# Leveraging natural phenotypic variations of heterogenous ALS populations-in-a-dish to enable scalable drug discovery

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2023 · $987,822

## Abstract

Project Summary/Abstract
ALS is a complex disease with diverse genetic etiologies. Although drugs targeting known causal mutations
(e.g. SOD1 ASOs) may treat individual forms of ALS, this approach cannot address the vast majority of cases
with unknown genetic etiology. Moreover, the large number of causal genes and rarity of each genetic form
suggest that treating ALS will require many patient-specific therapies or broadly-effective treatments. Thus,
there is a pressing need for new, scalable approaches that identify patient-specific or broadly-effective
therapeutic strategies for multiple forms of ALS, particularly those with unknown genetic etiologies.
Studies using induced motor neurons (iMNs) from iPSCs indicate that iMNs from most ALS patients, including
those without known mutations, display ALS disease phenotypes including rapid degeneration. We performed
phenotypic screening on ALS iMNs to identify the most efficacious therapeutic targets. However, iMN drug
responses are heterogeneous across patient lines, and probing disease mechanisms and drug responses on a
sufficient number of lines is prohibitively expensive and labor intensive. In this transformative project, we will
overcome this critical barrier in ALS drug discovery by combining ALS iPSC disease modeling with GENEVA, a
novel platform we developed for cancer therapeutics that uses single cell transcriptomics to assess drug
effects on dozens of patient lines in one dish. GENEVA uses SNP-based computational demultiplexing of
single-cell RNA-seq data to profile responses to therapies across pools of many iPSC lines. We developed
computational tools that analyze the high-content readout of scRNA-seq to (i) precisely quantify the sensitivity
of every line based on its representation within the population in the case and control arm, (ii) identify the
molecular mechanisms that underlie the response to the drug and possible mechanisms of resistance, and (iii)
reveal differences in response between subpopulations as a result of heterogeneity within every line. GENEVA
will increase the scale of ALS lines in drug discovery by 10-50-fold and reveal disease and drug response
mechanisms at single cell resolution, enabling the discovery of new therapeutic targets with either broad
efficacy or high patient specificity. Using this “population-in-a-dish” approach, GENEVA-ALS will identify
neurodegenerative and drug response mechanisms across ALS patient cohorts at an unprecedented
scale, removing a critical bottleneck in ALS drug discovery. The proposed study will 1) establish the
GENEVA-ALS population-in-a-dish platform, 2) establish temporal maps of iMN disease processes for 45 ALS
lines, 3) validate 3 therapeutic targets with novel mechanisms of action that show broad efficacy across ALS
iMN lines, 4) determine if GENEVA-ALS can predict efficacy in ALS patients in a phase 2 clinical trial, and 5)
identify new therapeutic targets in a genome-wide CRISPRi screen on 30 ALS lines. Our study seeks t...

## Key facts

- **NIH application ID:** 10706307
- **Project number:** 5R01NS131409-02
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Hani Goodarzi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $987,822
- **Award type:** 5
- **Project period:** 2022-09-18 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10706307

## Citation

> US National Institutes of Health, RePORTER application 10706307, Leveraging natural phenotypic variations of heterogenous ALS populations-in-a-dish to enable scalable drug discovery (5R01NS131409-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10706307. Licensed CC0.

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