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Multiome-based identification of HSC subtype markers shows that ageing, but not inflammatory stress, increases HSC platelet bias

GSE328367 Mus musculus Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Other 3 samples Submitted 2026/07/22 Platform GPL21103
Summary
Individual haematopoietic stem cells (HSCs) display heterogeneous capacities to reconstitute the major blood lineages, including platelets, erythrocytes, myeloid cells, B cells, and T cells. Single-cell transplantation assays have revealed that the HSC compartment contains lineage-restricted subtypes capable of reconstituting only specific lineages, such as platelets, alongside true multi-lineage HSCs. The proportion of these lineage-restricted HSCs increases with age, contributing to declined immunity and potentially to clonal haematopoiesis. To date, their identification and isolation have primarily relied on transgenic reporters, such as the Vwf-EGFP mouse line, which offer only moderate accuracy. In this study, we profiled highly purified adult mouse long-term HSCs (LT-HSCs; LSK CD48⁻ CD150⁺ CD34⁻) using the DOGMAseq platform, which enables simultaneous measurement of transcriptome (RNA-seq), chromatin accessibility (ATAC-seq), and surface protein expression (CITE-seq). We subsequently identified candidate clusters corresponding to platelet-biased and multi-lineage HSCs, and developed a fluorescence-activated cell sorting (FACS) strategy to enrich these populations. This strategy employs four surface markers detectable with commercially available antibodies and achieves higher enrichment specificity than the Vwf-EGFP reporter.
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Direct links to NCBI, no account and no request form: the whole study as GSE328367_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 3 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1454860 and SRA study SRP692722. Searching any of these in the dataset finder brings you back here.

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