Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.

Phuc Nguyen,Phuc Nguyen,Pamela S Becker,Pamela S Becker,Pamela S Becker,Pamela S Becker,Hao Yuan Kueh,Jin Dai,Sylvia Chien,Raymond J Monnat

doi:10.1371/journal.pcbi.1009626

Abstract

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

Highlights

Cells maintain and switch between distinct phenotypic states in a dynamic manner
We did so to ensure that both shape and textural morphological features of imaged cells are adequately utilized for feature encoding
Cells with similar textural features can be discriminated using other features, e.g., Custers C7 and C8 are both enriched with dark cell interior textures, but differ in size with Cluster C7 cells larger on average than those in Cluster C8. These results demonstrate that UPSIDE can generate meaningful learned morphological features in an unsupervised manner, and these features can be effectively decoded into images to aid interpretability

Summary

Introduction

Cells maintain and switch between distinct phenotypic states in a dynamic manner. Identifying these states and understanding the basis for and dynamics by which they interconvert is a central challenge in biology. Transmitted light microscopy images directly reveal cell morphology and have historically formed the basis for identifying cell types and cell states in diverse fields, ranging from cell biology to neuroscience [6,7]. These images can be acquired at successive timelapse intervals and over long times, with minimal phototoxicity and without prior labeling or genetic manipulation. The resultant live cell movies can reveal additional information about the dynamics of these cell phenotypic states

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Conclusion