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.
Highlights
It has been more than 60 years since experiments first proved the existence of bone marrow cells capable of producing the whole blood system
We chose a data set of Hematopoietic stem cells (HSCs) that were profiled as part of a study in which these cells were annotated with an HSC-score based on their gene expression [19]
On the basis of these shared parameters, dimensionality reduction was used to show that the repopulating HSCs in the single-cell transplantation experiments possessed surface marker profiles similar to those of the high-HSC-score cells
Summary
It has been more than 60 years since experiments first proved the existence of bone marrow cells capable of producing the whole blood system. Initial scRNA-seq studies were limited in throughput by the cost and difficulty of profiling large numbers of cells Newer technologies such as dropletbased scRNA-seq methods [12−14] are enabling generation of increasingly large data sets, with multiple studies capturing tens of thousands of cells from the blood system [9,15−17]. This has many exciting implications for hematopoiesis research, yet these technologies bring their own challenges. Many scRNA-seq data sets do not incorporate these measurements Even in those studies using technologies such as index sorting [20,21] or CITE-seq [22] to link protein and gene expression, the identification of HSCs is still dependent on the choice of markers measured in the experiment. Identifying potentially rare populations of HSCs in single-cell data remains a challenge
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