# Discovering the molecular genetic principles of cell type organization through neurobiology-guided computational analysis of single cell multi-omics data sets

> **NIH NIH RF1** · DUKE UNIVERSITY · 2021 · $1,401,426

## Abstract

ABSTRACT
Understanding the biological principles of cell type diversity and organization is necessary for deciphering
neural circuits underlying brain function. The recent rapid accumulation of single cell transcriptomic and
epigenomic data sets provides unprecedented opportunity to explore the molecular genetic basis of cell
type identity, diversity, and organization. However, analysis of multi-omics datasets have been largely driven
by statistic methods that typically do not engage the deep knowledge of neurobiology and developmental
biology. As such, most statistic methods do not distinguish technical noise and methodological biases from
biologically relevant signals and relationships, and have limited power in achieving biological discovery and
insight. Neurobiology guided feature selection based on inherent physiological and developmental
processes is essential to move beyond simple statistical clustering of molecular types towards achieving
multi-modal definition of neuron types and revealing their inherent relationships as a taxonomy. We have
discovered that transcriptional architectures of synaptic input/output (I/O) communication may underlie the
essence of cortical GABAergic neuron identity. We hypothesize that transcriptional architectures of synaptic
communication is a general defining feature for brain neuron types. We will test this hypothesis by
performing a series of supervised learning and feature selection analyses of publically available and
emerging single sc-transcriptomic data sets across brain areas and systems from the BRIAN Initiative Cell
Census Network (BICCN). We further hypothesize that cell type transcriptional signatures of synaptic
communication is orchestrated by well-defined gene regulatory programs rooted in epignomic landscape.
We will test this hypothesis by joint analysis of sc-transcriptome, ATACseq, and DNA methylome dataset of
the same cortical cell populations from BICCN to identify co-expressed gene signatures that reliably define
cell identity, focusing on signatures of synaptic communication. Based on transcriptomic and epigenomic
architecture of synaptic communication, we will further develop neurobiology guided feature selection
algorithms to improve and refine the current statistical clustering the cortical transcriptomic types. In
addition, we will generate web portal tools for automated classification of transcriptomic cell types. Our study
will establish a unified paradigm of neuronal cell type organization in which epignomic landscape configures
core gene regulatory programs to shape synaptic communication properties that define cardinal neuron
types. Together, this work will establish a molecular genetic framework for understanding neuronal diversity
and achieving a biological classification across brain areas and mammalian species.

## Key facts

- **NIH application ID:** 10189902
- **Project number:** 1RF1MH125921-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Z JOSH HUANG
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,401,426
- **Award type:** 1
- **Project period:** 2021-05-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189902

## Citation

> US National Institutes of Health, RePORTER application 10189902, Discovering the molecular genetic principles of cell type organization through neurobiology-guided computational analysis of single cell multi-omics data sets (1RF1MH125921-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10189902. Licensed CC0.

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