# Molecular tools for targeting RNA

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $332,850

## Abstract

Abstract
Due to rapid advances in RNA sequencing, scientists now have the ability to easily map transcriptome
changes in cellular function and disease to complement a robust catalog of functional genome elements and
variants. Genome editing tools, in particular the CRISPR-Cas9 platform, allow for rapid control and
manipulation of genome sequences for genetic study. To extend these tools to the direct and facile
perturbation of RNA, the proposed work aims to develop a broad suite of transcriptome engineering tools
based on diverse RNA-targeting CRISPR systems to complement RNAi. Analogous to dCas9 for DNA,
programmable RNA binding will enable modular modes of function including and beyond RNA knockdown,
such as splice isoform engineering, by leveraging a combination of bioinformatic, biochemical, and protein
engineering approaches to demonstrate and optimize the utility of the system. Using patient-derived induced
pluripotent stem cells differentiated into neurons as a model system, RNA mis-splicing diseases will be
targeted as a proof-of-concept to modulate alternative splicing. This proposal expects to enable a robust,
flexible platform to interrogate gene expression, study transcript dynamics, and dissect the function of coding
and noncoding transcripts.

## Key facts

- **NIH application ID:** 10072580
- **Project number:** 7R01GM131073-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Patrick Hsu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $332,850
- **Award type:** 7
- **Project period:** 2020-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10072580

## Citation

> US National Institutes of Health, RePORTER application 10072580, Molecular tools for targeting RNA (7R01GM131073-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10072580. Licensed CC0.

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