗∗ p < 0.0001, significance in gene expression or accessibility in FGFR1 ; ns, not significant, using one-sided Wilcoxon rank-sum test. (C) Shifts in neoplastic cell state proportions for each patient between initial resection and post-therapy time points in snATAC-seq ( n = 11 paired samples). Post-therapy samples were merged for one patient with three longitudinal samples. A two-sided Wilcoxon signed-rank test for paired samples was used. (D) Heatmap of differential transcription factor (TF) motif accessibility in each pHGG neoplastic cell state. Values are Z score-normalized deviation scores calculated using chromVAR. The differential TF accessibility analysis was performed by a Wilcoxon rank-sum test, comparing chromVAR deviation score between each cell state and the other cell states. The top 20 differential TFs are displayed for each state. (E) Overview of top 15 significant transcriptional regulators for each neoplastic cell state based on predicted enhancer-promoter interactions and TF-target gene pairs. The size of the dot indicates the fraction of the total gene targets in the network regulated by each TF. Color indicates chromVAR deviation Z score as in (D). (F–G) Transcriptional regulatory networks (TRNs) for (F) MES-like state and (G) OPC/NPC-like state, showing top 50 upregulated genes and top 15 TFs in each TRN. TRNs represent all cells within the respective clusters across patients and time points. Diamond nodes represent TFs, and circle nodes represent target genes. Node size is proportional to the average gene expression for target genes and average chromVAR Z score for TFs. Node color is proportional to the average log 2 fold change of the gene in that cell state post-therapy across all cells. Edge line thickness is proportional to the linear regression coefficient for the predicted enhancer-promoter interaction and the fraction of cells with chromatin accessibility at the enhancer peak.$
Figure Legend Snippet: Transcriptional regulation of pHGG neoplastic cell states (A) Stacked bar plot of cell-type proportions of neoplastic cell states across dataset comparing initial resection and post-therapy samples. AC, astrocyte; GPC, glial progenitor cell; MES, mesenchymal; OC, oligodendrocyte; OPC, oligodendrocyte progenitor cell; NPC, neural progenitor cell; NEU, neural. (B) Representative ATAC signal tracks of FGFR1 locus and gene expression across cell states and time points. The link track represents the predicted enhancer-promoter interactions colored by the regression coefficient, and the TF motifs present at the enhancer peaks are indicated. Differentially accessible peaks between time points are highlighted. ∗∗∗ p < 0.0001, significance in gene expression or accessibility in FGFR1 ; ns, not significant, using one-sided Wilcoxon rank-sum test. (C) Shifts in neoplastic cell state proportions for each patient between initial resection and post-therapy time points in snATAC-seq ( n = 11 paired samples). Post-therapy samples were merged for one patient with three longitudinal samples. A two-sided Wilcoxon signed-rank test for paired samples was used. (D) Heatmap of differential transcription factor (TF) motif accessibility in each pHGG neoplastic cell state. Values are Z score-normalized deviation scores calculated using chromVAR. The differential TF accessibility analysis was performed by a Wilcoxon rank-sum test, comparing chromVAR deviation score between each cell state and the other cell states. The top 20 differential TFs are displayed for each state. (E) Overview of top 15 significant transcriptional regulators for each neoplastic cell state based on predicted enhancer-promoter interactions and TF-target gene pairs. The size of the dot indicates the fraction of the total gene targets in the network regulated by each TF. Color indicates chromVAR deviation Z score as in (D). (F–G) Transcriptional regulatory networks (TRNs) for (F) MES-like state and (G) OPC/NPC-like state, showing top 50 upregulated genes and top 15 TFs in each TRN. TRNs represent all cells within the respective clusters across patients and time points. Diamond nodes represent TFs, and circle nodes represent target genes. Node size is proportional to the average gene expression for target genes and average chromVAR Z score for TFs. Node color is proportional to the average log 2 fold change of the gene in that cell state post-therapy across all cells. Edge line thickness is proportional to the linear regression coefficient for the predicted enhancer-promoter interaction and the fraction of cells with chromatin accessibility at the enhancer peak.

