# C4: Neuroanatomy > **NIH NIH U19** · PRINCETON UNIVERSITY · 2024 · $457,594 ## Abstract Project Summary/Abstract: Core 4, Neuroanatomy The overall goal of this U19 collaboration is to elucidate how working memory and decision-making are supported by interacting neurons and brain regions. To achieve this goal, our research projects will need cutting-edge neuroanatomy tools, which the Neuroanatomy Core will provide. As it has done in the first U19 funding period, this core will continue to support protocols, software, and standards for light-sheet microscopy, used with viral tracers to map long-range connectivity between brain regions. We will also add two state-of-the-art imaging technologies: light-sheet microscopy of cleared whole mouse brains to enable brainwide imaging of neural recording sites and immediate-early gene expression, and serial-section transmission electron microscopy (TEM) to provide rapid automated ultrastructural analysis. The first aim will be to support brainwide imaging of neural recording sites, activation patterns, and connectivity. We will support the automated image processing and brain-registration pipeline that we developed and extend it to new research aims. Our pipeline will identify recording sites registered to a standardized atlas, and how brain regions are activated throughout learning. We will provide transcriptional profiling of our imaged tissue for post-hoc identification of cell types in our imaging datasets. The second aim will be to optimize electron-microscopy sample preparation and serial sectioning. Datasets from the TEM system will be processed by our petascale image-analysis pipeline to search for sequential connectivity underlying sequential neural activity. In the long term, petascale connectomics may be applied to other projects in the U19 to investigate circuit mechanisms of cognition in various brain areas. Our TEM system has the highest raw throughput capacity of its kind in the world, but further work is needed to realize its full potential. This core will optimize sample preparation and serial sectioning for TEM, and complete software required to fully automate TEM imaging. We will optimize EM staining protocols for uniform, high contrast in cubic millimeters of tissue, using our new X-ray-assisted technique. We will also optimize our automated tape-collecting ultramicrotome system, to scale up serial sectioning from 4,000 ultrathin sections to tens of thousands of sections. The third aim will be to automate high-throughput, parallel TEM imaging. When complete, our high-throughput technology will enable imaging of cubic-millimeter datasets in a few weeks instead of the current 6-12 months. New functionalities will extend the duty cycle from the current eight hours with human monitoring to 24 hours automatically, enabling each TEM to produce over 20 TB of data in 24 hours. Software that can handle data throughput at this scale will be built to realize the full potential of our imaging pipeline. More broadly, we expect that our pioneering methods for automating light-sheet and e... ## Key facts - **NIH application ID:** 10900699 - **Project number:** 5U19NS132720-02 - **Recipient organization:** PRINCETON UNIVERSITY - **Principal Investigator:** Samuel Sheng-Hung Wang - **Activity code:** U19 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $457,594 - **Award type:** 5 - **Project period:** 2023-08-08 → 2028-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10900699 ## Citation > US National Institutes of Health, RePORTER application 10900699, C4: Neuroanatomy (5U19NS132720-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10900699. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*