Single-cell transcriptional diversity is a hallmark of developmental potential.

Autor: Gulati GS; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Sikandar SS; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Wesche DJ; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Manjunath A; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Bharadwaj A; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Berger MJ; Department of Computer Science, Stanford University, Stanford, CA 94305, USA., Ilagan F; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Kuo AH; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Hsieh RW; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Cai S; School of Life Sciences, Westlake University, Zhejiang Province, China., Zabala M; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Scheeren FA; Department of Medical Oncology, Leiden University Medical Center, 2333 ZA Leiden, Netherlands., Lobo NA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Qian D; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA., Yu FB; Chan Zuckerberg Biohub, San Francisco, CA 94305, USA., Dirbas FM; Department of Surgery, Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA., Clarke MF; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA.; Department of Medicine, Stanford University, Stanford, CA 94305, USA., Newman AM; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA. amnewman@stanford.edu.; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Jazyk: angličtina
Zdroj: Science (New York, N.Y.) [Science] 2020 Jan 24; Vol. 367 (6476), pp. 405-411.
DOI: 10.1126/science.aax0249
Abstrakt: Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.
(Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)
Databáze: MEDLINE
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