# Multimodal phenotyping of zebrafish models of human disease

> **NIH NIH R24** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $769,325

## Abstract

Historically, human diseases have been described (and treated) based on their symptomology. Increasingly,
however, elucidation of the molecular and functional underpinning of diseases is transforming our capacity to
develop targeted therapeutic interventions. In the era of CRISPR, our ability to generate precise models of
genetic diseases has been greatly improved, but unfortunately, a major bottleneck remains in our capacity to
measure and interpret the resulting phenotypes. Moreover, most phenotypic measurements of animal models
are qualitative, require expert human assessment, are narrowly focused based on the interests of individual
labs, or are difficult to compare to other phenotypes. Together, these issues constrain our ability to generate
the mechanistic genotype-phenotype maps that are necessary to understand how organisms are built, how
health is maintained, and how these processes are disrupted in human diseases. Thanks to recent
technological advances, it is now realistic to contemplate large-scale, systematic disease model generation
and characterization. One of these advances is Multiplexed, Intermixed CRISPR Droplets (MIC-Drop), a
technology that combines CRISPR, microfluidics, and barcoding to enable high-throughput gene disruption in
zebrafish. By combining MIC-Drop with single-cell RNAseq and with automated physiological and behavioral
assays, it is now possible to generate and characterize disease models with unprecedented depth and
efficiency. Using this approach, we propose to target all human-disease-associated transcription factors in the
zebrafish genome. This project will generate animal models for hundreds of human disease genes, along with
single-cell transcriptomic data and detailed physiological and behavioral phenotypic descriptors for each
model. These data will be analyzed using computational techniques to detect, categorize, and interpret mutant
phenotypes that are otherwise intractable to manual curation. These data will be made available in a variety of
formats to enable exploration of deep phenotypic data for all these disease models. We anticipate that this
resource will improve the utilization, accessibility, and translational value of animal models to the research
community.

## Key facts

- **NIH application ID:** 10848834
- **Project number:** 1R24OD035409-01A1
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** James Alan Gagnon
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $769,325
- **Award type:** 1
- **Project period:** 2024-06-15 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10848834

## Citation

> US National Institutes of Health, RePORTER application 10848834, Multimodal phenotyping of zebrafish models of human disease (1R24OD035409-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10848834. Licensed CC0.

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