Computational Methods for Integrating Tissue and Single Cell Genomic Data from the Brain (R01 Clinical Trial Not Allowed) | RFA MH 20 300

Frequently Asked Questions (FAQs)

What is the title of this grant opportunity?

The opportunity is titled "Computational Methods for Integrating Tissue and Single Cell Genomic Data from the Brain (R01 Clinical Trial Not Allowed)."

What is the Funding Opportunity Number (FOA number)?

The Funding Opportunity Number is RFA-MH-20-300.

What agency is offering this funding opportunity?

This is an NIH (National Institutes of Health) discretionary research grant opportunity.

What is the assistance listing / CFDA number and scientific area?

The CFDA (assistance listing) number provided is 93.242, and the area is health, with a focus on computational/statistical neuroscience and mental health research.

What grant mechanism is being used?

The mechanism is an R01 research project grant.

Are clinical trials allowed under this FOA?

No. The FOA is explicitly labeled "Clinical Trial Not Allowed," so supported projects should not include interventional clinical trials that prospectively assign human participants to interventions.

What is the central goal of the FOA?

The central goal is to support projects that develop new computational/statistical methods or apply cutting-edge existing methods to integrate two major types of brain genomic data: bulk or region-level tissue data and single-cell genomic data.

What kinds of data does this FOA focus on integrating?

The FOA focuses on integration of (1) tissue-level (bulk or region-level) genomic assays from the brain and (2) single-cell genomic data from the brain, aiming to connect signals across these different biological and technical scales.

Why does NIH emphasize integrating tissue-level and single-cell brain genomic data?

Tissue-level assays capture broad molecular signals across brain regions but typically mix signals from many cell types. Single-cell methods can resolve signals by cell type or cell state but can face issues like sampling limits, sparsity, and batch effects. Integrating both aims to produce clearer, more mechanistic, cell-resolved understanding of gene regulation in the brain relevant to mental disorders.

What scientific outcomes is NIH aiming to accelerate through this FOA?

The FOA aims to accelerate understanding of how specific brain cell types contribute to mental disorders, especially by identifying cell type-specific gene regulatory networks and biological pathways involved in disease development (pathogenesis) and disease mechanisms (pathophysiology).

What types of projects are considered responsive to this FOA?

Projects are responsive if they develop tools, computational pipelines, statistical frameworks, or integrative analyses that improve inference about cell type-specific gene regulation, network structure, and pathway involvement from complex brain genomic datasets by integrating tissue-level and single-cell data.

Does the FOA support development of new methods, or only application of existing methods?

Both are within scope. The FOA supports projects that either develop new methods or apply cutting-edge existing methods for integration of tissue-level and single-cell brain genomic data.

What is the core theme of this announcement?

A core theme is integration across data scales, connecting region-level tissue signals with cell type- and cell state-resolved single-cell signals to improve mechanistic interpretation.

What technical and analytical challenges does the FOA expect applicants to address?

Examples of challenges highlighted include cell type deconvolution, batch correction, cross-platform harmonization, linking regulatory elements to target genes, network inference, pathway analysis, and validation using independent datasets or orthogonal evidence.

What kinds of deliverables are implied as strong outputs for a competitive application?

The FOA emphasizes rigorous methodology, feasibility, and clear deliverables such as software, reproducible workflows, validated models, or high-impact integrative findings from brain genomic datasets.

Is the FOA limited to a particular type of brain cell?

No specific cell type is limited in the provided description. The FOA references neuronal and non-neuronal populations and focuses on cell type-specific and cell state-resolved gene regulation in the brain.

How does this FOA relate to mental health research?

The FOA targets computational approaches that can clarify how cell type-specific gene regulatory programs and pathways relate to mental illness biology, aiming to improve mechanistic models of disease processes in the brain.

Does this FOA require generating new data?

The description indicates projects may be "computational, statistical, and/or based on existing or newly generated genomic data," but it does not specify a requirement to generate new data.

What is meant by "bulk or region-level tissue data" in this context?

In the FOA description, tissue-level data refers to assays that measure genomic signals across brain regions or tissue samples, capturing broad molecular patterns while generally mixing contributions from multiple cell types.

What is meant by "single-cell genomic data" in this context?

Single-cell genomic data refers to approaches that measure genomic signals at the resolution of individual cells (or closely related single-cell profiles), enabling separation of signals by cell type or cell state but often with technical challenges such as sparsity and batch effects.

Who is eligible to apply?

Eligibility is broad and includes many U.S. and non-U.S. organizations, spanning government entities, higher education institutions, nonprofits, for-profits, small businesses, and other eligible entities listed in the FOA description.

Are non-U.S. (foreign) organizations eligible?

Yes. The FOA explicitly includes non-domestic (non-U.S.) entities (foreign organizations) as eligible applicants.

Are U.S. tribal governments and tribal organizations eligible?

Yes. The FOA includes federally recognized Native American tribal governments and tribal organizations, as well as tribal governments that are not federally recognized.

Are organizations that serve underrepresented communities explicitly included in eligibility?

Yes. The FOA highlights eligibility for institutions such as HBCUs, TCCUs, Hispanic-serving Institutions, AANAPISIs, and Alaska Native and Native Hawaiian Serving Institutions.

Are faith-based or community-based organizations eligible to apply?

Yes. The FOA explicitly includes faith-based or community-based organizations among eligible applicants.

Are U.S. territories or possessions eligible?

Yes. The FOA includes U.S. territories or possessions as eligible applicants.

Are federal agencies eligible to apply?

Yes. The eligibility list includes eligible federal agencies.

Are for-profit organizations and small businesses eligible?

Yes. The eligibility list includes for-profit organizations (other than small businesses) and small businesses.

Are nonprofits eligible even if they do not have 501(c)(3) status?

Yes. The eligibility description includes nonprofit organizations with or without 501(c)(3) status (other than institutions of higher education).

Are universities eligible?

Yes. Both public/state-controlled institutions of higher education and private institutions of higher education are listed as eligible.

When was this opportunity created and when was it published?

The creation date provided is June 26, 2019, and it was published by NIH with an original closing date listed as September 3, 2019.

What is the original closing date shown for this FOA?

The original closing date provided is September 3, 2019.

Is the award ceiling (maximum award amount) provided?

No. The provided source fields do not specify an award ceiling.

Is the expected number of awards provided?

No. The provided source fields do not specify the expected number of awards.

How should applicants think about budget and project scope given limited award details?

Based on the description, applicants would typically shape budgets and timelines to match an R01 scope, emphasizing rigorous methodology, feasibility, and concrete deliverables such as tools, pipelines, statistical frameworks, and validated integrative analyses.

What distinguishes a strong application according to the description?

A strong application would clearly define the computational problem in brain genomics integration, explain why existing approaches are insufficient, propose well-justified integration methods for tissue-level and single-cell datasets, address key data challenges (for example, deconvolution and batch effects), and include validation strategies using independent datasets or orthogonal evidence.

What is the broader payoff NIH is seeking from funded projects?

The intended payoff is a more detailed, cell-resolved understanding of molecular circuitry in the brain that clarifies how mental disorders arise and persist and informs better hypotheses for future experimental work and therapeutic discovery, even though clinical trials are not supported under this announcement.