NIH – BRAIN Initiative Cell Census Network (BICCN) Scalable Technologies and Tools for Brain Cell Census (R01 Clinical Trial Not Allowed)

October 1, 2018 by School of Medicine Webmaster

The BRAIN Initiative Cell Census program awarded 9 collaborative projects in 2017 and 5 in 2018 under four companion FOAs (RFA-MH-17-210-215-225, and -230), 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 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. The BICCN 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 development and use of common brain coordinate systems to maximize data and 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. When non-imaging-based approaches are used, applicants should spatially assign the data to the brain regions as accurately as possible. For example, microdissection and computational tools may help map single cell sequencing 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 AtlasBigBrainBrainSpan, 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.

This FOA is related to the implementation of the Recommendations in “Section III.1. Discovering Diversity” and “Section III.2. Maps at Multiple Scales”of the Final Report ( of the BRAIN working group.

Research Objectives

The purpose of this FOA is to accelerate the integration and use of scalable technologies and tools to enhance brain cell census research, including the development of technology platforms and/or resources that will enable a swift and comprehensive survey of brain cell types and circuits.

Cell types have been increasingly defined by their location, morphology, connectivity, neurotransmitter type, lineage, function, and most recently, their transcriptomic and epigenomic profiles. Despite a general challenge in defining and distinguishing a cell type from a cell state due to the physiological variability and plasticity of a cell, there is general agreement that cell types can be defined provisionally by stable and generally intrinsic properties of a cell, including (1) molecular signature (e.g., transcriptome, epigenome, proteome, metabolome), (2) anatomy (e.g., location, size, orientation, morphology, and connectivity), (3) function, and (4) development and differentiation. This definition of a cell type can provide a good starting point for a systematic cell census, and a foundation for understanding how brain cells are organized and connected as the source of perceptions, actions, and memories. Recent rapid technological advancements enable the possibility of a large-scale brain cell census in mammalian brains. However, there still exist multiple technology and resource gaps and roadblocks that limit the capability, the breadth, and depth of the current brain cell census research. This FOA solicits applications to overcome these shortcomings and aim for a comprehensive and complete analysis of cell properties and neuronal connectivity in human, non-human primate, and mouse brains. The projects are expected to augment the ongoing systematic collection and integrative analysis of cell census data by the BICCN.

Tools/technologies and studies relevant for this initiative should be scalable and are expected to significantly improve the technical performance including throughput, sensitivity, selectivity, spatiotemporal resolution and robustness. Of interest are those tools/technologies that have potential to enable comprehensive and complete cell census research for human, non-human primate, and mouse brains. In particular, the FOA supports (a) improving technology and resource platforms to remove limitations and bottlenecks in the current pipeline of brain cell census data generation; (b) integrating experimental and computational methods to enhance capabilities of cell census data generation and analysis and to reduce barriers to hypothesis-driven research; (c) generating a substantial amount of spatiotemporal cell census data and/or resources to demonstrate the utility and performance of the improved technology and resource platforms; and (d) conducting comparative studies by using proper criteria to evaluate and benchmark quality of biospecimen, performance of cell census tools/technologies, and effectiveness of computational approaches. Applications are expected to address limitations and gaps of existing technologies/tools and cell census research as a benchmark against which the improvements or competitive advantages of the proposed ones will be measured.

Examples of scalable tools/technologies of interest and studies include but are not limited to:

(1) Molecular profiling

  • high-throughput transcriptomics, epigenomics, proteomics, and/or metabolomics to enable large-scale cell census in a comprehensive manner
  • highly sensitive and accurate -omics assays to enable detailed analyses of complete molecular signatures
  • multiplexed imaging-based spatial profiling of RNAs, DNA methylation and chromatin states, proteins, and/or metabolites in individual cells across whole brain
  • simultaneous detection and assay of multiple biomolecule species (e.g., imaging both RNAs and proteins in same cells)
  • improved spatial -omics methods to acquire both molecular signatures and 3D cell location and morphology
  • generation of cell census tools and reagents including protein capturing reagents (e.g., monoclonal antibodies, recombinant antibodies, nanobodies), genetic and non-genetic cell-specific targeting tools (e.g., viral vectors), labeling reagents (e.g., super bright dyes), molecular markers (e.g., nucleic acid scaffolded sensors)

(2) Neuron morphology

  • innovative approaches that will improve the throughput and accuracy of single neuron morphology reconstruction
  • efficient approaches to brightly labeling whole neuron morphology including dendritic and axonal terminals presynaptic and postsynaptic structures with adequate sparsity
  • generation of improved genetic (e.g. enhancers, promoters) viral reagents to label defined neuronal types
  • implementation of high-throughput, high-resolution, and brain-wide imaging
  • Efficient computational methods for defining, comparing, and classifying topological and topographic features
  • implementation of computational approaches for digital reconstruction, analysis, and 3D brain mapping of neuron morphology

(3) Neuron connectivity and circuit diagram

  • comprehensive mapping of connected neurons in local circuits and whole brain at subcellular resolution
  • high resolution imaging of circuit structural components including dendritic spines, synaptic junctions, axons, dendrites, and soma
  • improved synaptic and trans-synaptic markers, viral and non-viral tracers for antero-, retro-grade, and/or trans-synaptic tracing
  • improved labeling tools and technologies to visualize neuron synapses with trackable axons and dendrites for circuit diagram
  • comprehensive mapping of chemical and electrical synapses
  • implementation of improved computation approaches for the image processing, segmentation, analysis, 3D brain registration, and data annotation
  • cross-scale and cross-modality neuron connectivity map and analysis

(4) Cell lineage and development

  • implementation of high-throughput methods for a comprehensive characterization of molecular and anatomical signatures of brain cells at different developmental stages
  • systematic study of developmental cell types and lineage relationships combining single-cell omics and lineage tracing methods in mouse and non-human primate brains
  • establishment of 3D reference brain atlases and common coordinate frameworks for developmental brain maps
  • implementation of computation approaches to analyze, integrate, and map the developmental brain cell census data to 3D common coordinate frameworks

Applications funded under this RFA are expected to align their specific aims with the overarching goal of the BICCN 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 awardees are expected to generate a substantial amount of cell census data and/or resources during the project period using the proposed technologies or via collaboration with the BICCN and are expected to follow the BICCN’s resource and data sharing policy.

Milestones and timelines:  The success of the project will be facilitated by the adoption of clear, quantitative milestones with realistic and efficient timelines. Applications are expected to include annual milestones with metrics that will document progress towards the achievement of the specific aims.

Applicants are strongly encouraged to consult the appropriate Scientific/Research Contact, listed below, to discuss the alignment of their proposed work with the goals of this FOA and BRAIN Initiative Program.

Deadlines:  December 22, 2018 and December 24, 2019 (letters of intent); January 22, 2019 and January 24, 2020 (full proposals)


Filed Under: Funding Opportunities