Journal: HemaSphere

Article Title: Distinct stem cell identities converge into shared erythroid stress in ERCC6L2 disease and Shwachman–Diamond syndrome

doi: 10.1002/hem3.70374

Figure Lengend Snippet: Data summary. (A) Schematic of sample and data processing. Numbers denote the number of samples; amplification of TP53 loci enabled targeted genotyping of known TP53 mutation sites (Supporting Information S2: Methods, ). Image created with BioRender. (B) ERCC6L2 disease (ED) and Shwachman–Diamond (SDS) blood and bone marrow (BM) samples included in this study, depicted at their different stages of disease. Numbers denote the number of samples obtained from 10 ED and 5 SDS patients for BM, and from 12 ED and 5 SDS patients for blood. Colors depict the type of samples (BM or blood). Samples with “no TP53 ” denote samples without somatic TP53 mutations, and other samples depict cases with 1–4 TP53 mutations. (C) Detected cell types of our integrated single‐cell transcriptomics data. (D) TP53 mutation status of cells. Numbers denote the number of cells that were identified as TP53 ‐mutated or TP53 wild‐type. AML, acute myeloid leukemia; BMF, bone marrow failure; HC, healthy control; HSC, hematopoietic stem cell; LMP, lymphomyeloid progenitor; MDS, myelodysplastic syndrome; MPP, multipotent progenitor cell; VAF, variant allele fraction.

Article Snippet: For BM samples, we applied single‐cell RNA sequencing (scRNA‐seq, 3′ 10X Genomics) and integrated the data using single‐cell variational inference (scVI).

Techniques: Amplification, Mutagenesis, Single-cell Transcriptomics, Control, Variant Assay

Journal: HemaSphere

Article Title: Distinct stem cell identities converge into shared erythroid stress in ERCC6L2 disease and Shwachman–Diamond syndrome

doi: 10.1002/hem3.70374

Figure Lengend Snippet: Transcriptional landscape of bone marrow (BM) erythroid progenitors, peripheral blood cells, and fibroblasts in ERCC6L2 disease (ED) compared to Shwachman–Diamond syndrome (SDS). (A) Comparison of ED bone marrow failure (BMF) and SDS BMF differentially expressed genes (DEGs) showing log 2 fold changes (log 2 FC) of expression in ED BMF ( n samples = 12) and SDS BMF ( n samples = 4) against healthy controls ( n samples = 63) in BM hematopoietic stem cell (HSC) and multipotent progenitor cell (MPP) ( n cells = 370; 30; 27 for healthy control, ED BMF and SDS BMF, respectively), erythroid–myeloid progenitor (EMP) ( n cells = 1579; 140; 62), early erythroid progenitor (EEP) ( n cells = 11,826; 777; 232), and late erythroid progenitor (LEP) ( n cells = 2422; 858; 385). Genes falling close to the diagonal exhibit similar magnitude and direction of differential expression in both diseases, whereas genes deviating from the diagonal reflect differences in the extent of dysregulation between ED and SDS. Blue points indicate genes concordantly regulated in both conditions (upregulated or downregulated relative to controls), while orange points indicate genes regulated in opposite directions between ED and SDS. (B) Top 10 non‐redundant pathways across cell types for BM. Enriched pathways were sorted by FDR‐adjusted P‐values P adj . Redundant pathways (pathways containing DEGs of which more than half of the DEGs are members of a pathway with a smaller P adj ) and pathways not enriched for one of the cell types were filtered out. From the remaining pathways, the top 10 based on the smallest P adj across cell types were plotted. (C) Hematopoietic‐ and erythroid‐specific pathway enrichment in ED BMF and SDS BMF. Reactome pathway enrichment analysis focusing on pathways related to hematopoiesis and erythropoiesis in bulk blood RNA‐seq data. Pathways were selected based on lineage relevance and the presence of multiple significantly differentially expressed genes, thereby excluding pathways driven by single‐gene effects. Shown are pathways significantly enriched in ED BMF and SDS BMF compared to healthy controls, with adjusted P‐values indicated. (D) Comparison of ED BMF and SDS BMF DEGs showing log 2 FC of expression in ED BMF ( n = 28) and SDS BMF ( n = 7) against healthy controls ( n = 11) in blood samples. (E) Top five enriched pathways in ED BMF and SDS BMF compared to healthy controls in blood samples. (F) Comparison of ED and SDS DEGs showing log 2 FC of expression in ED ( n = 74) and SDS ( n = 55) against healthy controls ( n = 68) in fibroblast samples. (G) Top five enriched pathways on ED and SDS compared to healthy controls in fibroblasts. FDR, false discovery rate; HC, healthy control; R , Pearson correlation coefficient. DEGs, genes with P adj < 0.05 in the differential expression (DE) analysis results. Enriched pathways, pathways with P adj < 0.05 in pathway analysis results. DEGs were obtained using MAST for BM in (A) and using DESeq2 for blood in (D) and fibroblast (F) and enriched pathways were obtained using enrichR for BM (B, C) , blood (E) , and fibroblasts (G) .

