A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation

Nestorowa, Sonia; Hamey, Fiona; Pijuan-Sala, Blanca; Diamanti, Evangelia; Shepherd, Mairi; Laurenti, Elisa; Wilson, Nicola K.; Kent, David G.; Göttgens, Berthold

doi:10.1182/blood-2016-05-716480

Cited by 640 publications

(757 citation statements)

References 57 publications

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“…Similar observations have been made in mice where the most primitive and quiescent HSC populations express low levels of total mRNA. 41,42 A correlation between TKI insensitivity and primitiveness/ quiescence has been suggested previously. 6,7 Using our approach, we can now visualize primitive, quiescent, TKI-insensitive cells and correlate this distinct population with immunophenotype during TKI treatment.…”

Section: Cd38mentioning

confidence: 99%

Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML

Warfvinge¹,

Geironson²,

Sommarin³

et al. 2017

Blood

117

120

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Section: Cd38mentioning

confidence: 99%

Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML

Warfvinge¹,

Geironson²,

Sommarin³

et al. 2017

Blood

117

120

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show abstract

“…( can capture a variety of more complex structures. The diffusion map method has been specifically adapted for use with singlecell expression data (17) and has proved to be a powerful tool for representing spatial heterogeneity in single-cell data from mouse embryos (18), and branching differentiation dynamics for both single-cell qRT-PCR data describing embryonic blood development (10) and single-cell RNA sequencing (scRNA-Seq) data for adult HSPCs (19). When applied to our data, diffusion map analysis using all of the genes analyzed by single-cell qRT-PCR demonstrated that the new and old data sets integrated well (SI Appendix, Fig.…”

Section: Resultsmentioning

confidence: 99%

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Hamey

Nestorowa

Kinston

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-bycell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyteerythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.gene regulatory networks | hematopoiesis | single cell | Boolean network | stem progenitor cells

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“…The hematopoiesis dataset 15 was obtained from the Gene Expression Omnibus 21 . The embryonic 18 and glioblastoma 19 datasets were obtained from conquer 22 , a repository of uniformly processed scRNA-seq datasets.…”

Section: Methods and Datamentioning

confidence: 99%

netSmooth: Network-smoothing based imputation for single cell RNA-seq

Ronen¹,

Akalin²

2018

F1000Res

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Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

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A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation

Abstract: Key Points An expression map of HSPC differentiation from single-cell RNA sequencing of HSPCs provides insights into blood stem cell differentiation. A user-friendly Web resource provides access to single-cell gene expression profiles for the wider research community.

Cited by 640 publications

References 57 publications

Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML

Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

netSmooth: Network-smoothing based imputation for single cell RNA-seq

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