Spatial and temporal dynamics of RhoA activities of single breast tumor cells in a 3D environment revealed by a machine learning-assisted FRET technique
Brian CH Cheung (1), Louis Hodgson (2, 3), Jeffrey E Segall (2),, Mingming Wu (1) ((1) Department of Biological, Environmental Engineering,, Cornell University, USA, (2) Department of Anatomy, Structural Biology,, Albert Einstein College of Medicine, USA

TL;DR
This study introduces a machine learning-assisted FRET method to analyze RhoA activity dynamics in single breast tumor cells within 3D environments, revealing differences from 2D migration patterns and linking RhoA polarization to cell shape.
Contribution
It develops a novel machine learning-enhanced FRET technique for studying RhoA activity in 3D cell migration, addressing previous imaging challenges and providing new insights into cell polarization.
Findings
RhoA activity is more polarized along the cell's long axis in 2D than in 3D.
RhoA activity exhibits distinct front-back movement in 2D versus 3D environments.
RhoA polarization correlates with cell shape regardless of the environment.
Abstract
One of the hallmarks of cancer cells is their exceptional ability to migrate within the extracellular matrix (ECM) for gaining access to the circulatory system, a critical step of cancer metastasis. RhoA, a small GTPase, is known to be a key molecular switch that toggles between actomyosin contractility and lamellipodial protrusion during cell migration. Current understanding of RhoA activity in cell migration has been largely derived from studies of cells plated on a two-dimensional (2D) substrate using a FRET biosensor. There has been increasing evidence that cells behave differently in a more physiologically relevant three-dimensional (3D) environment, however, studies of RhoA activities in 3D have been hindered by low signal-to-noise ratio in fluorescence imaging. In this paper, we present a machine learning-assisted FRET technique to follow the spatiotemporal dynamics of RhoA…
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