Article Snippet: For BM samples, we applied single‐cell RNA sequencing (scRNA‐seq, 3′ 10X Genomics) and integrated the data using single‐cell variational inference (scVI).

Techniques: Comparison, Expressing, Control, Quantitative Proteomics, RNA Sequencing

Journal: Infection and Drug Resistance

Article Title: The Role of Coicis Semen in Staphylococcus aureus -Induced Osteomyelitis: Bioinformatics Integrated with Experimental Validation

doi: 10.2147/IDR.S596872

Figure Lengend Snippet: Identification of overlapping genes among drug targets, disease-related genes, and GEO differentially expressed genes ( MPO and ITGB3 ) and Single-Cell Analysis. ( A ) The “drug–active ingredient–target gene–pathway–disease” network, illustrating the interactions among Coicis Semen , its active ingredients, candidate target genes, enriched signaling pathways, and osteomyelitis. ( B ) Venn diagram of the predicted target genes of Coicis Semen , osteomyelitis-related genes, and differentially expressed genes, with 3 overlapping common genes identified. ( C ) UMAP clustering plot of single-cell RNA sequencing data from bone marrow tissues of mice with S. aureus -induced osteomyelitis, identifying major cell populations including B cells, endothelial cells, macrophages, mast cells, monocytes, neutrophils, and T cells. ( D ) FeaturePlot showing the characteristic expression of Mpo and Itgb3 across different cell populations. ( E ) Expression heatmap of marker genes across different cell populations, applied for cell type annotation. ( F ) Violin plots displaying the expression distribution of Mpo and Itgb3 across different cell populations. Mpo was mainly enriched in neutrophils, while Itgb3 was primarily expressed in monocytes and also detected in partial macrophages.

Article Snippet: Single-cell RNA sequencing was performed by Singleron Biotechnologies Co., Ltd.

Techniques: Single-cell Analysis, Protein-Protein interactions, Single Cell, RNA Sequencing, Expressing, Marker

Journal: bioRxiv

Article Title: Loss of the Y chromosome drives epigenetic and transcriptomic plasticity in lung adenocarcinoma

doi: 10.64898/2026.06.02.729627

Figure Lengend Snippet: (A) Experimental overview illustrating the generation of isogenic single-cell LOY and ROY clones from parental A549 cells and the multi-omic analysis of clones and patients. (B) Representative WGS profiles of ROY (top, orange) and LOY (bottom, blue) clones. Left: zoomed-in view of the Y chromosome. Right: genome-wide Circos plots (right). (C) Volcano plot of differentially expressed genes between LOY (n=4) and ROY (n=4) clones (Y-linked genes excluded). X-axis: log2 fold change; y-axis: -log 10 (p-value). Blue: significantly upregulated genes in LOY; orange: downregulated in LOY. (D) Dot plot showing the top 6 Hallmark gene sets with FDR<0.25 from preranked GSEA of RNA-seq data. X-axis: normalized enrichment score (NES); y-axis: gene sets. Dot size indicates gene count; color represents -log 10 (FDR). (E) Heatmap of lead genes contributing to the EMT gene set enrichment. TPM values are z-scored across clones. (F) Dot plot of Hallmark gene sets from GSEA of full proteome data at 70% confluency (left) and 100% confluency (right). X-axis: normalized enrichment score (NES); y-axis: gene sets. Dot size indicates protein count; color represents -log 10 (FDR). ( G ) Confocal microscopy images of clones stained for N-cadherin (green). Nuclei are counterstained with DAPI (4′,6-diamidino-2-phenylindole, blue). Scale bar 20 µm (H) . Flow cytometry histograms of CD90 expression for LOY (blue) and ROY (orange) clones, with isotype controls shown. (I) Dot plot of top 10 Hallmark gene sets from preranked GSEA of TCGA LUAD samples (LOY vs. ROY). (J) Dot plot the top 8 positively enriched Hallmark gene sets from GSEA of the LuCA single-cell dataset (LOY vs. ROY tumor cells).

Article Snippet: The sorted CD45 + and CD45 neg cells derived from paired distal normal lung and lung adenocarcinoma samples were used for single-cell RNA sequencing analysis (10X Genomics, Single Cell 3′ Gene Expression v3.1).

Techniques: Single Cell, Clone Assay, Genome Wide, RNA Sequencing, Confocal Microscopy, Staining, Flow Cytometry, Expressing

Journal: bioRxiv

Article Title: Loss of the Y chromosome drives epigenetic and transcriptomic plasticity in lung adenocarcinoma

doi: 10.64898/2026.06.02.729627

Figure Lengend Snippet: (A) Experimental workflow for single-cell profiling of isogenic A549 ROY and LOY clones using Epi-CyTOF and scMultiome, alongside clinical data reanalysis . (B) Heatmap of Epi-CyTOF histone modification measurements. Values are z-scored across clones; blue: lower-than-average; red: higher-than-average levels. Hierarchical clustering was applied to histone marks and samples. (C) Boxplot showing the epigenetic heterogeneity metric derived from multidimensional Epi-CyTOF data. (D) Boxplot of EMT module scores from snRNA-seq, based on lead genes from . (E) Heatmaps of lead EMT gene expression (snRNA-seq, left) and corresponding chromatin accessibility at gene promoters (scATACseq, right). (F) Boxplot of transcriptional heterogeneity (QuoTHiC) in premalignant and tumor cells compared to normal AT2 cells in a cohort from the LuCA dataset , . (G) Boxplot comparing QuoTHiC scores across cell types and ROY/LOY status from the cohort shown in (F).

