# Unraveling subcellular heterogeneity of molecular coordination by machine learning

> **NIH NIH R35** · BOSTON CHILDREN'S HOSPITAL · 2021 · $442,500

## Abstract

PROJECT SUMMARY/ABSTRACT
Recent advances in fluorescence microscopy allow researchers to acquire an unprecedented amount of live
cell image data at high spatial and temporal resolutions. However, these images pose a significant challenge
for data analyses due to massive subcellular heterogeneity. Although conventional computer vision algorithms
have facilitated automatic image analysis, traditional ensemble-averaging of subcellular heterogeneity could
lead to the loss of critical mechanistic details. Given the current rapid growth of cell biological data from new
technological development, it is nearly impossible to keep up with the data generation if we solely rely on
human intelligence for algorithm development and data analysis. Recently, machine learning (ML) is making
tremendous progress and has shown that computers can outperform humans in the analysis of complex high
dimensional datasets. Conventional ML application in cell biology, however, is usually limited to fixed cells or
low spatial resolution setting (single cell resolution), which is limited in analyzing dynamic subcellular
information. To fill this voids, we have been developing an ML framework for fluorescence live cell image
analyses at the subcellular level. In our previous study, we established the method to deconvolve the
subcellular heterogeneity of lamellipodial protrusion from live cell imaging, which identified distinct subcellular
protrusion phenotypes with differential drug susceptibility. Thus, our goal is to advance this ML framework and
address technical and cell biological challenges in the live cell analysis. The overall goal of our research is two-
fold: i) advancing a new ML framework for cell biological research (technological development) and ii) applying
our ML framework to integrate mechanobiology and metabolism in cell protrusion (targeted cell biological
study). First, we will advance our ML framework for the deconvolution of subcellular heterogeneity of protrusion
and molecular coordination in live cells. This method will integrate time-series modeling and ML to deconvolve
subcellular molecular coordination. Second, we will develop deep learning based high-throughput fluorescence
live cell imaging. This will include microscope automation, resolution enhancement, and data synthesis, which
will build up the massive dataset for ML. Third, we will apply our ML framework to study the mechanosensitivity
of subcellular bioenergetic status in cell protrusion. We will evaluate how AMPK reacts to mechanical forces
and controls the subcellular organization of actin assembly and mitochondria to promote energy-demanding
protrusion phenotypes. Our ML framework will bring unprecedented analytical power to cell biology by
analyzing a large numbers of individual cells at the high spatial resolution and automatically extracting a
multitude of subcellular phenotypes. This framework can be applied to various areas of cell biology such as
cytoskeleton, membrane remodeling, an...

## Key facts

- **NIH application ID:** 10267171
- **Project number:** 5R35GM133725-04
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Kwonmoo Lee
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $442,500
- **Award type:** 5
- **Project period:** 2019-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10267171

## Citation

> US National Institutes of Health, RePORTER application 10267171, Unraveling subcellular heterogeneity of molecular coordination by machine learning (5R35GM133725-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10267171. Licensed CC0.

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