Concurrent stem- and lineage-affiliated chromatin programs precede hematopoietic lineage restriction

Safi, Fatemeh; Dhapola, Parashar; Warsi, Sarah; Erlandsson, Eva; Sitnicka, Ewa; Bryder, David; Böiers, Charlotta; Thakur, Ram Krishna; Karlsson, Göran

doi:10.1101/2020.04.29.069062

Cited by 2 publications

(2 citation statements)

References 63 publications

(65 reference statements)

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“…Even though the integration of ScRNA- and ScATAc-seq data is efficient in defining transcriptional signatures responsible for lineage identity, the capacity to perform multiomics at single-cell resolution has even more merit for defining molecular states of less distinct populations, such as early progenitor stages and HSCs[33, 34]. Thus, when focusing on early pseudotime, 14,731 early enhancers were defined, with 650 enhancers in common for all six trajectories.…”

Section: Resultsmentioning

confidence: 99%

Single-Cell Multiomics Reveals Distinct Cell States at the Top of the Human Hematopoietic Hierarchy

Sommarin

Dhapola

Safi

et al. 2021

Preprint

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The advent of single cell (Sc) genomics has challenged the dogma of haematopoiesis as a tree-like structure of stepwise lineage commitment through distinct and increasingly restricted progenitor populations. Instead, analysis of ScRNA-seq has proposed that the earliest events in human hematopoietic stem cell (HSC) differentiation are characterized by only subtle molecular changes, with hematopoietic stem and progenitor cells (HSPCs) existing as a continuum of low-primed cell-states that gradually transition into a specific lineage (CLOUD-HSPCs). Here, we combine ScRNA-seq, ScATAC-seq and cell surface proteomics to dissect the heterogeneity of CLOUD-HSPCs at different stages of human life. Within CLOUD-HSPCs, pseudotime ordering of both mRNA and chromatin data revealed a bifurcation of megakaryocyte/erythroid and lympho/myeloid trajectories immediately downstream a subpopulation with an HSC-specific enhancer signature. Importantly, both HSCs and lineage-restricted progenitor populations could be prospectively isolated based on correlation of their molecular signatures with CD35 and CD11A expression, respectively. Moreover, we describe the changes that occur in this heterogeneity as hematopoiesis develops from neonatal to aged bone marrow, including an increase of HSCs and depletion of lympho-myeloid biased MPPs. Thus, this study dissects the heterogeneity of human CLOUD-HSPCs revealing distinct HSPC-states of relevance in homeostatic settings such as ageing.

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Section: Resultsmentioning

confidence: 99%

Single-Cell Multiomics Reveals Distinct Cell States at the Top of the Human Hematopoietic Hierarchy

Sommarin

Dhapola

Safi

et al. 2021

Preprint

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“…To overcome this problem, it may be possible to extend the CellRank model to include epigenetic information such as chromatin accessibility. Many regulatory processes are initiated at the epigenetic level and only become visible transcriptionally after a delay, or not at all 3,72,73 . Including such information in the CellRank model, possibly by introducing limited memory to the Markov chain, could therefore increase its applicability.…”

Section: Discussionmentioning

confidence: 99%

CellRank for directed single-cell fate mapping

Theis

Lange

Bergen

et al. 2020

Preprint

110

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Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

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Concurrent stem- and lineage-affiliated chromatin programs precede hematopoietic lineage restriction

Cited by 2 publications

References 63 publications

Single-Cell Multiomics Reveals Distinct Cell States at the Top of the Human Hematopoietic Hierarchy

Single-Cell Multiomics Reveals Distinct Cell States at the Top of the Human Hematopoietic Hierarchy

CellRank for directed single-cell fate mapping

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