Year: 2020
Type: article
Abstract: A challenge in stem cell biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Though the development of single cell assays allows for the capture of progenitor cell states in great detail, these assays cannot definitively link cell states to their long-term fate. Here, we use expressed DNA barcodes to clonally trace single cell transcriptomes dynamically during differentiation and apply this approach to the study of hematopoiesis. Our analysis identifies functional boundaries of cell potential early in the hematopoietic hierarchy and locates them on a continuous transcriptional landscape. We reconstruct a developmental hierarchy showing separate ontogenies for granulocytic subtypes and two routes to monocyte differentiation that leave a persistent imprint on mature cells. Finally, we use our approach to benchmark methods of dynamic inference from single-cell snapshots, and provide evidence of strong early fate biases dependent on cellular properties hidden from single-cell RNA sequencing.
Source: Science
Institutions: Harvard University, Boston Children's Hospital
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: Howard Hughes Medical Institute, American Society of Hematology, Leukemia and Lymphoma Society, Life Sciences Research Foundation, Chan Zuckerberg Initiative, EMBO, National Institutes of Health, Harvard Stem Cell Institute, National Heart, Lung, and Blood Institute, NIH Office of the Director, National Cancer Institute
Awards: mechanisms of in situ functional stem cell heterogeneity in native and transplantation hematopoiesis, G2045661193, G226432989, G3483171413, Molecular regulation of native hematopoiesis, G4740088492, G4920700466, G507482250, Droplet microfluidic technology for single cell cancer genomics, G5634314426, Novel Growth Factor Regulators of Early Erythropoieisis, G7105522622, G7773464603, Functional consequences of stem and progenitor cell heterogeneity
OpenAlex ID: W3002219094