# NIH grant: High-Throughput, Automated Mapping of Neuronal Activity to Genetic Identity (1R21NS150995-01) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1R21NS150995-01)

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NIH grant 1R21NS150995-01: High-Throughput, Automated Mapping of Neuronal Activity to Genetic Identity
Institute: {'code': 'NS', 'abbreviation': 'NINDS', 'name': 'National Institute of Neurological Disorders and Stroke', 'admin_org_id': '12500053', 'admin_funding_url': ''}. PI: RUI  CHANG. Award: $462052.
Start: 2026-08-21T00:00:00.
Project Summary
Neural circuits are composed of highly diverse neurons whose gene expression patterns determine their
morphology, connectivity, and physiological properties. Understanding how these transcriptional programs
shape neural activity is a central challenge in neuroscience. Modern in vivo imaging enables precise
measurement of neuronal dynamics within intact networks, revealing how ensembles of neurons encode sensory
information and drive behavior. However, these recordings alone provide no information about the underlying
gene expression programs that define each neuron’s molecular state and functional identity. This disconnect
limits our ability to interpret neural dynamics in molecular terms and to uncover the genetic principles governing
circuit organization and function. Spatial transcriptomics (ST) technologies now allow hundreds to thousands of
genes to be mapped directly in tissue while preserving spatial relationships among cells. When integrated with in
vivo imaging, these datasets could transform neuroscience by directly linking neuronal activity to transcriptomic
identity. Yet, despite rapid advances in both imaging and molecular profiling, there remains no unified
experimental–computational framework that connects these modalities. Current approaches require extensive
manual annotation, rely on limited genetic markers, and depend on specialized transgenic models, preventing
broad adoption across laboratories and species. A practical and automated solution that can align functional
imaging and ST datasets at single-cell resolution would close this gap, providing a powerful new avenue for
studying how gene expression programs drive neural activity. The overall objective of this project is to establish
an automated, scalable framework for linking neuronal activity with genetic identity across both peripheral and
central nervous systems. In our preliminary studies in the mouse nodose ganglion, we developed an innovative
pipeline that achieves high-yield and high-confidence alignment between two-photon calcium imaging and
RNAscope-based ST datasets using a fully automated algorithmic framework. Building on this foundation, we
will adapt our framework to commercial high-plex ST platforms such as Vizgen’s MERSCOPE, enabling
transcriptome-scale integration with in vivo imaging in peripheral neurons. We will then extend the approach to
the brain, using both existing and newly generated cortical datasets to refine experimental procedures and
optimize computational alignment for the increased density and architectural complexity of central tissue.
Successful completion of this project will yield a validated and generalizable framework that unites neuronal
function with transcriptomic identity at single-cell resolution. These studies will generate critical proof-of-concept
data and establish the feasibility of large-scale implementation, providing a foundation for future research
programs aimed at decoding the molecular logic of neural circuits. Ultimately, this work will advance our
understanding of how gene expression programs shape neuronal activity, connectivity, and behavior, and will
make functional–genetic mapping accessible to the broader neuroscience community.
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