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