# Unraveling the genetic basis of cellular behaviors with deep learning and imaging-based reverse genetics

> **NIH NIH DP2** · CALIFORNIA INSTITUTE OF TECHNOLOGY · 2022 · $1,173,600

## Abstract

Project Summary
Imaging and genomics are becoming increasingly intertwined, as multiplexed RNA FISH and
multiplexed immunohistochemistry now make it possible to perform “omic” measurements while
preserving spatial information. These new technologies are allowing us to create a new,
descriptive understanding of normal and diseased tissues. For cell culture models, they offer the
promise of measuring multiple facets of cellular behavior – ranging from cell shape to gene
expression – all in the same cell. This can be done by pairing dynamic live-cell imaging data
with end-point spatial genomics measurements. Such measurements could even be performed
in the setting of perturbations, creating a powerful tool for mapping biological networks. In this
proposal, I seek to make these methods accessible to the life science community by using
large-scale data annotation, deep learning, and cloud computing to solve several outstanding
cellular image analysis problems facing the spatial genomics field. I also propose to develop a
simple, scalable approach to performing perturbations in imaging-based experiments.
The work proposed here is three-fold. First, we will develop deep learning methods for
performing whole cell segmentation in tissues as well as segmentation and lineage construction
in live-cell imaging movies. To ensure these models generalize across tissues, cell lines, and
imaging platforms we will undertake a large-scale data annotation effort to create a
standardized collection of images that have been annotated with single cell resolution. Second,
we will also develop new deep learning methods for unsupervised learning of cellular behaviors.
Third, we will create a new approach to imaging-based reverse genetic screens. In this
approach, we will use CRISPR-Display to create multi-color spatial patterns in cell nuclei. This
will allow us to link cells and perturbations in images while minimizing the number of collected
images. Libraries with 100’s of thousands of perturbations would be interpretable with only 1-2
rounds of low-magnification 4 color imaging.
Achieving these high-risk, high-reward goals will constitute a transformative advance as it will
empower researchers studying living systems with imaging at the resolution of a single cell with
both ease and scale. Once finished, this work will place the microscope back at the center of the
biologist’s toolkit and enable images to become a universal datatype for biology.

## Key facts

- **NIH application ID:** 10472362
- **Project number:** 1DP2GM149556-01
- **Recipient organization:** CALIFORNIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** David A VAN VALEN
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,173,600
- **Award type:** 1
- **Project period:** 2022-09-08 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10472362

## Citation

> US National Institutes of Health, RePORTER application 10472362, Unraveling the genetic basis of cellular behaviors with deep learning and imaging-based reverse genetics (1DP2GM149556-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10472362. Licensed CC0.

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