Dataset Information

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

ABSTRACT: 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE328367 | GEO | 2026/07/22

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:We employed CITE-Seq profiling of 62 277 high-quality FACS-enriched human bone marrow hematopoietic stem and progenitor cells (HSPCs) from 15 healthy donors representing young, mid and old age groups, using the BD Rhapsody platform. Our targeted gene panel consisting of 596 genes was supplemented with 46 cell-surface proteins to cover the most relevant markers of early HSPC differentiation and cell cycle status. We demonstrate a molecular map of early human hematopoietic stem cell (HSC) differentiation and provide evidence of early branching points into lineage trajectories across different age groups. Our data shows that the HSC branched into two main lineages, with the first branching point giving rise to megakaryocytic and erythroid progenitors (MKP/ERP), followed by lymphoid and myeloid progenitors (LMP/MDP). As expected, the most immature HSCs were defined by high expression of CD34, CD90, CRHBP, HOPX, HLF, MLLT3. Re-clustering and trajectory analysis of the most immature HSPCs confirmed early lineage commitment, suggesting that an MKP/ERP development does not include myeloid or lymphoid progenitors. A similar pattern was observed for all age groups. Interestingly, we observed increased expression of novel marker genes (HLA-E, DLK1, ADGRG6, and MMRN1) and the surface protein CD273/PD-L2 in the most immature HSCs. Besides a gradual upregulation of CD38 along differentiation, differentiated cells showed lower CD45 levels. tradeSeq analysis defined the continuous single cell expression changes in genes upon early differentiation and lineage commitment, allowing the detection of decision-making gene networks. Our data adds further evidence to a structured hierarchical organization of human hematopoiesis with a sequential loss of lineage potential, with the first branching point into the MEK/ERY lineage, and not an early commitment of uni-lineage restricted precursors. We report on the upregulation of ADGRG6, DLK1, HLA-E, MMRN1 and CD273 in the most immature HSCs. Functional experiments confirmed that CD273+ HSCs displayed the most quiescent profile with a high stemness signature as well as the immune-modulatory function of CD273+ on HSPCs in regulating T cell activation and cytokine release.

Project description:During adult bone marrow hematopoiesis, extremely rare and dormant hematopoietic stem cells (HSCs) harbor the highest self-renewal activity within all blood cells. They give rise to active HSCs, which generate multipotent progenitors (MPPs) which differentiate into lineage-committed progenitors and subsequently mature cells. While HSCs are characterized by long-term self-renewal capacity, quiescence and multipotency, MPPs show steadily decreasing self-renewal activity, are cycling but are thought to maintain multipotency. To establish a comprehensive genome-wide landscape of expressed transcripts, we performed a quantitative transcriptome analysis by next-generation sequencing (RNA-seq) of seven ex vivo FACS-sorted mouse HSC/progenitor populations. Eleven-fold coverage of the genome was achieved, revealing quantification of > 27,000 mRNA species of which 589 long non-coding RNAs (lncRNAs) were quantified. A profile of 79 differentially expressed lncRNAs in HSC-MPPs was identified suggesting a role for these RNA species in HSC/progenitor biology. Expression clusters of transcription factors and cell adhesion molecules are identified between the different cell populations. Dormant HSCs, as identified by label-retaining assays, showed a highly differential expression profile compared to active HSCs. In addition to >200 differentially expressed cell surface receptors and lncRNAs, processes including metabolism, development, immune response, signaling (TGFb, Kit, senescence/autophagy) are distinct between the two types of HSCs. In addition, using whole cell proteome analysis of FACS-sorted HSCs and MPP1, >6,000 proteins were identified by quantitative tandem mass spectrometry. Quantification of these proteins confirmed the close relationship between these cell types also seen in their transcript profile and revealed processes such as energy metabolism, immune response and cell cycle to be modulated along early lineage progression. While MPP1/2 still show multilineage potential in reconstitution experiments, a strong lineage bias and low self-renewal potential is observed in mice reconstituted with MPP3/4. These functional differences are accompanied by complex changes in their transcriptome and is also revealed by principal component analysis. In summary, the global mRNA, lncRNA and proteome signatures uncovered here and which are complemented by functional assays, provide a comprehensive and searchable resource of the molecular make-up of the entire HSC/progenitor population present in the bone marrow. These data will provide the basis for a global understanding of stem cell biology in the adult blood system. The uploaded dataset corresponds to the quantitative proteomic comparison of HSC and MPP1, which was done in three biological replicates. Data analysis: MS raw data files were processed with MaxQuant (version 1.3.0.5) (Cox and Mann 2008). Enzyme specificity was set to trypsin/P and a maximum of two missed cleavages were allowed. Cysteine carbamidomethylation and methionine oxidation were selected as fixed and variable modifications, respectively. The derived peak list was searched using the built-in Andromeda search engine (version 1.3.0.5) in MaxQuant against the Uniprot mouse database (2013.02.20) containing 75,721 proteins to which 247 frequently observed contaminants as well as reversed sequences of all entries had been added. Initial maximal allowed mass tolerance was set to 20 ppm for peptide masses, followed by 6 ppm in the main search, and 0.5 Dalton for fragment ion masses. The minimum peptide length was set to six amino acid residues and three labeled amino acid residues were allowed. A 1% false discovery rate (FDR) was required at both the protein level and the peptide level. In addition to the FDR threshold, proteins were considered identified if they had at least one unique peptide. The protein identification was reported as an indistinguishable “protein group” if no unique peptide sequence to a single database entry was identified. The ‘match between runs’ was enabled for consecutive peptide fractions with a 2 minutes time window. The iBAQ algorithm was used for estimation of the abundance of different proteins within a single sample (proteome) (Schwanhausser 2011). For evaluation of differential protein expression between HSC and MPP1, statistical analysis was performed for the proteins quantified in all three replicates using the Limma package in R/Bioconductor (Gentleman 2004, Smyth 2004). After fitting a linear model to the data, an empirical Bayes moderated t-test was used for the protein ratios, which were weighted on log10(summed peptide intensities) in order to capture the effect that the statistical spread of unregulated proteins is much more focused for highly abundant proteins than for low abundance ones (Cox 2008). P-values were then adjusted for multiple testing with Benjamini and Hochberg's method and proteins with an adjusted p-value lower than 0.1 were considered to be differentially expressed between HSC and MPP1. Associated transcriptomics data has been deposited at ArrayExpress with accession E-MTAB-2262.

Dataset Information

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

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets