The complete methylome of hematopoietic stem cells and their immediate progeny
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE52709 | GEO | 2014/08/25
SECONDARY ACCESSION(S): PRJNA229871
REPOSITORIES: GEO
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Project description:Epigenetic mechanisms regulate the multilineage differentiation capacity of hematopoietic stem cells (HSCs) into a variety of blood and immune cells. Our recent work revealed evidence of multilineage gene priming in HSCs, where open cis-regulatory elements (CREs) exclusively shared between HSCs and unipotent lineage cells were enriched for DNA binding motifs of known lineage-specific transcription factors. To test the hypothesis that HSC-primed lineage-specific CREs remain accessible throughout differentiation, we used ATAC-seq to map the temporal dynamics of chromatin remodeling during blood cell differentiation. We observed epigenetic-driven clustering of oligopotent and unipotent progenitors into distinct erythromyeloid and lymphoid branches, with multipotent HSCs and MPPs associating with the erythromyeloid lineage. We mapped the dynamics of lineage-primed CREs throughout hematopoiesis and identified both unique and shared CREs as potential lineage reinforcement mechanisms at fate branch points in hematopoiesis. These findings provide insight into the regulation of stem cell multipotency and lineage commitment throughout hematopoiesis and serve as a resource to test functional drivers of hematopoietic lineage fate.
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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.
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Project description:Integration of index sorting and single cell functional assays allowed identification of novel haematopoietic stem cell (HSC) and multiprogenitor subsets (MPP) that differ in their lineage differentiation potential in vitro and in vivo, cell cycle properties and long-term repopulation capacity in the NSG xenograft model. Here we report single cell transcriptomes of CD49f+ HSCs as well as those of CD49f+ Subset1 (CD19- CD38- CD45RA- CD90+ CD49f+ CD34lo CLEC9Ahi, Myelo-erythroid-skewed in vitro but Lymphoid-competent) and CD49f+ Subset2 cells (CD19- CD38- CD45RA- CD90+ CD49f+ CD34hi CLEC9Alo, Myelo-Lymphoid competent but Erythroid-deficient). We also report bulk transcriptomes of pools of 20 cells from HSC/MPP Subset1 (CD19- CD38- CD45RA- CD34lo CLEC9Ahi) and HSC/MPP Subset2 (CD19- CD38- CD45RA- CD34hi CLEC9Alo). Altogether these data show a diffuse transcriptional landscape of the CD49f+ HSC compartment, which is polarised along an axis that separates Myelo-Erythroid and Myelo-Lymphoid lineage-priming. Consistently with their differentiation capacity in vitro, CD49f+ Subset1 cells cluster at the Myelo-Erythroid end of the landscape, while CD49f+ Subset2 cells cluster at the Myelo-Lymphoid end. In addtion, these lineage-priming signatures were found to be more marked in HSC/MPP Subset1 and HSC/MPP Subset2, than in the equivalent CD49f+ subsets. In conclusion, 49f+ Subset1 and 49f+ Subset2 populations have activated distinct transcriptional lineage-priming programmes corresponding to the phenotypic lineage-skewing observed in vitro, that then become reinforced within the broader HSC/MPP pool. Altogether our data shows that lineage-priming and lineage-restriction programmes are initially established within the CD49f+ HSC subset in humans.
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