# Defining and perturbing gene regulatory dynamics in the developing human brain

> **NIH NIH R01** · STANFORD UNIVERSITY · 2023 · $611,882

## Abstract

SUMMARY
Human brain development represents perhaps the pinnacle of complex organ specification, and an ideal model
system for understanding 1) how normal development can produce all the cell types necessary for human
cognition and 2) how genetic variation can perturb this process and lead to disease. We will generate large-scale
single cell data sets to develop accurate models capable of predicting the effects of both genetic changes to
regulatory elements and perturbations to trans-acting regulatory factors on gene expression during the complex
developmental process of human brain development. We will study two highly medically relevant, human, in
vitro, temporally dynamic differentiation systems that faithfully recapitulate fetal differentiation patterns: hiPSC-
derived cerebral cortical and spinal cord organoids. For each of these differentiation trajectories, we will work in
distinct aims toward mapping, perturbing, modeling, validating, and learning: Mapping: we will generate
systematic, single cell multi-omic (RNA-seq, ATAC-seq, and protein quantification) data to map regulatory
elements, chromatin contacts, RNA polymerase, protein binding, and gene expression through differentiation of
hiPSCs to brain tissue. Perturbing: We will use CRISPR-based methods to comprehensively identify TFs
required for differentiation and map the single-cell gene regulatory and expression impact of perturbing a subset
of these factors at multiple time points across these differentiation trajectories. Modeling: We will develop multi-
input nucleotide-resolved neural networks to learn dynamic gene regulatory networks using these mapping and
perturbation data sets. These models will aim to understand the changing landscape of regulation and grammars
of transcription factor motifs over differentiation time, and will predict both chromatin and gene expression effects
expected from genetic perturbations. Validating: We will apply our network models to identify, investigate, and
experimentally test perturbations relevant to understanding disease variation, by knocking down transcription
factors, perturbing regulatory elements, and editing disease-associated noncoding variants. Learning and
comparing: Finally, we will extract and test molecular properties of transcription factor function from validated
models, and compare experimental and modeling approaches to better understand accuracy, advantages, and
disadvantages. Successful completion of our project will provide mechanistic interpretations for how genetic
variants may impact development (by disrupting regulatory element that in turn disrupt gene expression) in brain
development. Our Stanford team comprises a diverse team of investigators with a history of productive
collaboration, and with expertise in genomics methods development (Greenleaf, Engreitz), single cell methods
and analysis (Greenleaf, Pasca), 3D cellular models of human brain (Pasca), and deep learning for genomic
data sets (Kundaje). The output of t...

## Key facts

- **NIH application ID:** 10658683
- **Project number:** 1R01NS128028-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** William James Greenleaf
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $611,882
- **Award type:** 1
- **Project period:** 2023-04-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10658683

## Citation

> US National Institutes of Health, RePORTER application 10658683, Defining and perturbing gene regulatory dynamics in the developing human brain (1R01NS128028-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10658683. Licensed CC0.

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