Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis.

Stem Cell Reports
D. A. JoyTodd C. McDevitt

Abstract

Lineage tracing is a powerful tool in developmental biology to interrogate the evolution of tissue formation, but the dense, three-dimensional nature of tissue limits the assembly of individual cell trajectories into complete reconstructions of development. Human induced pluripotent stem cells (hiPSCs) can recapitulate aspects of developmental processes, providing an in vitro platform to assess the dynamic collective behaviors directing tissue morphogenesis. Here, we trained an ensemble of neural networks to track individual hiPSCs in time-lapse microscopy, generating longitudinal measures of cell and cellular neighborhood properties on timescales from minutes to days. Our analysis reveals that, while individual cell parameters are not strongly affected by pluripotency maintenance conditions or morphogenic cues, regional changes in cell behavior predict cell fate and colony organization. By generating complete multicellular reconstructions of hiPSC behavior, our tracking pipeline enables fine-grained understanding of morphogenesis by elucidating the role of regional behavior in early tissue formation.

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Software Mentioned

FCRN
Python
Inception
statsmodels
Net

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