A Scalable Cross-Modal Workflow for Linking In Vivo, Neural Activity to Neuronal Cell Types via Spatial Gene Profiling

A central challenge in systems neuroscience research is to elucidate how distinct neuronal cell types coordinate to produce complex behaviors: an endeavor that requires linking their molecular identity, connectivity and activity patterns in behaving animals. Recent advances in single-cell RNA sequencing and spatial transcriptomics have revealed remarkable molecular diversity among neurons. However, most functional recording techniques, including in vivo two-photon calcium imaging, do not reveal the molecular cell identities of recorded cells. Although manual one-to-one matching between functional imaging and post hoc molecular profiling has been achieved for dozens to a few hundred neurons, these approaches are typically labor-intensive, difficult to scale, and capture only a limited fraction of cell types. Integrating these two modalities at large scale remains challenging, limiting our ability to fully understand the general logic of neural computation. Here we present a cross-modal workflow that integrates in vivo two-photon calcium imaging in behaving mice with Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH), a thick-tissue spatial transcriptomic approach. As a proof of concept, we apply this workflow to the mouse dorsal hippocampus, a brain region with high neuronal density and small cell size where manual cell matching is impractical, making it a stringent test of the method’s accuracy and scalability. Using our approach, we achieved highly accurate alignment between in vivo neural activity and ex vivo molecular cell-type identity across hundreds to thousands of neurons, enabling direct mapping of neural dynamics to molecularly defined neuronal populations. We showed that longitudinal neural recordings followed by molecular mapping reveal distinct neural activity patterns that emerge as animals learn a spatial navigation task. By linking activity patterns to molecularly defined cell types, this method provides a framework for testing whether neural representations are organized through cell-type-specific coding or distributed population activity, offering insight into the computational logic by which neural circuits generate complex behaviors.MOTIVATION The mammalian brain contains an immense diversity of neurons exhibiting substantial morphological, functional, and molecular diversity. Understanding how this molecular heterogeneity supports neural computation and shapes behavior remains a central challenge in systems neuroscience. Yet, most approaches for recording neural activity provide limited access to molecular identity, making it difficult to directly relate functional dynamics to cell-type-specific features across many neurons in the same tissue. Here, we present a cross-modal workflow that recovers, aligns, and matches in vivo neural activity with targeted molecular profiling, enabling functional readouts and RNA profiling to be coupled at the individual cell level. This method provides a powerful platform for uncovering how molecularly defined neurons contribute to complex activity patterns and, ultimately, animal behavior.