Distinct myeloid progenitor–differentiation pathways identified through single-cell RNA sequencing

Drissen, Roy; Buza‐Vidas, Natalija; Woll, Petter S.; Thongjuea, Supat; Gambardella, Adriana; Giustacchini, Alice; Mancini, Elena; Zriwil, Alya; Lutteropp, Michael; Grover, Amit; Mead, Adam J.; Sitnicka, Ewa; Jacobsen, Sten Eirik W.; Nerlov, Claus

doi:10.1038/ni.3412

Cited by 199 publications

(230 citation statements)

References 59 publications

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“…[17][18][19][20][21][22][23] In leukemia, single-cell methods additionally offer the opportunity to discriminate between leukemic and healthy cells, thereby allowing for specific characterization of the infrequent residual LSC population even months into treatment. Here we have dissected the heterogeneity of the CML LSC population both at diagnosis and following 3 months of 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

114

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

114

120

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“…Recent studies utilizing scRNA-seq [1][2][3][4][5] and single-cell lineage tracing techniques [6][7][8] , call into question the traditional view of hematopoietic differentiation as a sequence of binary fate choices giving rise to a succession of increasingly fate restricted progenitor types 9 . Evidence from these studies rather suggests early cell fate priming starting at the level of multipotent progenitors (MPP) or even within the HSC pool.…”

Section: Introductionmentioning

confidence: 99%

FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

Herman

Sagar

Grün

2017

Preprint

159

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Differentiation of multipotent cells is a complex process governed by interactions of thousands of genes subject to substantial expression fluctuations. Resolving cell state heterogeneity arising during this process requires quantification of gene expression within individual cells. However, computational methods linking this heterogeneity to biases towards distinct cell fates are not well established. Here, we perform deep single-cell transcriptome sequencing of ~2,000 bone-marrow derived mouse hematopoietic progenitors enriched for lymphoid lineages. To resolve subtle transcriptome priming indicative of distinct lineage biases, we developed FateID, an iterative supervised learning algorithm for the probabilistic quantification of cell fate bias. FateID delineates domains of fate bias within progenitor populations and permits the derivation of high-resolution differentiation trajectories, revealing a common progenitor population of B cells and plasmacytoid dendritic cells, which we validated by in vitro differentiation assays. We expect that FateID will enhance our understanding of the process of cell fate choice in complex multi-lineage differentiation systems. INTRODUCTIONRecent studies utilizing scRNA-seq 1-5 and single-cell lineage tracing techniques 6-8 , call into question the traditional view of hematopoietic differentiation as a sequence of binary fate choices giving rise to a succession of increasingly fate restricted progenitor types 9 . Evidence from these studies rather suggests early cell fate priming starting at the level of multipotent progenitors (MPP) or even within the HSC pool. Pronounced heterogeneity of common myeloid progenitors (CMP) was elucidated with high resolution 1 , and an early fate bias emerging in human short term HSCs was suggested in a more recent study 2 . However, heterogeneity of lymphoid progenitors has not been well investigated with single-cell resolution. Since lymphoid progenitor heterogeneity was previously found by flow cytometry 10,11 , utilizing distinct sets of cell surface markers in combination with differentiation assays, we here perform an scRNA-seq analysis to comprehensively elucidate heterogeneity across lymphoid progenitors purified from the bone-marrow of adult mice. Although a number of methods for lineage reconstruction have been developed [12][13][14][15] , these algorithms are not specifically designed to uncover subtle transcriptome changes and uniquely assign cells to individual branches without accounting for multi-lineage bias. Weak transcriptome modulations also remain undiscovered by state-of-the-art clustering methods, which partition cells into groups without accounting for the co-existence of fate bias towards multiple lineages within individual cells. To elucidate the process of cell fate emergence, we introduce FateID, a computational method for the quantification of fate bias, manifested by subtle lineage specific transcriptome modulations within a multipotent progenitor . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the aut...

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“…Single-cell RNA sequencing (scRNA-seq) allows high-resolution whole-transcriptome profiling of individual cells and direct analysis of subpopulations of cells from a larger heterogeneous population. [23][24][25][26][27][28][29][30][31][32][33] Gain or loss of chromosomes should lead to over-or underexpression of genes located on the affected chromosomes. 34 Concomitant increases and decreases in chromosome-wide gene expression levels could be used to infer chromosome copy numbers by scRNA-seq.…”

Section: Introductionmentioning

confidence: 99%

Single-cell RNA-seq reveals a distinct transcriptome signature of aneuploid hematopoietic cells

Zhao

Gao

et al. 2017

Blood

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Distinct myeloid progenitor–differentiation pathways identified through single-cell RNA sequencing

Cited by 199 publications

References 59 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

FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data

Single-cell RNA-seq reveals a distinct transcriptome signature of aneuploid hematopoietic cells

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