Techniques Used: Gene Expression

Identifying therapeutic sensitivities through in vitro drug screening (A) A linear mixed model was used to identify differentially expressed genes within neoplastic cells overall between initial resection and post-therapy time points accounting for individual patient variability. Volcano plot shows the log fold change and adjusted p value for each gene included in the model, with selected genes labeled. Log fold change >0.5 and adjusted p value < 0.05 are indicated with dashed lines. (B) Gene set enrichment analysis (GSEA) of Hallmark (H) and KEGG (K) pathways across all genes in (A) ranked by log fold change. (C) Schematic of radiation experiment. Cells were treated with 4 Gy of ionizing radiation and allowed to recover for 4 weeks before undergoing bulk RNA sequencing. (D) GSEA of post-radiation changes using analogous GLMM as in (B) using three pHGG cell lines. Significant pathways shared with (B) are shown and highlighted in red in both panels. (E) Top gene targets by aggregate ranking score. Criteria include screening against drug databases, LINCS1000 compound perturbations, DepMap, differential gene expression, and participation in ligand-receptor signaling as a receptor target. (F) Selected growth curves from in vitro drug screening in human pHGG cell lines grown in spheroid culture. Cells were treated with drugs at indicated concentrations, and growth was monitored using a fluorescent reporter over 72 h of drug treatment ( n = 24 control, 2 drug-treated replicates each). y axis indicates the log2 fold change of total fluorescence signal from the zero time point. Positive values indicate a net proliferation, while negative values indicate net cell death. IC 50 values (in nM) are indicated for each drug and cell line. Data are shown as mean ± SEM with n = 2 replicates per condition. (G) Synergy scores (zero interaction potency) for combinations of trametinib and navitoclax across concentrations. Median ZIP scores are 18.1 (913 cell line), 10.5 (195 cell line), −1.49 (1763 cell line), and 0.82 (3058 cell line).

Figure Legend Snippet: Identifying therapeutic sensitivities through in vitro drug screening (A) A linear mixed model was used to identify differentially expressed genes within neoplastic cells overall between initial resection and post-therapy time points accounting for individual patient variability. Volcano plot shows the log fold change and adjusted p value for each gene included in the model, with selected genes labeled. Log fold change >0.5 and adjusted p value < 0.05 are indicated with dashed lines. (B) Gene set enrichment analysis (GSEA) of Hallmark (H) and KEGG (K) pathways across all genes in (A) ranked by log fold change. (C) Schematic of radiation experiment. Cells were treated with 4 Gy of ionizing radiation and allowed to recover for 4 weeks before undergoing bulk RNA sequencing. (D) GSEA of post-radiation changes using analogous GLMM as in (B) using three pHGG cell lines. Significant pathways shared with (B) are shown and highlighted in red in both panels. (E) Top gene targets by aggregate ranking score. Criteria include screening against drug databases, LINCS1000 compound perturbations, DepMap, differential gene expression, and participation in ligand-receptor signaling as a receptor target. (F) Selected growth curves from in vitro drug screening in human pHGG cell lines grown in spheroid culture. Cells were treated with drugs at indicated concentrations, and growth was monitored using a fluorescent reporter over 72 h of drug treatment ( n = 24 control, 2 drug-treated replicates each). y axis indicates the log2 fold change of total fluorescence signal from the zero time point. Positive values indicate a net proliferation, while negative values indicate net cell death. IC 50 values (in nM) are indicated for each drug and cell line. Data are shown as mean ± SEM with n = 2 replicates per condition. (G) Synergy scores (zero interaction potency) for combinations of trametinib and navitoclax across concentrations. Median ZIP scores are 18.1 (913 cell line), 10.5 (195 cell line), −1.49 (1763 cell line), and 0.82 (3058 cell line).

