High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies

Published

February 20, 2024

Author(s)

Anthony Asmar, Zackery Benson, Adele Peskin, Mylene Simon, Joe Chalfoun, Michael Halter, Anne L. Plant

Abstract

To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure.

Citation

PLoS One

Pub Type

Journals

Download Paper

https://doi.org/10.1371/journal.pone.0298446

Local Download

Keywords

Stem cells, Quantitative Microscopy, Engineering Biology, AI

Citation

Asmar, A. , Benson, Z. , Peskin, A. , Simon, M. , Chalfoun, J. , Halter, M. and Plant, A. (2024), High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies, PLoS One, [online], https://doi.org/10.1371/journal.pone.0298446, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956705 (Accessed December 30, 2024)