The BRAIN Initiative: The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative® is aimed at revolutionizing our understanding of the human brain. By accelerating the development and application of innovative technologies, researchers will be able to produce a new dynamic picture of the brain that, for the first time, will show how individual cells and complex neural circuits interact in both time and space. It is expected that the application of these new tools and technologies will ultimately lead to new ways to treat and prevent brain disorders.
NIH is one of several federal agencies involved in the BRAIN Initiative. Planning for the NIH component of the BRAIN initiative is guided by the long-term scientific plan, “BRAIN 2025: A Scientific Vision,” which details seven high-priority research areas and calls for a sustained federal commitment of $4.5 billion over 12 years. This FOA is based on careful consideration by the NIH of the recommendations of the BRAIN 2025 Report, and input from the NIH BRAIN Multi-Council Working Group. Videocasts of the NIH BRAIN Multi-Council Working Group are available at http://www.braininitiative.nih.gov/about/mcwg.htm.
To enable rapid progress in development of new technologies as well as in theory and data analysis, the BRAIN Initiative encourages collaborations between neurobiologists and scientists from statistics, physics, mathematics, engineering, and computer and information sciences; and NIH welcomes applications from investigators in these disciplines.
NIH encourages BRAIN Initiative applications from investigators that are underrepresented in the biomedical, behavioral, or clinical research workforce (see data at http://www.nsf.gov/statistics/showpub.cfm?TopID=2&SubID=27 and the most recent report on Women, Minorities, and Persons with Disabilities in Science and Engineering). Such individuals include those from underrepresented racial and ethnic groups, those with disabilities, and those from disadvantaged backgrounds.
NIH also encourages businesses to participate in the BRAIN Initiative. It is possible for companies to submit applications directly to BRAIN Initiative program announcements or to collaborate with academic researchers in joint submissions. Small businesses should consider applying to one of the BRAIN Initiative small business FOAs (http://braininitiative.nih.gov/funding/index.htm).
In addition to the National BRAIN initiative, the NIH continues to have a substantial annual investment in neuroscience research. The Institutes and Centers contributing to the NIH BRAIN Initiative (http://braininitiative.nih.gov/) support those research efforts through investigator-initiated applications as well as through specific FOAs. Potential applicants to this FOA are strongly encouraged to contact Scientific/Program staff if they have any questions about the best FOA for their research.
The BRAIN Initiative will require a high level of coordination and sharing between investigators.
This FOA is related to the Recommendations in Section III.1 and 2 of the Final Report (http://braininitiative.nih.gov/2025/index.htm) of the BRAIN working group. Specifically, this FOA solicits applications that will address the recommendations in “Section III.1. Discovering Diversity” and “Section III.2. Maps at Multiple Scales.”
The mammalian brain contains an astronomical number of cells. There are an estimated 1.13 x 10^8 cells in the mouse brain and an estimated 1.7 x 10^11 cells in the human brain, with each neuron making thousands of synapses with other cells. Since the early work of Ramón y Cajal, beginning with the elegant staining of individual neurons in the brain using the Golgi method, brain cell types have been increasingly defined by their location, morphology, connectivity, neurotransmitter type, physiology, and most recently, their transcriptional profile. Cataloging brain cell types and their connectivity is a prerequisite to understanding how they are organized in circuits, and how they change in brain disorders. In addition, a detailed understanding of cell classes and subclasses will enable the development of novel tools that allow researchers to target specific cell types and manipulate circuits for further study. However, there is not yet a consensus on what a brain cell type is, since a variety of factors including experience, cell interaction, and neuromodulators can diversify the molecular, electrical, and structural properties of similar cells, and cell phenotypes may change over time. Nonetheless, there is general agreement that cell types can be defined provisionally by invariant and generally intrinsic properties, and that this classification can provide a good starting point for a census. A workshop co-sponsored by the BRAIN Initiative and the NIH Single Cell Analysis Program shared information on how investigators are currently describing cellular phenotypes and novel approaches to better quantify, evaluate, understand, and communicate the brain cell classification. The consensus is that classification of cell types will be facilitated by a systematic collection and integrative analysis of three data elements at the cellular level: (1) molecular signature (e.g., transcriptome, epigenome, proteome, metabolome), (2) anatomy (e.g., location, size, orientation, morphology, and connectivity), and (3) function (e.g., electrophysiology, functional connectivity). Current technological capabilities promise a new era in the call for a brain cell census hallmarked by high dimensionality of molecular information at an unprecedented scale and resolution. Single cell ‘omics’ analyses (transcriptomics, epigenomics, and proteomics) will likely help define unique cell type markers and unveil the regulatory code that controls cell type formation, maintenance, and transition in health and disease. The new molecular insights gained may thus transform our understanding of cells types by revealing fundamental biological principles with mechanistic underpinnings to define discreet cell classes as well as relevant transition states.