Techniques Used: In Vitro, Drug discovery, Labeling, RNA Sequencing, Gene Expression, Control, Fluorescence

Image Search Results

Journal: Cell Reports Medicine

Article Title: A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma

doi: 10.1016/j.xcrm.2026.102766

Figure Lengend Snippet: Longitudinal single-cell RNA and ATAC atlas of pediatric high-grade glioma (pHGG) (A) Overview of the multiomics studies on patient-matched longitudinal pHGG specimens. (B and C) Uniform manifold approximation and projection (UMAP) of (B) snRNA-seq data (401,253 cells) and (C) snATAC-seq data (118,736 cells) annotated by major cell type category (left) and stacked bar plot of cell-type proportions across dataset comparing initially resected pHGG samples with post-therapy samples. (D) Cell-type proportions in snRNA-seq data across each patient and therapeutic time point, along with a summary of patient demographics and molecular subtype. (E and F) Shifts in cell-type proportions for each patient between initial resection and post-therapy time points in (E) snRNA-seq ( n = 14 initial/post-therapy paired samples) and (F) snATAC-seq ( n = 11 paired samples). Post-therapy samples were merged for one patient with three longitudinal samples. A two-sided Wilcoxon signed-rank test for paired samples was used.

Article Snippet: Chromium Next GEM Single Cell Multiome ATAC + GEX Kit , 10x Genomics , Cat# PN-1000283.

Techniques: Single Cell

Journal: Cell Reports Medicine

Article Title: A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma

doi: 10.1016/j.xcrm.2026.102766

Figure Lengend Snippet: Transcriptional regulation of pHGG neoplastic cell states (A) Stacked bar plot of cell-type proportions of neoplastic cell states across dataset comparing initial resection and post-therapy samples. AC, astrocyte; GPC, glial progenitor cell; MES, mesenchymal; OC, oligodendrocyte; OPC, oligodendrocyte progenitor cell; NPC, neural progenitor cell; NEU, neural. (B) Representative ATAC signal tracks of FGFR1 locus and gene expression across cell states and time points. The link track represents the predicted enhancer-promoter interactions colored by the regression coefficient, and the TF motifs present at the enhancer peaks are indicated. Differentially accessible peaks between time points are highlighted. ∗∗∗ p < 0.0001, significance in gene expression or accessibility in FGFR1 ; ns, not significant, using one-sided Wilcoxon rank-sum test. (C) Shifts in neoplastic cell state proportions for each patient between initial resection and post-therapy time points in snATAC-seq ( n = 11 paired samples). Post-therapy samples were merged for one patient with three longitudinal samples. A two-sided Wilcoxon signed-rank test for paired samples was used. (D) Heatmap of differential transcription factor (TF) motif accessibility in each pHGG neoplastic cell state. Values are Z score-normalized deviation scores calculated using chromVAR. The differential TF accessibility analysis was performed by a Wilcoxon rank-sum test, comparing chromVAR deviation score between each cell state and the other cell states. The top 20 differential TFs are displayed for each state. (E) Overview of top 15 significant transcriptional regulators for each neoplastic cell state based on predicted enhancer-promoter interactions and TF-target gene pairs. The size of the dot indicates the fraction of the total gene targets in the network regulated by each TF. Color indicates chromVAR deviation Z score as in (D). (F–G) Transcriptional regulatory networks (TRNs) for (F) MES-like state and (G) OPC/NPC-like state, showing top 50 upregulated genes and top 15 TFs in each TRN. TRNs represent all cells within the respective clusters across patients and time points. Diamond nodes represent TFs, and circle nodes represent target genes. Node size is proportional to the average gene expression for target genes and average chromVAR Z score for TFs. Node color is proportional to the average log 2 fold change of the gene in that cell state post-therapy across all cells. Edge line thickness is proportional to the linear regression coefficient for the predicted enhancer-promoter interaction and the fraction of cells with chromatin accessibility at the enhancer peak.

Article Snippet: Chromium Next GEM Single Cell Multiome ATAC + GEX Kit , 10x Genomics , Cat# PN-1000283.

