Drug Discovery 2022: driving the next life science revolution

Morphological trajectory inference: Tracking cell state transitions and the impact of small-molecule perturbations

Tue4  Oct03:45pm(30 mins)
Where:
Auditorium 1
Dr Rebecca Hughes

Authors

R Hughes1
1 University of Edinburgh, UK

Abstract

During development, in response to stimuli, and throughout life, cells transition from one state to another. However, we too often fail to exploit the continuous resolution of cellular transitions, and instead classify cell states as discrete entities; healthy:diseased, stem-cell:mature-cell, on-target:off-target. Trajectory inference could fundamentally change morphological analysis by enabling the computational alignment and modelling of cellular states such as differentiation, cancer progression, and drug treatments to both illuminate the underlying biological processes and study the effect of small-molecule perturbations as cells transition from one state to another. We describe the first morphological assay to leverage the mathematics of trajectory inference and apply it to high dimensional, single cell, morphological data. We have used the Cell Painting assay to generate such data across two separate areas of interest. Firstly, we have applied pseudotime trajectory inference to track liver cell fate from progenitor through to mature hepatocytes and cholangiocytes to build a morphological map of liver cell differentiation. To this, we have mapped 12,000 compound induced morphologies to identify compounds, targets and pathways to support appropriate lineage commitment and aid the development of cell-based and small-molecule therapies to treat liver disease. In a second and completely unique application we have applied trajectory inference to develop morphological pseudodose modelling providing insights into the dynamics of compound induced cellular changes, including drug mechanisms, off-target toxicities, and cell specific effects.

Morphological trajectory inference represents a holistic, high-throughput and inexpensive alternative to single-cell RNA-seq providing deeper insights into cell state transitions and the impact of small-molecule perturbations.

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