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

Authors
  • Hamey, Fiona K1
  • Göttgens, Berthold2
  • 1 Wellcome-MRC Cambridge Stem Cell Institute and Department of Haematology, University of Cambridge, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, United Kingdom. Electronic address: [email protected] , (United Kingdom)
  • 2 Wellcome-MRC Cambridge Stem Cell Institute and Department of Haematology, University of Cambridge, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, United Kingdom. , (United Kingdom)
Type
Published Article
Journal
Experimental hematology
Publication Date
Oct 01, 2019
Volume
78
Pages
11–20
Identifiers
DOI: 10.1016/j.exphem.2019.08.009
PMID: 31513832
Source
Medline
Language
English
License
Unknown

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 within large scRNA-seq data sets. To address this, we tested a range of machine learning approaches and developed a tool, hscScore, to score single-cell transcriptomes from murine bone marrow based on their similarity to gene expression profiles of validated HSCs. We evaluated hscScore across scRNA-seq data from different laboratories, which allowed us to establish a robust method that functions across different technologies. To facilitate broad adoption of hscScore by the wider hematopoiesis community, we have made the trained model and example code freely available online. In summary, our method hscScore provides fast identification of mouse bone marrow HSCs from scRNA-seq measurements and represents a broadly useful tool for analysis of single-cell gene expression data. Copyright © 2019 ISEH -- Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.

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