Techniques: Gene Expression

Journal: Cell Reports Medicine

Article Title: A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma

doi: 10.1016/j.xcrm.2026.102766

Figure Lengend Snippet: Identifying therapeutic sensitivities through in vitro drug screening (A) A linear mixed model was used to identify differentially expressed genes within neoplastic cells overall between initial resection and post-therapy time points accounting for individual patient variability. Volcano plot shows the log fold change and adjusted p value for each gene included in the model, with selected genes labeled. Log fold change >0.5 and adjusted p value < 0.05 are indicated with dashed lines. (B) Gene set enrichment analysis (GSEA) of Hallmark (H) and KEGG (K) pathways across all genes in (A) ranked by log fold change. (C) Schematic of radiation experiment. Cells were treated with 4 Gy of ionizing radiation and allowed to recover for 4 weeks before undergoing bulk RNA sequencing. (D) GSEA of post-radiation changes using analogous GLMM as in (B) using three pHGG cell lines. Significant pathways shared with (B) are shown and highlighted in red in both panels. (E) Top gene targets by aggregate ranking score. Criteria include screening against drug databases, LINCS1000 compound perturbations, DepMap, differential gene expression, and participation in ligand-receptor signaling as a receptor target. (F) Selected growth curves from in vitro drug screening in human pHGG cell lines grown in spheroid culture. Cells were treated with drugs at indicated concentrations, and growth was monitored using a fluorescent reporter over 72 h of drug treatment ( n = 24 control, 2 drug-treated replicates each). y axis indicates the log2 fold change of total fluorescence signal from the zero time point. Positive values indicate a net proliferation, while negative values indicate net cell death. IC 50 values (in nM) are indicated for each drug and cell line. Data are shown as mean ± SEM with n = 2 replicates per condition. (G) Synergy scores (zero interaction potency) for combinations of trametinib and navitoclax across concentrations. Median ZIP scores are 18.1 (913 cell line), 10.5 (195 cell line), −1.49 (1763 cell line), and 0.82 (3058 cell line).

Article Snippet: Chromium Next GEM Single Cell Multiome ATAC + GEX Kit , 10x Genomics , Cat# PN-1000283.

Techniques: In Vitro, Drug discovery, Labeling, RNA Sequencing, Gene Expression, Control, Fluorescence