Research Objectives
The BRAIN Initiative Cell Census Network (BICCN)
The BRAIN Initiative Cell Census program awarded nine collaborative projects in 2017 and five in 2018, which collectively constitute the BRAIN Cell Census Network (BICCN). The overarching goal of the BICCN is to generate comprehensive 3D common reference brain cell atlases that will integrate molecular, anatomical, functional, and cell lineage data for describing cell types in mouse, human, and non-human primate brains.
The expected outcomes of the BICCN include:
- fundamental knowledge on diverse cell types and their three-dimensional organizational logic in the brain;
- an open-access 3D digital brain cell reference atlas with molecular, anatomical, and physiological annotations of brain cell types in mouse;
- a comprehensive neural circuit diagram in mouse brain;
- reagents for cell-specific targeting;
- validated high throughput and low-cost approaches to characterizing cell diversity in human and/or non-human primate brain samples.
The BICCN operates as a cooperative network to promote collaboration and coordination among the projects within the Network and the BRAIN Initiative, as well as with any external research entities that have similar goals. Currently the BICCN has established close collaboration and coordination relationship with Data Archive projects funded under RFA-MH-17-255. It is expected that the BICCN awardees and their collaborators will work together to achieve the common goals. This will involve regular meetings and other coordinated activities within the BICCN as well as in the BRAIN Initiative and more broadly with the research and education communities. Thus, the BICCN will leverage existing atlases and common coordinate systems to facilitate collaborative efforts for the data annotation and 3D spatial mapping.
Common Brain Tissues and Coordinate Systems
A large amount of data concerning brain anatomy and physiology exists and continues to grow rapidly. With the advent of single-cell ‘omics’ technologies, new biomolecular data are expected to flourish, adding to the existing expansion of data sets. Correspondingly, there is an increasing need to enhance data interoperability and harmonization among data producers and data accessibility to the broad research community, and to reduce unnecessary repetition in data generation. Atlases and common coordinate systems play a fundamental role in gathering, analyzing, communicating, and standardizing data. This FOA embraces the existing effort of the research community (e.g., the International Neuroinformatics Coordination Facility) to collaboratively build up brain atlases with broadly accessible common brain coordinate systems to integrate and disseminate the brain cell census data. Thus, this FOA supports the use of common brain samples, and common brain coordinate systems to minimize source variability and maximize resource sharing. Accordingly, the NIH expects that imaging–based cell census data will be registered to common coordinate systems, which include in-situ hybridization, immunohistochemistry, cell morphology, and neuronal connectivity mapping. In case non-imaging-based approaches are used, applicants should propose how to spatially assign the data to the brain regions as accurately as possible. For example, microdissection and computational tools may help map single cell data onto a reference brain atlas spatially.
Much progress has been made to develop and implement common coordinate systems for human brain (e.g., Allen Human Brain Atlas, BigBrain, BrainSpan, Talairach Coordinate System, MNI Coordinate System) and image segmentation and registration tools (e.g, Insight Segmentation and Registration Toolkit (ITK)) that allow individual labs to integrate their data to the common coordinate systems.
