RT Journal Article
SR Electronic
T1 Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.07.31.454574
DO 10.1101/2021.07.31.454574
A1 Edward Ren
A1 Sungmin Kim
A1 Saad Mohamad
A1 Samuel F. Huguet
A1 Yulin Shi
A1 Andrew R. Cohen
A1 Eugenia Piddini
A1 Rafael Carazo Salas
YR 2021
UL http://biorxiv.org/content/early/2021/08/01/2021.07.31.454574.abstract
AB Predicting how stem cells become patterned and differentiated into target tissues is key for optimising human tissue design. Here, we established DEEP-MAP - for deep learning-enhanced morphological profiling - an approach that integrates single-cell, multi-day, multi-colour microscopy phenomics with deep learning and allows to robustly map and predict cell fate dynamics in real-time without a need for cell state-specific reporters. Using human pluripotent stem cells (hPSCs) engineered to co-express the histone H2B and two-colour FUCCI cell cycle reporters, we used DEEP-MAP to capture hundreds of morphological- and proliferation-associated features for hundreds of thousands of cells and used this information to map and predict spatiotemporally single-cell fate dynamics across germ layer cell fates. We show that DEEP-MAP predicts fate changes as early or earlier than transcription factor-based fate reporters, reveals the timing and existence of intermediate cell fates invisible to fixed-cell technologies, and identifies proliferative properties predictive of cell fate transitions. DEEP-MAP provides a versatile, universal strategy to map tissue evolution and organisation across many developmental and tissue engineering contexts.Competing Interest StatementThe authors have declared no competing interest.