A) Schematic of the YBX1 knock-down (KD) workflow in hCOs. Three stem cell lines (UCLA1 hESC, UCLA6 hESC, KOLF iPSC) were infected with a YBX1-targeting shRNA (EGFP-labeled). Cortical organoids were generated from each cell line, which, after 18 days, were dissociated and re-aggregated together in the presence of YBX1 shRNA lentivirus (Anton-Bolanos, et al., 2024; Nano et al., 2025). The resulting chimeroids were grown for 8 weeks in culture, at which time EGFP+ (YBX1-KD) and EGFP– (unperturbed) cells were isolated by FACS and captured for single-cell multi-omic profiling (simultaneous scRNA-seq and scATAC-seq). Representative immunofluorescence images of 5-week-old chimeroids show YBX1 shRNA (EGFP, green), SOX2 (DAPI), and CTIP2 (DAPI), confirming cortical identity. Scale bar = 300 μm. B) YBX1 expression is significantly and consistently depleted in YBX1-KD cells across all three cell lines. (Top) UMAP projection from both RNA and ATAC modalities (UMAP WNN) of YBX1-KD hCO cells colored by perturbation condition. (Bottom) YBX1 expression in unperturbed versus YBX1-KD cells for each cell line, summarized with boxplots (two-sided Wilcoxon test). C) Loss of YBX1 favors deep layer fate over upper layer fate. (Top) UMAP (WNN) projection of cells colored by cell type, with the Deep Layer Excitatory Neuron cluster highlighted. (Bottom) Percent change in cell type proportion in YBX1-KD versus unperturbed cells for each cell type. Individual dots represent each cell line, data summarized by boxplot. Dashed line at 0 indicates no change. D) Loss of YBX1 activates neuronal gene programs in both radial glia and deep layer neurons. Dot plots show the percent change in activity of developmental meta-modules (Nano et al., 2025) in YBX1-KD versus unperturbed cells in radial glia (top) and deep layer excitatory neurons (bottom), colored by biological process. Module 20, associated with deep layer fate (Nano et al., 2025), is among the most elevated modules in both cell types. Only statistically significant effects are shown (p < 0.05, two-sided Wilcoxon test). E) YBX1 depletion attenuates most PFC signatures across the excitatory neuronal lineage. Bar and error bars show the average percent change in PFC signature activity in YBX1-KD versus unperturbed cells across radial glia, IPC, deep layer excitatory neurons, and upper layer excitatory neurons. Values for each individual cell line shown as dots. Only statistically significant effects are shown (p < 0.05, two-sided Wilcoxon test). F) Across the glutamatergic lineage, YBX1 is required for the majority of cell type-specific PFC signatures. Pie charts summarizing the number of cell type-specific PFC signatures that are dependent (pink) versus independent (teal) of YBX1 in each cell type. White indicates signatures that are not enriched in the PFC within the indicated cell type. G) YBX1 depletion induces broad shifts in chromatin accessibility, with effects significantly amplifying from radial glia to deep layer excitatory neurons (two-sided Wilcoxon test). Dots represent significantly differentially accessible chromatin peaks between unperturbed and YBX1-KD cells within radial glia and deep layer excitatory neurons, plotted based on fold-change in accessibility (p < 0.05, logistic regression test). Data are colored by YBX1-dependent regions (less accessible in YBX1-KD), YBX1-repressed regions (more accessible in YBX1-KD), and relatively unchanged regions (less than 25% change in accessibility, grey dots). Data summarized by boxplot. H) Loss of YBX1 modestly decreases the chromatin accessibility of PFC signatures. The average promoter region accessibility in each PFC signature was calculated per cell. Data show the percent change in this “PFC signature chromatin accessibility” in YBX1-KD vs unperturbed cells. Comparisons were conducted within radial glia and deep layer neurons, focusing solely on the PFC signatures relevant to each cell type. Dots indicate the percent-change in average chromatin accessibility for each cell type-specific signature, summarized by boxplots. P-value calculated by two-sided Wilcoxon test. I) YBX1 regulates PFC signatures at both the chromatin and transcriptional level in radial glia, but acts predominantly as a transcriptional regulator in deep layer neurons. Dot plot displays the percent change induced by YBX1-KD in each cell type-specific PFC signature, both in terms of average chromatin accessibility (blue) and gene expression (green). Dashed line at 0 indicates no change. J–K) In YBX1-sensitive PFC signatures shared between radial glia and deep layer neurons (J), YBX1 is required to open chromatin in radial glia but shifts to a predominantly transcriptional role in deep layer neurons – a cascade not observed in non-YBX1-sensitive signatures (K). Bar and error bars show, for the indicated PFC signatures, the average percent change in chromatin accessibility (blue) and gene expression (green) induced by YBX1-KD. Effects in radial glia and deep layer neurons are shown, with the values from individual cell lines shown as dots.

Journal: bioRxiv

Article Title: Intrinsic coordination of dynamic molecular signatures shape the human prefrontal cortex

