# Unraveling heterogeneity of molecular mechanisms in cellular motility and morphodynamics by machine learning

> **NIH NIH R35** · BOSTON CHILDREN'S HOSPITAL · 2024 · $452,217

## Abstract

PROJECT SUMMARY/ABSTRACT
Live cell imaging has revolutionized the study of dynamic cellular processes, enabling researchers to gain
valuable functional insights that would be impossible to obtain from static images. The recent advances in
microscopy technology have made it possible to acquire an unprecedented amount of live cell image data at
high spatiotemporal resolutions. However, this abundance of data has brought new challenges, particularly
concerning phenotypic and causal heterogeneity. This heterogeneity can complicate the analysis and
interpretation of the data, making it challenging to identify critical mechanistic details and understand how
cellular processes function in different conditions. To address this issue, we have been developing machine
learning platforms that can deconvolve the heterogeneity of live cell images at subcellular and cellular levels.
However, there are still many challenges in analyzing live cell heterogeneity in greater detail. First,
conventional feature selection/learning that aids in classifying known phenotypes could eliminate biologically
meaningful heterogeneity. Second, current cell biology research focuses primarily on mechanisms that
produce similar average effects across various populations, which could suffer from causal heterogeneity.
Even with the significant progress made in single-cell biology, the discovery of causal mechanisms at the
single-cell resolution is still not fully achieved. Finally, the substantial heterogeneity in cell motility and
morphodynamics poses a challenge to obtaining integrated and systematic understandings of these
processes, despite their clear significance in fields such as tissue regeneration and cancer metastasis. To
overcome these challenges, we propose to advance current machine learning methods to i) identify features
that preserve subtype heterogeneity while being discriminative between known phenotypes, ii) acquire causal
datasets of live cell images by tracking cells before and after optogenetic treatment, and iii) deconvolve the
causal heterogeneity of single live cells by integrating time-series modeling and deep learning. We will apply
these methods to map out single-cell mechanisms governing cellular motility and morphodynamics of various
cell types, which will be valuable resources for the cell biology community. Then, we will characterize how
mechanical and metabolic perturbations can shape the heterogeneous landscapes of cell motility and
morphodynamics, providing new insights into the underlying mechanisms integrating these processes.

## Key facts

- **NIH application ID:** 10842697
- **Project number:** 2R35GM133725-07
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Kwonmoo Lee
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $452,217
- **Award type:** 2
- **Project period:** 2019-09-15 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10842697

## Citation

> US National Institutes of Health, RePORTER application 10842697, Unraveling heterogeneity of molecular mechanisms in cellular motility and morphodynamics by machine learning (2R35GM133725-07). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10842697. Licensed CC0.

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