Postdoc San Raffaele Telethon Institute for Gene Therapy, Italy
Abstract: Hematopoietic Stem and Progenitor Cells (HSPCs) are heterogeneous populations composed by more primitive subsets and committed progenitors responsible for supporting hematopoiesis. Thanks to their repopulating potential these cells are extensively exploited in clinical applications such as HSPC gene therapy (GT). Thus, understanding HSPC biological properties is of paramount importance for enhancing both their collection and genetic engineering, with the ultimate goal of improving the outcome of HSPC GT. Recent technological advances considerably increased the throughput of single-cell RNA-sequencing (scRNA-seq) experiments hence, rather than analysing each HSPC dataset independently, data integration enhances the characterization of each subpopulation and enables the analysis of low abundant cell types. Although several scRNA-seq atlases have been released none of them focuses on the most primitive HSPC subset that are crucial for the long-term outcome of HSPC GT. Since CD34 marker is commonly exploited for HSPC enrichment in clinical protocols, we collected and integrated in-house (SR-TIGET) unpublished and publicly available CD34+ FACS-sorted scRNA-seq datasets. Such atlas transcriptionally profiles over 277 thousand HSPC derived from healthy donors throughout ages (pediatric to elderly) and clinically relevant sources (bone marrow, cord blood, peripheral blood, mobilized peripheral blood). Such gene expression matrix was normalized, scaled by the number of transcripts and cell cycle stage, and integrated removing batch effects such as dataset, donor and source. The resulting object was then projected onto lower dimensional space, and transcriptionally homogeneous cells were identified by Louvain algorithm. We found a cluster of low-cycling long-term HSCs, composed by cells from all sources and ages, expressing stem-related genes such as AVP, MLLT3 and CRHBP suggesting the existence of a “universal” HSC population with unique gene signature. Sub-clustering of this compartment will allow to identify the most primitive HSC gene signature, that will be tested across HSPC sources. Overall, this work will allow to identify novel pathways associated with stemness and self-renewal that could be also exploited to improve the ex-vivo manipulation of HSPCs for their clinical application.
