Functional stemness-related genes revealed by single-cell profiling of naïve and stimulated human CD34 + cells from CB and mPB

Abstract

Hematopoietic stem cells (HSCs) from different sources show varied repopulating capacity, and HSCs lose their stemness after long-time ex vivo culture. However, the underlying mechanisms of the stemness differences because of the cell sources and the culture stimulation are not fully understood. Here, we applied single-cell RNA-seq (scRNA-seq) to analyze the naïve and stimulated human CD34+ cells from cord blood (CB) and (mPB). We collected over 16,000 single-cell data to construct a comprehensive trajectory inference map and characterized the HSC population on the hierarchy top, which is under quiescent state. Then we compared HSCs in CB to those in mPB and HSCs of naïve samples to those of cultured samples, and identified stemness-related genes (SRGs) associated with culture time (CT-SRGs) and cell source (CS-SRGs), respectively. Interestingly, CT-SRGs and CS-SRGs share genes enriched in the signaling pathways such as mRNA catabolic process, Translational initiation, Ribonucleoprotein complex biogenesis and Cotranslational protein targeting to membrane, suggesting dynamic protein translation and processing may be a common requirement for stemness maintenance. Meanwhile, CT-SRGs are enriched in pathways involved in glucocorticoid and corticosteroid response that affect HSCs homing and engraftment. In contrast, CS-SRGs specifically contain genes related purine and ATP metabolic process which is important to initiate hematopoiesis. Finally, we presented an application through a small-scale drug screening using Connectivity Map (CMap) against CT-SRGs and found a small molecule cucurbitacin I, targeting STAT3/JAK2, can efficiently expand HSCs ex vivo while maintaining its stemness. These results indicate SRGs revealed by scRNA-seq can provide helpful insights to understand the stemness differences under diverse circumstances, and CT-SRGs can be a valuable database to identify candidates enhancing functional HSC expansion during ex vivo culture.

A novel computational method enables RNA editome profiling during human hematopoiesis from scRNA-seq data

“…2 E), which is consistent with the classical model of lineage determination in human hematopoietic hierarchy (Fig. 2 A) 42 – 44 . HSC and MPP were found to be clustered together.…”

“…We can find the conversed RNA editing sites in these different samples and cell culture states. We used a previous dataset with accession code CNP0000978 42 as reference to identify cell types in our data (Fig. 2 B).…”

“…MLP, MEMP, CMP, GMP, MEP and ProB were annotated in the query datasets based on the expression of marker genes reported in other research (Supplemental Fig. S1 B) 42 . Then by integrating the reference and query datasets, we obtained 32,303 cells with specific gene expression profiles (Fig.…”

“…The reference dataset (aligned reads) was downloaded from CNSA ( https://db.cngb.org/cnsa/ ) of CNGBdb with accession code CNP0000978. Both the reference and query dataset were CD34 + cells obtained from human CB and mPB, and the method of cell cultivation, scRNA-seq libraries and pre-processing were as previously described 42 . The raw sequencing data of query dataset generated in this study was deposited in the CNGB Sequence Archive (CNSA; https://db.cngb.org/cnsa/ ) of China National GeneBank DataBase (CNGBdb) with accession code CNP0003367.…”

Manufacturing of Human T-lymphoid Progenitors from Two Different Hematopoietic Stem Cell Sources and Perspective for New Immunotherapies

“…Progress made in single-cell transcriptomics created new insights into the comparison between CB and mPB HSPCs. In particular, Dong and colleagues performed scRNAseq of fresh and cultured CD34 + cells from CB and mPB, enabling them to delineate distinct stemness-related genes associated with cell sources and culture conditions for both sources [21].…”