Predicting ‘live’ cell fate dynamics in human stem cells by deep learning-enhanced morphological profiling

Session

Clinical Diagnostic Imaging

Authors

Professor Rafael E Carazo Salas (1, 2)

Affiliations

1. University of Bristol
2. CellVoyant Technologies Limited

Keywords

Stem cells, iPS, proliferation, differentiation, cell fate dynamics, high-content microscopy, machine learning, Artificial Intelligence

Abstract text

The Regenerative Medicine of tomorrow will rely on being able to produce a wide variety of “designer” tissues of choice (neurons, heart cells, liver cells, etc) that can be used for Personalised tissue replacement in the clinic. This goal has become achievable in particular thanks induced Pluripotent Stem cells (iPSCs), a technology enabling to manufacture pluripotent stem cells from any individual and use them to derive from them almost any desired target cell or tissue of choice by differentiation in vitro. However, key challenges have to be overcome before iPSC therapeutics becomes a routine reality in the market and in the clinic, notably how to control efficiently and consistently iPSC differentiation into target tissues of choice. In this talk I will describe machine learning-enabled microscopy phenomics technologies we have pioneered in the past few years that make it possible to visualise and predict in a non-destructive manner human cell proliferation and differentiation dynamics ‘live’, across days, and at single-cell level. Leveraging such technologies in the future will be key to enable robust human tissue design and manufacturing for therapeutic applications.

References

1. Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs. Ren E, Kim S, Mohamad S, Huguet SF, Shi Y, Cohen AR, Piddini E, Carazo Salas RE. BioRxiv. 2021. doi: https://doi.org/10.1101/2021.07.31.454574 . 

2. Multiplexed live visualization of cell fate dynamics in hPSCs at single-cell resolution. Kim S, Ren E, Marco Casanova P, Piddini E, Carazo Salas RE. BioRxiv. 2021. doi: https://doi.org/10.1101/2021.01.30.428961 . 

3. From observing to predicting single-cell structure and function with high-throughput/high-content microscopy. Chessel A#, Carazo Salas RE. Essays Biochem. 2019. https://doi:10.1042/EBC20180044

4. Big Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities. Del Sol A#, Thiesen HJ, Imitola J, Carazo Salas RE. Cell Stem Cell 2017. doi: 10.1016/j.stem.2017.01.006