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

Abstract

<jats:p>Haematopoietic stem and progenitor cells (HSPCs) develop through distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) is able to recapitulate some of these processes, however, it has proven difficult to generate functional haematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we exploited single cell RNA sequencing (scRNAseq) in combination with single cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors as well as erythroid, megakaryocyte and leukocyte-committed progenitors and we identified CD44, CD326, ICAM2/CD9 and CD18 as markers of these progenitors, respectively. Using an artificial neural network (ANN), that we trained on a scRNAseq derived from human fetal liver, we were able to identify a wide range of hPSCs-derived HPSC phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded and was completely missing in the dataset that had been generated using cells selected on the basis of CD43expression. 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 exploited in the future to improve the in vitro production of HSCs.</jats:p>

Multi-Modal Profiling of Human Fetal Liver-Derived Hematopoietic Stem Cells Reveals the Molecular Signature of Engraftment Potential

“…Notably, a recent study profiling PSC-derived HSPCs at the single cell level, identified ID2 as enriched in what was considered the most naïve in vitro-derived HSPC fraction, highlighting an encouraging overlap in gene expression with engraftable HSCs in vivo (Fidanza et al, 2020). In this work, the transcriptomic profile of PSC-derived HSPCs was compared to that from FL HSPCs using an artificial neural network, identifying additional commonalities but also dissimilarities that might suggest how to further improve PSC-based HSC generation.…”

Single-Cell RNA Sequencing to Disentangle the Blood System

“… 77 When a reference dataset is available, automated cell type annotation can reveal gene expression differences between normal and perturbed tissues that are not compromised by developmental state heterogeneity or contaminating cell types. For example, (1) in acute myeloid leukemia patient samples, we used a Random Forest algorithm to classify malignant cell states by their similarity to annotated cell types from healthy donors, 47 (2) in pluripotent stem cell–derived cultures, Artificial Neural Networks identified hematopoietic stem cell–like cells similar to human fetal liver hematopoietic stem cells, 78 and (3) using a community clustering strategy, alignment of Gfi1 mutant cells to wild-type controls showed that Gfi1 -target genes are altered sequentially, as cells traverse successive differentiation states. 79 Analytical innovations such as cellular trajectory analysis and inference of dynamic information using splice variants (RNA velocity) continue to expand the scope of single-cell genomics.…”

Defining the Emerging Blood System During Development at Single-Cell Resolution

“…The holistic, unsupervised, and high-throughput data generated by scRNA-seq allow for defining the PSC-differentiation process, including cell hierarchies, molecular regulation, and genetic networks, as well as for resolving heterogeneity of HSPCs formed in the culture ( Angelos et al, 2018 ; Han et al, 2018 ). Recently, scRNA-seq followed by trajectory analysis revealed the cellular heterogeneity and differences between hPSCs differentiated toward blood and fetal HSCs ( Fidanza et al, 2020 ). Furthermore, candidate surface markers with the potential to prospectively isolate distinct populations within the differentiation hierarchy were identified.…”

“…Lately Cellular Indexing of Transcriptomes and Epitopes by Seq (CITE-seq; Stoeckius et al, 2017 ), a modified version of scRNA-seq, has been introduced where cells are stained with antibodies coupled to unique oligonucleotides that are subsequently included in the sequencing library, thus making possible direct correlation of immunophenotype and transcriptome. CITE-seq was applied to validate the cell surface markers identified by scRNA-seq in Fidanza et al (2020) , comprehensively defining the cellular and immunophenotypic hierarchy of hPSC differentiation in vitro . Importantly, by performing machine learning and comparing the data with published single-cell transcriptome data from the human embryo ( Popescu et al, 2019 ), distinct cell types could be identified in the in vitro data set.…”

“…Hematopoietic lineage trajectory showed an HSC/multipotent progenitor (MPP) cluster differentiating toward lymphoid, myeloid, and megakaryocyte–erythroid–mast cell (MEM) progenitor populations, where HSCs/MPPs were situated at the branching point of lineage commitment, with early transcriptome priming toward all the different lineages ( Popescu et al, 2019 ). This extensive source of data has already been used as a reference material for later studies ( Cao et al, 2020 ; Fidanza et al, 2020 ).…”