期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 115, 期 32, 页码 8167-8172出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1808021115
关键词
support vector machine learning; stem cells; differentiation; vascular biology
资金
- National Research Service Award (NRSA) F31 Predoctoral Fellowship [F31HL134329]
- Maryland Stem Cells Research Fund [MSCRFI-2784]
- American Heart Association [15EIA22530000]
- NCI Physical Sciences-Oncology Center [U54CA210173]
- NATIONAL CANCER INSTITUTE [U54CA210173] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [F31HL134329] Funding Source: NIH RePORTER
Morphogenesis during human development relies on the interplay between physiochemical cues that are mediated in part by cellular density and cytoskeletal tension. Here, we interrogated these factors on vascular lineage specification during human-induced pluripotent stem-cell (hiPSC) fate decision. We found that independent of chemical cues, spatially presented physical cues induce the self-organization of Brachyury-positive mesodermal cells, in a RhoA/Rho-associated kinase (ROCK)-dependent manner. Using unbiased support vector machine (SVM) learning, we found that density alone is sufficient to predict mesodermal fate. Furthermore, the long-withstanding presentation of spatial confinement during hiPSC differentiation led to an organized vascular tissue, reminiscent of native blood vessels, a process dependent on cell density as found by SVM analysis. Collectively, these results show how tension and density relate to vascular identity mirroring early morphogenesis. We propose that such a system can be applied to study other aspects of the stem-cell niche and its role in embryonic patterning.
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