Reconstruction of single-cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition

Author:

Cheng Yu-Chen1234,Zhang Yun5,Tripathi Shubham6,Harshavardhan B. V.7,Jolly Mohit Kumar8ORCID,Schiebinger Geoffrey9,Levine Herbert1011ORCID,McDonald Thomas O.1234,Michor Franziska123412

Affiliation:

1. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215

2. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215

3. Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02215

4. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138

5. State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

6. Yale Center for Systems and Engineering Immunology and Department of Immunobiology, Yale School of Medicine, New Haven, CT 06510

7. Interdisciplinary Mathematics Initiative, Indian Institute of Science, Bangalore 560012, India

8. Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India

9. Department of Mathematics, University of British Columbia, Vancouver, BC V6T 1Z2, Canada

10. Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115

11. Department of Physics, Northeastern University, Boston, MA 02115

12. The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02138

Abstract

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single-cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along the partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4 , LAMA3 , and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF , CKS1B , and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the identification of the latter group of genes sheds light on potential cell cycle targets for modulating EMT trajectories.

Funder

National Science Foundation

Publisher

Proceedings of the National Academy of Sciences

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