Article Snippet: The sorted CD45 + and CD45 neg cells derived from paired distal normal lung and lung adenocarcinoma samples were used for single-cell RNA sequencing analysis (10X Genomics, Single Cell 3′ Gene Expression v3.1).

Techniques: Single Cell, Clone Assay, Modification, Derivative Assay, Gene Expression

Journal: bioRxiv

Article Title: Loss of the Y chromosome drives epigenetic and transcriptomic plasticity in lung adenocarcinoma

doi: 10.64898/2026.06.02.729627

Figure Lengend Snippet: (A) Schematic of the in vitro phenotypic characterization of isogenic A549 ROY and LOY clones. (B) Dot plot showing the top 10 Hallmark gene sets with FDR<0.25 from preranked GSEA of RNA-seq data comparing A549 LOY and ROY under glucose deprivation. X-axis: normalized enrichment score (NES); y-axis: gene sets. Dot size indicates gene count; color represents -log 10 (FDR). ( C) Dot plot showing significant Hallmark gene sets from preranked GSEA of global proteome data comparing LOY vs. ROY clones under glucose (left) and glutamine (right) deprivation. Dot size indicates protein count; color represents -log 10 (FDR). (D) Colony formation capacity under glutamine deprivation. Left: Boxplot of mean colony number (n = 3 technical replicates/clone). Right: Representative crystal violet-stained images. (E ) Normalized dose-response curves of four LOY (blue) and four ROY (orange) clones assessed 24 hours post-irradiation (0-40 Gy). Viability was measured in triplicate using CellTiter-Blue and fitted using non-linear regression. (F) Clonogenic survival following 10 Gy. Left: boxplots showing mean colony counts across two independent experiments (n=3 replicates/clone/experiment). Right: representative images from experiment 1. Note: For all boxplots, the center line represents the median, box limits represent upper and lower quartiles, and whiskers represent minimum and maximum values. Statistical significance was assessed using an unpaired t-test (α=0.05).

Article Snippet: The sorted CD45 + and CD45 neg cells derived from paired distal normal lung and lung adenocarcinoma samples were used for single-cell RNA sequencing analysis (10X Genomics, Single Cell 3′ Gene Expression v3.1).

Techniques: In Vitro, Clone Assay, RNA Sequencing, Staining, Irradiation

Journal: bioRxiv

Article Title: Loss of the Y chromosome drives epigenetic and transcriptomic plasticity in lung adenocarcinoma

doi: 10.64898/2026.06.02.729627

Figure Lengend Snippet: (A) Boxplots displaying cell proliferation (BrdU ELISA) of four LOY (blue) and four ROY (orange) clones under normal growth conditions (glucose+, glutamine+), glucose deprivation (glucose-, glutamine+), or glutamine deprivation (glucose+, glutamine-). (B) Volcano plot of differentially expressed genes between LOY (n=4) and ROY (n=4) clones under glucose deprivation (top) and glutamine deprivation (bottom) (Y-linked genes excluded). X-axis: log2 fold change; y-axis: -log 10 (p-value). Blue: significantly upregulated genes in LOY; orange: downregulated in LOY. (C) Dot plot showing the top 2 Hallmark gene sets with FDR<0.25 from preranked GSEA of RNA-seq data comparing A549 LOY and ROY under glutamine deprivation. X-axis: normalized enrichment score (NES); y-axis: gene sets. Dot size indicates gene count; color represents -log 10 (FDR). (D) Volcano plots of differentially abundant proteins under glucose (left) and glutamine (right) deprivation. Blue: significantly upregulated in LOY; orange: downregulated in LOY. X-axis: log2 fold change; y-axis: -log 10 (p-value). (E) 10-day colony formation assay of four LOY (blue) and four ROY (orange) clones, standard growth conditions. Left: boxplot of mean colony number (n=3 technical replicates/clone). Right: representative images. (F) Normalized growth curves of four LOY (blue) and four ROY (orange) clones over 5 days following 10 Gy irradiation, fitted using non-linear regression. (G) Boxplot quantifying the area occupied by colonies after 10 Gy irradiation (experiment 1). (H) Boxplot quantifying the area occupied by non-irradiated controls (left) and representative images (right) from experiment 1. Box plots as in .

Article Snippet: The sorted CD45 + and CD45 neg cells derived from paired distal normal lung and lung adenocarcinoma samples were used for single-cell RNA sequencing analysis (10X Genomics, Single Cell 3′ Gene Expression v3.1).

Techniques: Enzyme-linked Immunosorbent Assay, Clone Assay, RNA Sequencing, Colony Assay, Irradiation

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