Human haematopoietic stem cell lineage commitment is a continuous process
By Lars Velten, S. Haas, S. Raffel, S. Blaszkiewicz, S. Islam, B. P. Hennig, Christoph Hirche, C. Lutz, E. Buss, D. Nowak and 7 more. Nature Cell Biology, 2017
805 citations recorded by the source corpus.
Blood formation is believed to occur through stepwise progression of haematopoietic stem cells (HSCs) following a tree-like hierarchy of oligo-, bi- and unipotent progenitors. However, this model is based on the analysis of predefined flow-sorted cell populations. Here we integrated flow cytometric, transcriptomic and functional data at single-cell resolution to quantitatively map early differentiation of human HSCs towards lineage commitment. During homeostasis, individual HSCs gradually acquire lineage biases along multiple directions without passing through discrete hierarchically organized progenitor populations. Instead, unilineage-restricted cells emerge directly from a ‘continuum of low-primed undifferentiated haematopoietic stem and progenitor cells’ (CLOUD-HSPCs). Distinct gene expression modules operate in a combinatorial manner to control stemness, early lineage priming and the subsequent progression into all major branches of haematopoiesis. These data reveal a continuous landscape of human steady-state haematopoiesis downstream of HSCs and provide a basis for the understanding of haematopoietic malignancies.
Subfields: Hematology, Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/a24439ae7b68f6dfbb77b3a3301461c6d426ec30
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Hematology
In Immunology
In Molecular Biology
- Rahul Satija
- Fabian J Theis
- F. Alexander Wolf
- Jason D. Buenrostro
- W.J. Greenleaf
- Beijing Wu
- M. Corces
- Manu N. Setty
Citations in the corpus
- CellRank for directed single-cell fate mapping (2020, positive): “To evaluate the impact of including velocity information, we benchmarked CellRank with similarity-based methods that provide cell-fate probabilities (Palantir 21 , STEMNET 50 and FateID 51 ) and a velocity-based method that computes initial/terminal states (velocyto 14 ) on the pancreas data (Supple”
- Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, methodology): “[19] (Supplemental Figure 4); presumably because the latter contains a higher fraction of strongly heterog neous stem cells and deviates strongly from a tree topology, which h s b en observed previously for hematopoietic ata [26].”
- BloodSpot: a database of healthy and malignant haematopoiesis updated with purified and single cell mRNA sequencing profiles (2018, background): “In the initial versions (2,3) of BloodSpot, microarray was the standard high-throughput technique to assess gene expression in haematopoietic cell types, and large and comprehensive studies delineated the full constitution of the haematopoietic system (4), as well large cohorts of patients with aber”
- Lineage tracing on transcriptional landscapes links state to fate during differentiation (2018, background): “Consistent with recent scSeq studies (19), progenitors with different fate potentials did not partition into discrete cell states, but instead formed a structured continuum.”
- Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics (2018, background): “As studies have progressed to profiling complex human tissues [3, 4] and even entire organisms [5, 6], there is a growing appreciation of the need for massively parallel technologies and datasets to uncover rare and subtle cell states [7–9].”
- Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry (2018, methodology): “been used to elucidate hematopoietic differentiation (3-5) and immune cell subsets (6) 54 including dendritic cells and monocytes (7), and innate lymphoid cells (8).”
- Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation (2018, background): “Furthermore, recent work measuring single-cell transcriptomes has revealed significant transcriptional heterogeneity in isolated progenitors (Notta et al., 2016) and across differentiation (Laurenti and Göttgens, 2018; Velten et al., 2017).”
- Developmental and Functional Heterogeneity of Monocytes. (2018, background): “Also exercise and age can also influence the dynamic equilibrium of monocyte subsets (Heimbeck et al., 2010; Seidler et al., 2010; Verschoor et al., 2014).”