# Revealing the transcriptomic basis of neuronal identity through functional meta-analysis

> **NIH NIH R01** · COLD SPRING HARBOR LABORATORY · 2021 · $480,000

## Abstract

PROJECT SUMMARY
Our overarching goal is to understand how the relationships between genes contribute to functional properties
in neurons and how those functions combine to define types of neurons. This is a central question of basic
neuroscience, and one which is newly assessable using single cell RNA sequencing (scRNA-seq). These data
provide high-throughput snapshots of gene activities across thousands of cells and thus shed new light on the
relationships between genes within and across cells. We propose to exploit this data in conjunction with
previously known details about gene function and neuronal identity to learn new features of both. Our
research approach is meta-analytic, using data from many different laboratories to obtain a more robust
aggregate signal. In addition to developing meta-analytic methods to pursue our direct research interests, the
methods are of broad practical relevance to neuroscience laboratories studying many different questions,
including diseases of the nervous system. Disseminating our software deliverables in a convenient-to-use form
is a central component of each of our research objectives.
The three complementary objectives in this project are to:
1. Learn patterns of gene expression which characterize known cell identity. Building on our
previous research showing conserved expression patterns across cell-types, we will define shared gene
expression patterns, called co-expression, specific to neuronal sub-populations. These shared expression
patterns will be used as an assay into cellular identity.
2.Identify novel cell subtypes through changes in the expression relationships between genes.
Variation in co-expression is a form of transcriptional rewiring which often indicates a change in function. To
find novel neuronal sub-types we will assess the data for changes in co-expression reflecting a change in
functions linked to neuronal identity. We will identify novel transcriptional signatures which replicate across
laboratories.
3. Determine consensus methods for customized cell-type learning. Defining wholly unknown
expression profiles is likely to benefit from a variety of approaches. In order to find agreement between those
approaches, we will develop an algorithm to efficiently search through gene sets likely to find those with
complementary value. These gene sets will then be assessed across many pre-existing methods, with
customized combinations and aggregate output reported and made available through a public web-server.

## Key facts

- **NIH application ID:** 10224662
- **Project number:** 5R01MH113005-05
- **Recipient organization:** COLD SPRING HARBOR LABORATORY
- **Principal Investigator:** Jesse Gillis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $480,000
- **Award type:** 5
- **Project period:** 2017-07-13 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224662

## Citation

> US National Institutes of Health, RePORTER application 10224662, Revealing the transcriptomic basis of neuronal identity through functional meta-analysis (5R01MH113005-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10224662. Licensed CC0.

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