# Photoactivatable cell sorting to link genetic variation with complex cellular phenotypes

> **NIH NIH R21** · NEW YORK UNIVERSITY · 2022 · $418,647

## Abstract

PROJECT SUMMARY/ABSTRACT
Individuals differ from each other in many traits, and very few trait differences have simple genetic causes.
Indeed, traits associated with common diseases in humans tend to be quite complex, with variation caused by
the combined effects of many genetic variants as well as environmental influences and random chance.
Determining the genetic contributions to variation in complex traits therefore remains challenging. One
approach to meeting this challenge is to perform genetic analysis in laboratory organisms. Laboratory
experiments can control for sources of variation that human studies cannot, and can serve as a test bed for
developing new methods to determine genotypes and phenotypes at large scale. The budding yeast,
Saccharomyces cerevisiae, long used as a model for eukaryotic cell biology, has emerged as a key organism for
such experiments. Current yeast experiments achieve high statistical power for detecting genetic effects on
trait variation by sampling thousands to millions of individuals. However, to achieve these sample sizes the
experiments focus on traits that are easy to measure or select for, such as resistance to toxic environments. This
limited repertoire leaves a big gap in understanding the genetic basis of differences in complex cellular traits
such as morphological ones. The shapes and sizes of cells are highly relevant to various disease processes but
are understudied by quantitative geneticists. To fill this gap, this project will use a combination of high-
throughput microscopy, automated image analysis, and photoactivatable cell sorting to sample individuals for
high-power genetic analysis. Genetic crosses between natural-isolate strains of budding yeast will generate
large numbers of recombinant progeny. Real-time image analysis and microscope control will be used to
identify cells with extreme trait values and label them via photoactivation of a genetically encoded or
experimentally applied convertible fluorophore. Selected cells will then be recovered using fluorescence
activated cell sorting and pooled for genome sequencing. Genetic variants that contribute to differences in cell
morphology will be identified as those that are over-represented in selected pools relative to unselected pools.
The project will produce a broadly applicable method for linking complex cellular traits with genetic
differences. It will also yield new insights into the genetic basis of variation in such traits, and thereby advance
understanding of the genetic underpinnings of complex diseases.

## Key facts

- **NIH application ID:** 10539111
- **Project number:** 1R21HG012713-01
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Mark L Siegal
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $418,647
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10539111

## Citation

> US National Institutes of Health, RePORTER application 10539111, Photoactivatable cell sorting to link genetic variation with complex cellular phenotypes (1R21HG012713-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10539111. Licensed CC0.

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