doi: 10.64898/2026.05.13.724991

Figure Lengend Snippet: A) Schematic of the YBX1 knock-down (KD) workflow in hCOs. Three stem cell lines (UCLA1 hESC, UCLA6 hESC, KOLF iPSC) were infected with a YBX1-targeting shRNA (EGFP-labeled). Cortical organoids were generated from each cell line, which, after 18 days, were dissociated and re-aggregated together in the presence of YBX1 shRNA lentivirus (Anton-Bolanos, et al., 2024; Nano et al., 2025). The resulting chimeroids were grown for 8 weeks in culture, at which time EGFP+ (YBX1-KD) and EGFP– (unperturbed) cells were isolated by FACS and captured for single-cell multi-omic profiling (simultaneous scRNA-seq and scATAC-seq). Representative immunofluorescence images of 5-week-old chimeroids show YBX1 shRNA (EGFP, green), SOX2 (DAPI), and CTIP2 (DAPI), confirming cortical identity. Scale bar = 300 μm. B) YBX1 expression is significantly and consistently depleted in YBX1-KD cells across all three cell lines. (Top) UMAP projection from both RNA and ATAC modalities (UMAP WNN) of YBX1-KD hCO cells colored by perturbation condition. (Bottom) YBX1 expression in unperturbed versus YBX1-KD cells for each cell line, summarized with boxplots (two-sided Wilcoxon test). C) Loss of YBX1 favors deep layer fate over upper layer fate. (Top) UMAP (WNN) projection of cells colored by cell type, with the Deep Layer Excitatory Neuron cluster highlighted. (Bottom) Percent change in cell type proportion in YBX1-KD versus unperturbed cells for each cell type. Individual dots represent each cell line, data summarized by boxplot. Dashed line at 0 indicates no change. D) Loss of YBX1 activates neuronal gene programs in both radial glia and deep layer neurons. Dot plots show the percent change in activity of developmental meta-modules (Nano et al., 2025) in YBX1-KD versus unperturbed cells in radial glia (top) and deep layer excitatory neurons (bottom), colored by biological process. Module 20, associated with deep layer fate (Nano et al., 2025), is among the most elevated modules in both cell types. Only statistically significant effects are shown (p < 0.05, two-sided Wilcoxon test). E) YBX1 depletion attenuates most PFC signatures across the excitatory neuronal lineage. Bar and error bars show the average percent change in PFC signature activity in YBX1-KD versus unperturbed cells across radial glia, IPC, deep layer excitatory neurons, and upper layer excitatory neurons. Values for each individual cell line shown as dots. Only statistically significant effects are shown (p < 0.05, two-sided Wilcoxon test). F) Across the glutamatergic lineage, YBX1 is required for the majority of cell type-specific PFC signatures. Pie charts summarizing the number of cell type-specific PFC signatures that are dependent (pink) versus independent (teal) of YBX1 in each cell type. White indicates signatures that are not enriched in the PFC within the indicated cell type. G) YBX1 depletion induces broad shifts in chromatin accessibility, with effects significantly amplifying from radial glia to deep layer excitatory neurons (two-sided Wilcoxon test). Dots represent significantly differentially accessible chromatin peaks between unperturbed and YBX1-KD cells within radial glia and deep layer excitatory neurons, plotted based on fold-change in accessibility (p < 0.05, logistic regression test). Data are colored by YBX1-dependent regions (less accessible in YBX1-KD), YBX1-repressed regions (more accessible in YBX1-KD), and relatively unchanged regions (less than 25% change in accessibility, grey dots). Data summarized by boxplot. H) Loss of YBX1 modestly decreases the chromatin accessibility of PFC signatures. The average promoter region accessibility in each PFC signature was calculated per cell. Data show the percent change in this “PFC signature chromatin accessibility” in YBX1-KD vs unperturbed cells. Comparisons were conducted within radial glia and deep layer neurons, focusing solely on the PFC signatures relevant to each cell type. Dots indicate the percent-change in average chromatin accessibility for each cell type-specific signature, summarized by boxplots. P-value calculated by two-sided Wilcoxon test. I) YBX1 regulates PFC signatures at both the chromatin and transcriptional level in radial glia, but acts predominantly as a transcriptional regulator in deep layer neurons. Dot plot displays the percent change induced by YBX1-KD in each cell type-specific PFC signature, both in terms of average chromatin accessibility (blue) and gene expression (green). Dashed line at 0 indicates no change. J–K) In YBX1-sensitive PFC signatures shared between radial glia and deep layer neurons (J), YBX1 is required to open chromatin in radial glia but shifts to a predominantly transcriptional role in deep layer neurons – a cascade not observed in non-YBX1-sensitive signatures (K). Bar and error bars show, for the indicated PFC signatures, the average percent change in chromatin accessibility (blue) and gene expression (green) induced by YBX1-KD. Effects in radial glia and deep layer neurons are shown, with the values from individual cell lines shown as dots.

Article Snippet: Multiomic libraries were generated using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Kit (10X Genomics, CG000338 Rev F) according to manufacturer instructions, using 7-8 cycles for ATAC library amplification, 6-7 cycles for cDNA amplification, and 10-14 cycles for gene expression library amplification depending on input DNA amount.

Techniques: Knockdown, Infection, shRNA, Labeling, Generated, Isolation, Single Cell, Immunofluorescence, Expressing, Activity Assay, Gene Expression

Review

Structured Review

Images

1) Product Images from "A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma"

Similar Products

Image Search Results