Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets.

ABSTRACT: Hematopoietic stem cells (HSCs) are an essential source and reservoir for normal hematopoiesis, and their function is compromised in many blood disorders. HSC research has benefitted from the recent development of single-cell molecular profiling technologies, where single-cell RNA sequencing (scRNA-seq) in particular has rapidly become an established method to profile HSCs and related hematopoietic populations. The classic definition of HSCs relies on transplantation assays, which have been used to validate HSC function for cell populations defined by flow cytometry. Flow cytometry information for single cells, however, is not available for many new high-throughput scRNA-seq methods, thus highlighting an urgent need for the establishment of alternative ways to pinpoint the likely HSCs with

SUBMITTER: Hamey FK 

PROVIDER: S-EPMC6900257 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature