DynaMorph: self-supervised learning of morphodynamic states of live cells-Reference-Cited by-同舟云学术

DynaMorph: self-supervised learning of morphodynamic states of live cells

Published:2022-05-15 Issue:6 Volume:33 Page:
ISSN:1059-1524
Container-title:Molecular Biology of the Cell
language:en
Short-container-title:MBoC

Author:

Wu Zhenqin¹,Chhun Bryant B.²,Popova Galina³,Guo Syuan-Ming²,Kim Chang N.³,Yeh Li-Hao²,Nowakowski Tomasz³,Zou James⁴,Mehta Shalin B.²

Affiliation:

1. Department of Chemistry, Stanford University, Stanford, CA 94305

2. Chan Zuckerberg Biohub, San Francisco, CA 94158

3. Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143

4. Department of Biomedical Data Science, Stanford University, Stanford, CA 94305

Abstract

A cell’s function depends on its dynamic architecture. There is growing interest in modern cell biology for methods enabling the automated analysis of morphodynamics of live cells. Here we demonstrate the power of label-free live cell imaging and self-supervised deep learning for automated analysis of complex dynamics of human microglia.

Publisher

American Society for Cell Biology (ASCB)

Subject

Cell Biology,Molecular Biology

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