# Simultaneous high-throughput functional, transcriptomic and connectivity profiling using FUNseq > **NIH NIH RF1** · BAYLOR COLLEGE OF MEDICINE · 2022 · $2,276,111 ## Abstract Project Summary Recent advances in technology driven by the BRAIN Initiative have yielded new methods for characterizing the ac- tivity, transcriptome, and microscale anatomy of neurons throughout the brain, and have increased the throughput of these techniques by orders of magnitude. It is currently possible to record the simultaneous activity of popu- lations of neurons on the order of tens of thousands of neurons in awake behaving animals in vivo using large field of view multiphoton imaging or high density silicon probes, and new machine learning methods are enabling more comprehensive functional characterization than ever before. Transcriptomic profiling can be accomplished at scales of tens or hundreds of thousands of neurons in vitro. Finally the microscale anatomy of axonal pro- jections and connections across the brain can also be assessed in tens of thousands of neurons in the same animal using dense electron microscopy reconstruction for local circuits, or (as we propose here) RNA barcoding methods for local and long-range axonal projections. Each of these techniques on their own can provide im- portant clues about the diversity of neurons and their organization into canonical circuits with specific functional, transcriptomic, or axonal projection profiles. While these techniques are all being pushed forward independently, they remain effectively siloed from each other, precluding multi-modal characterization of the same neurons in the same animal. Developing a comprehensive pipeline to characterize transcriptomic, axonal projections and in vivo functional fingerprints as we propose to do here would enable synergistic analyses of cell-type composition across these multiple dimensions. Finally, because of the low cost and high throughput of this approach, experi- ments using this novel pipeline could be repeated many times in different animals to answer pressing questions about how the relationship between the function, structure, and transcriptome of neurons changes across devel- opmental or disease states. In this proposal, we will leverage our team's combined expertise in in vivo functional imaging and Machine Learning to characterize the complex functional properties of neurons in primary visual cortex of the mouse, and novel sequencing techniques developed by PI Zador to combine transcriptomic profiling with RNA barcoding to measure single-neuron projection patterns throughout the brain at axonal resolution. ## Key facts - **NIH application ID:** 10413650 - **Project number:** 1RF1MH126883-01A1 - **Recipient organization:** BAYLOR COLLEGE OF MEDICINE - **Principal Investigator:** Andreas Tolias - **Activity code:** RF1 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $2,276,111 - **Award type:** 1 - **Project period:** 2022-05-01 → 2024-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10413650 ## Citation > US National Institutes of Health, RePORTER application 10413650, Simultaneous high-throughput functional, transcriptomic and connectivity profiling using FUNseq (1RF1MH126883-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10413650. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*