Research Scope of U01 Specialized Collaboratory on Human and Non-Human Primate Brains
As primate brains are several orders of magnitude larger than the mouse brain in size and number of cells, the NIH expects that the U01 Specialized Collaboratory on Human and Non-Human Primate Brains will begin to implement high throughput approaches and to establish experimental feasibility towards the generation of comprehensive reference brain atlases for larger brains at cellular resolution. The data production goals should be as comprehensive and complete as possible with a broad coverage of multiple different brain structures/regions and adequate depth in characterizing multiple cellular properties. Applicants should propose to use methods that have been demonstrated to generate high-quality data, to be cost-effective, and to have the capability to accurately and efficiently define cell types. Scalable and multiplexed approaches are expected to enable a comprehensive cell type survey. In addition, applicants are encouraged to adopt technology platforms that are capable of acquiring multimodal datasets from the same cells with adequate throughput. For example, RNA fluorescence in situ hybridization (FISH) may be combined with immunohistochemistry labeling to monitor both RNA and protein molecules in the same cells. Overall, the projects will strive to:
- establish brain tissue sources by characterizing and minimizing degradation of brain tissue during collection and preparation for multiple imaging and omics assays;
- establish sampling strategies by estimating sampling depth required for prospective investigations of brain cell heterogeneity;
- establish data standards, 3D common coordinate frameworks, mapping protocols, and technical parameters of the assays in a consistent and interoperable format by coordinating with other projects;
- characterize and minimize technical variations and false-negative or false-positive events of the assays;
- identify and analyze discrete vs continuous, transient vs stable, autonomous vs communal phenotypes and features of cells to discover cell spatial organizational principles;
- annotate and interpret data with common ontologies;
- integrate and link molecular and anatomical data sets to build a multilevel and multiscale brain cell map;
- understand the degree of brain cell organizational variability from different individuals and changes across the lifespan.
Applicants are expected to provide tissue collection approaches and criteria to ensure the quality of the brain tissue specimens and describe plans for biospecimen management and minimization of tissue degradation.
For the proposed use of human brain specimens, applicants are expected to develop inclusion and exclusion criteria that will minimize the risk of abnormal or degraded tissue. While postmortem healthy human brain tissues were previously collected by different projects (e.g., NIH NeuroBioBank; GTEx program, Biopreserv Biobank. 2015 Oct;13(5):311-9), new tissue sources may need to be established for the brain cell census research. Applicants are strongly encouraged to pursue broad donor consent for unrestricted sharing of data for research purposes to maximize the utility of biospecimens and data (see ENCODE project for Informed Consent examples). Applicants are also encouraged to consider Ethical, Legal and Social Issues (ELSI) of tissue collection and to consider return of results to donors or their families.
Examples of responsive research activities include but are not limited to the following 3 themes:
1. Molecular Signatures
Applicants should adopt scalable experimental approaches that will maximize the discovery and classification of all brain cell types. The research objectives may include but are not limited to:
- generating spatially defined single cell transcriptome data to unveil brain cell classes and types based on transcriptome signatures;
- generating immunohistochemistry and immunocytochemistry data using validated protein affinity reagents to identify specific cell types and neurite projections;
- generating spatially defined single cell epigenome data (e.g., chromatin accessibility, DNA methylation) to help define brain cell types;
- determining microenvironment, tissue composition and ratio of various cell types (e.g., neurons, glial cells, vascular cells, immune cells, progenitor cells, synapses, spines, stroma) in the brains.
2. Anatomy
Applicants should leverage existing reference anatomical atlases and adopt scalable experimental approaches that will maximize the discovery and classification of all brain cell types and/or neuronal connections. The research objectives may include but are not limited to:
- generating comprehensive novel data specifying cell spatial location and morphology (e.g., cell size and shape);
- generating novel mesoscale long- and short-distance neuronal connectivity map;
- discovering monosynaptic input and output neurons (e.g., using retrograde and anterograde viral tracings);
- determining microenvironment, tissue composition and ratio of various cell types (e.g., neurons, glial cells, vascular cells, immune cells, progenitor cells, synapses, spines, stroma) in the brains;
- generation of new 3D brain atlases and common coordinate frameworks for aggregating anatomical and other data types
3. Functional Measures
Applicants should adopt scalable experimental approaches that will maximize the discovery and classification of all brain cell types. The research objectives may include but are not limited to:
- generating electrophysiological and cell morphological data from neuron types with defined spatial location;
- generating functional connectivity data (e.g. synaptic input field, network modules) from neuron types with defined spatial location and cell morphology using advanced microscopic imaging methods.
The brain cell census data will be an important and unique resource for use by the broad research community, and thus the following issues related to the large-scale data production are paramount to the success of the program.
Data Quality. Two of the cornerstones of science advancement are rigor in designing and performing scientific research and the ability to reproduce biomedical research findings. (http://grants.nih.gov/reproducibility/index.htm). The BICCN is expected to establish stringent data quality standards and quality control and quality assurance processes for the experimental and statistical approaches, so that the data generated will be broadly referenced and used by the research community.
