# Purchase of a light microscopy system for high-throughput and high-resolution live cell imaging

> **NIH NIH R35** · BOSTON CHILDREN'S HOSPITAL · 2022 · $175,501

## Abstract

PROJECT SUMMARY/ABSTRACT
High-throughput microscopy (HTM) can generate vast spatiotemporal information on cellular structures and
dynamics under various conditions. Computational analyses of the whole aspect of such data could produce
unbiased, systematic representations of cellular heterogeneity across multiple scales. Current HTM, however,
is constructed by combining high-magnification microscopes with scanning stages; this configuration would
entail high complexity in the system design and operation, high cost, and slow image acquisition rates. We
reason that we can transcend the current HTM by integrating light microscopy and machine learning (ML). ML
is potent in discovering intricate, hidden structures in high-dimensional datasets with limited human
supervision. We will leverage ML’s power to learn important cellular features to create a high-throughput high-
resolution live cell imaging system. Our goal is to develop a new HTM platform, termed Machine Learning
Microscopy (MLM), that autonomously acquires high-resolution live cell movies in a high-throughput manner.
The proposed MLM platform will leverage deep neural networks to allow for i) high-resolution, continuous
imaging on live cells and ii) automated acquisition of single-cell and subcellular movies specific to target
phenotypes. In the parent award, we will apply MLM to construct a detailed phenotypic map of cell migration
and subcellular morphodynamics. The MLM will bring unprecedented analytical power to HTM by imaging
large numbers of single cells at high spatial resolution and facilitating extracting many cellular and subcellular
phenotypes. MLM can be applied to various areas of cell biology, such as cell division, cytoskeleton,
membrane remodeling, and membrane-bound organelles.

## Key facts

- **NIH application ID:** 10582350
- **Project number:** 3R35GM133725-05S1
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Kwonmoo Lee
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $175,501
- **Award type:** 3
- **Project period:** 2019-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10582350

## Citation

> US National Institutes of Health, RePORTER application 10582350, Purchase of a light microscopy system for high-throughput and high-resolution live cell imaging (3R35GM133725-05S1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10582350. Licensed CC0.

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