Research Projects

Project summary, Scalable technologies for brain-wide connectomics of transcriptomic cell types: focus on brainstem This proposal is to develop a scalable pipeline to combine high-resolution morphology and molecular classification of individual neurons to define morpho-molecular cell types in the brain. Complete morphology of individual neurons provides insights of connectivity and information processing in the brain and reveals how neuronal activity is routed across brain areas.

PROJECT ABSTRACT To meet the goal of the BRAIN Initiative Cell Census Network to catalogue and produce molecular profiles of every cell type in the human brain; order-of-magnitude improvements in single-cell assay throughput and coverage are required.

The complexity of the mammalian brain is unparalleled by any other organs, and understanding its cellular composition and their brain-wide organization is essential to understand the brain functions and dysfunctions. Extensive efforts have been made toward mapping brain cells through various lenses, and have established invaluable databases yielding new insights. However, only a few molecules, or cell-types, or regions per brain have been mapped in non-human primate and human brains, and fail to capture brain-wide complex intercellular relationships within individuals.

Development of a high throughput system for molecular imaging of different cell types in mouse brain tissues Mass spectrometry imaging (MSI) is a powerful tool for developing detailed molecular maps of biological tissues with high specificity and sensitivity. This label-free technique enables simultaneous imaging of multiple classes of molecules including lipids, metabolites, and proteins thereby advancing the understanding of tissue organization and function.

PROJECT SUMMARY This project is a collaboration across two universities and multiple scientific disciplines to develop new scalable 3D molecular imaging and analysis approaches for cell type identification within human brain tissue.

Digitized reference brains, also referred to as Common Coordinate Frameworks (CCFs), together with superposed atlas annotations, are of central importance to neuroscience. They bear the same relation to neuroscience as do reference genomes and genome annotations to cellular and molecular biology. Strikingly, however, such reference brains for humans lag far behind the corresponding CCFs for non-human model organisms such as the laboratory mouse. Existing data sets either have sections spaced relatively far apart or lack in-plane resolution down to the micron scale.

SUMMARY The human nervous system is possibly the most complex biological tissue, organized into multiple functionally distinct regions and comprised of over 200 billion neural and non-neural cells, requiring novel scalable tools to profile cell types and relationships in the tissue context with high spatial resolution.

ABSTRACT The brain is the most complex organ in the mammalian body. Bulk analysis obscures heterogeneity of cell types present even in the smallest brain regions. Multi-omics single-cell resolution 3D-characterization of brain tissue is critically important to create comprehensive brain cell censuses and altas. Recent technological advances allow for single-cell transcriptome mapping of mammalian brains, but single-cell proteomics technologies are lagging far behind transcriptomics technologies.

PROJECT SUMMARY A major challenge in studying the mammalian brain is to characterize the integrative properties of individual neurons, such as molecular profiles, complete morphology (dendrites, axons, synapses), connectivity, and activity; furthermore, this must be done at a scale that is commensurate with the goal of understanding all the neurons and their circuitry in the brain. While current single-cell transcriptomic and epigenomic profiling techniques are highly quantitative, scalable and informative, the technologies to study other neuronal cell-type defining properties(e.g.

Project Summary / Abstract A fundamental goal in neuroscience is understanding how information is processed in neuronal circuits. However, the immense complexity of most brain networks has been a significant barrier to progress. Neurons are a primary computational component of the brain, yet we do not have a comprehensive list of their types for even the simplest mammalian neuronal circuit. Moreover, a neuron’s function is dependent on how it is connected, yet mammalian neuronal networks consist of billions of cells with trillions of connections.