Data Comprehensiveness & Completeness. The data production goals should be as comprehensive and complete as possible with a broad coverage of different brain structures/regions and adequate depth in characterizing multiple cellular properties. In practice, achieving a “comprehensive and complete” census of brain cells that include neurons, glia cells, and other cell types will require careful planning of workflow, strategic allocation of resources, and optimal lineup of complementary technologies. The purpose is to establish a Network with complementary capabilities and capacities toward generating a comprehensive and complete brain cell atlas.
Data Utility. The ultimate utility of cell census data for the broad research community may reside in an effective integration of different cell census data elements including molecular content, cell anatomy, and physiology to define a cell type. Each element of data may be limited in its own value, but combined, the data collected by individual projects should inform and crossvalidate each other to arrive at an integrative description closer to nature. Rapid data exchange and integration are critical for defining a cell type and unveiling the organizational rules behind cellular makeup and neural circuits. The BICCN Collaboratories are expected to abide by the agreed data sharing policy and process to ensure unhindered data exchange and sharing.
Production Workflow. As the ultimate long-term goal is to establish comprehensive reference brain cell atlases, the BICCN should attain a high level of production at an affordable cost by adopting scalable technology platforms and streamlined workflows. As with other large-scale data generation efforts, the BICCN Collaboratories are expected to have the capability to operate at scale at the inception of the project, establish adequate process control with quantitative quality metrics at key points in the production workflow, and have plans to improve production workflow and cost efficiency.
Each U01 Collaboratory must include data management and analysis activities to provide central data storage, data management and information security services to all researchers within the Collaboratory, and will be responsible for ensuring the timely submission of data and data analyses to the Brain Cell Data Center (BCDC) funded under RFA-MH-17-215. In addition, the Collaboratory should have bioinformatics expertise to support data integration and analysis according to the Collaboratory’s research objectives, including mining and integrating existing data and information, and assisting study design. As appropriate, the Collaboratory may perform statistical analysis of single cell ‘omics data to identify and classify cell types, discover unique cell type markers, predict spatiotemporal relations, and infer gene expression regulatory mechanisms underlying cell type formation, maintenance, and transitional states. The Collaboratory’s data expert(s) will represent the Collaboratory in a cross-BICCN data sub-committee that will operate under the Steering Committee to promote coordination and collaboration among all BICCN Centers and Collaboratories including data sharing, data harmonization, refinement of data standards, data analysis and integration, data mapping to common brain coordinate systems, and data visualization.
Each U01 Collaboratory must also include administrative activities to coordinate the Collaboratory activities and facilitate its integration into the broader BICCN, as well as include mechanisms that will foster effective interactions with other BICCN network Program Director(s)/Principal Investigator(s) (PD(s)/PI(s)) and institutions to promote synergistic research efforts. The effective management of a production collaboratory requires a significant commitment by the PD/PI. A program manager may in addition strengthen the collaboratory administration. The Collaboratory administration will be responsible for organization, management, decision-making, and periodic evaluations of individual groups within the Collaboratory, involvement of institutional and other resources, and shared publications.
Milestones and timeline: The success of BICCN will be facilitated by the adoption of clear, quantitative milestones by each of the participating Centers and Collaboratories with a realistic and efficient timeline. Applications lacking milestones and timeline will be deemed incomplete and will not be reviewed (See Section IV.2).
Activities that are not responsive: The following research areas are considered outside the research scope of this FOA, and such applications will be considered non-responsive and will not be reviewed:
- Studies primarily focused on the pursuit of a biological mechanism or a hypothesis through basic research that does not result in the generation of comprehensive brain cell maps;
- Studies of cultured cells, isolated cell samples and/or stem cell lines that are maintained under culture conditions;
- Studies primarily focused on technology development. Note: Applications for technology development may be submitted to a separate BRAIN FOA (RFA-MH-19-136).
Deadlines: December 22, 2018 and December 24, 2019 (letters of intent); January 22, 2019 ; January 24, 2020 (full proposals
URL: https://grants.nih.gov/grants/guide/rfa-files/RFA-MH-19-149.html
Filed Under: Funding Opportunities