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Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.

ABSTRACT: Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, it has proven difficult to generate functional hematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we explored single-cell RNA sequencing (scRNAseq) in combination with single-cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors and erythroid-, megakaryocyte-, and leukocyte-committed progenitors, and we identified CD44, CD326, ICAM2/CD9, and CD18, respectively, as markers of these progenitors. Using an artificial neural network that we trained on scRNAseq derived from human fetal liver, we identified a wide range of hPSC-derived HSPCs phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded, and was completely missing in the data set that had been generated using cells selected on the basis of CD43 expression. By comparing the single-cell transcriptome of in vitro-generated HSC-like cells with those generated within the fetal liver, we identified transcription factors and molecular pathways that can be explored in the future to improve the in vitro production of HSCs.

SUBMITTER: Fidanza A

PROVIDER: S-EPMC7862875 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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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Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.

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