Population snapshots predict early haematopoietic and erythroid hierarchies

Tusi, Betsabeh Khoramian; Wolock, Samuel L.; Weinreb, Caleb; Hwang, Yung; Hidalgo, Daniel; Žilionis, Rapolas; Waisman, Ari; Huh, Jun R.; Klein, Allon M.; Socolovsky, Merav

doi:10.1038/nature25741

Cited by 325 publications

(479 citation statements)

References 70 publications

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“…This observation agrees with recent studies in mouse, human and zebra fish, that have shown that the HSPC pool is a particularly variable cell population, 7,9,10,12,13 and highlights the fact that classification accuracy will depend upon both the amount of data available for training and the heterogeneity intrinsic to the cell population being considered. This issue will also be important when we consider transferring mouse biology to the human.…”

Section: Mapping Biology From Mouse To Mansupporting

confidence: 91%

“…Thirdly, it is well-established that cell types in the hematopoietic cell lineages of the BM are arranged according to a hierarchical structure. [9][10][11] By considering adjacency relationships between clusters, we were able to broadly recapitulate the known structure of this hierarchy, indicating that the clustering structure that we observed captures salient features of the mouse BM biology ( Fig. 1d).…”

Section: Mapping Biology From Mouse To Manmentioning

confidence: 58%

“…This high overlap is notable because HSPCs and megakaryocytes are proximal in the mouse hematopoietic cell lineage map (Fig. 1d), reflecting the fact that megakaryocytes emerge directly via differentiation from HSPCs 9,20,21 . This result suggested to us that the mouse classifier might be revealing aspects of human HSPC/megakaryocyte biology that are not apparent from unsupervised clustering of the human dataset alone.…”

Section: Discovery Of Hidden Cell Identities Using Zero-shot Learningmentioning

confidence: 98%

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Mapping biology from mouse to man using transfer learning

Imanishi

et al. 2019

Preprint

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Biomedical research often involves conducting experiments on model organisms in the anticipationthat the biology learnt from these experiments will transfer to the human. Yet, it is commonly the case that biology does not transfer effectively, often for unknown reasons. Despite its importance to translational research this transfer process is not currently rigorously quantified. Here, we show that transfer learning -the branch of machine learning that concerns passing information from one domain to another -can be used to efficiently map biology from mouse to man, using the bone marrow (BM) as a representative example of a complex tissue. We first trained an artificial neural network (ANN) to accurately recognize various different cell types in mouse BM using data obtained from single-cell RNA-sequencing (scRNA-Seq) experiments. We found that this ANN, trained exclusively on mouse data, was able to identify individual human cells obtained from comparable scRNA-Seq experiments of human BM with 83% overall accuracy. However, while some human cell types were easily identified, others were not, indicating important differences in biology. To obtain a more accurate map of the human BM we then retrained the mouse ANN using scRNA-Seq data from a limited sample of human BM cells. Typically, less than 10 human cells of a given type were needed to accurately learn its representation in the updated model. In some cases, human cell identities could be inferred directly from the mouse ANN without retraining, via a process of biologically-guided zero-shot learning. These results show how machine learning can be used to reconstruct complex biology from limited data and have broad implications for biomedical research.Mapping Biology from Mouse to Man

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Section: Mapping Biology From Mouse To Mansupporting

confidence: 91%

Section: Mapping Biology From Mouse To Manmentioning

confidence: 58%

Section: Discovery Of Hidden Cell Identities Using Zero-shot Learningmentioning

confidence: 98%

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Mapping biology from mouse to man using transfer learning

Imanishi

et al. 2019

Preprint

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“…Clusters were identified using the "FindClusters" function and UMAP projection was generated using the "RunUMAP" function, both with the first 16 principle components. Seurat clusters were annotated using marker genes and by reference to previously published data 19 -Seurat identified two clusters corresponding to committed erythroid progenitors (CEP) and four clusters corresponding to cells undergoing erythroid terminal differentiation (ETD). Average gene expression for populations matching those obtained by FACS sorting was generated by using the "SubsetData" function to select cells with low levels of cell-surface barcodes corresponding to lineage markers, and appropriate levels of barcodes corresponding to CD71 and Ter119.…”

Section: Methodsmentioning

confidence: 99%

“…We used Tiled-C to study the changes in chromatin structure associated with gene activation in primary cells during erythroid differentiation. Using fluorescence-activated cell sorting (FACS), we isolated sequential stages of erythroid differentiation directly from mouse fetal livers 18,19 (Figure 3a,b). We focused our analysis on a ~3.3 Mb region containing the well-characterized a-globin genes and their associated regulatory elements.…”

mentioning

confidence: 99%

Dynamics of the 4D genome during lineage specification, differentiation and maturation in vivo

Oudelaar

Beagrie

Gosden

et al. 2019

Preprint

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Mammalian gene expression patterns are controlled by regulatory elements, which interact within Topologically Associating Domains (TADs). The relationship between activation of regulatory elements, formation of structural chromatin interactions and gene expression during development is unclear. We developed Tiled-C, a low-input Chromosome Conformation Capture (3C) approach, to study chromatin architecture at high spatial and temporal resolution through in vivo mouse erythroid differentiation. Integrated analysis of matched chromatin accessibility and single-cell expression data shows that regulatory elements gradually become accessible within pre-existing TADs during early differentiation. This is followed by structural re-organization within the TAD and formation of specific contacts between enhancers and promoters. In contrast to previous reports, our high-resolution data show that these enhancer-promoter interactions are not established prior to gene expression, but formed gradually during differentiation, concomitant with progressive upregulation of gene activity. Together, these results provide new insight into the close, interdependent relationship between chromatin architecture and gene regulation during development.

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Understanding fundamental principles of enhancer biology at a model locus

et al. 2023

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Despite ever‐increasing accumulation of genomic data, the fundamental question of how individual genes are switched on during development, lineage‐specification and differentiation is not fully answered. It is widely accepted that this involves the interaction between at least three fundamental regulatory elements: enhancers, promoters and insulators. Enhancers contain transcription factor binding sites which are bound by transcription factors (TFs) and co‐factors expressed during cell fate decisions and maintain imposed patterns of activation, at least in part, via their epigenetic modification. This information is transferred from enhancers to their cognate promoters often by coming into close physical proximity to form a ‘transcriptional hub’ containing a high concentration of TFs and co‐factors. The mechanisms underlying these stages of transcriptional activation are not fully explained. This review focuses on how enhancers and promoters are activated during differentiation and how multiple enhancers work together to regulate gene expression. We illustrate the currently understood principles of how mammalian enhancers work and how they may be perturbed in enhanceropathies using expression of the α‐globin gene cluster during erythropoiesis, as a model.

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Population snapshots predict early haematopoietic and erythroid hierarchies

Cited by 325 publications

References 70 publications

Mapping biology from mouse to man using transfer learning

Mapping biology from mouse to man using transfer learning

Dynamics of the 4D genome during lineage specification, differentiation and maturation in vivo

Understanding fundamental principles of enhancer biology at a model locus

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