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Our research group develops cutting-edge molecular and imaging technologies to interrogate brain circuit in both health and disease. We focus on bridging scales of analysis—from genetic and molecular processes to neural circuits and whole-organism behaviors—providing tools that advance mechanistic understanding of the nervous system.
Background and Challenges
Our understanding of the brain’s dense network, the spatial distribution, and the interplay of their intracellular compartments, and their molecular architecture have in large part been defined by molecular and imaging technologies. Correlative Light and Electron Microscopy (CLEM) approaches have enabled us to conduct multi-modal studies, by pairing molecular and functional circuit assessments utilizing different molecular tools, such as immunohistochemistry, and Ca2+ imaging, with diffraction-limited fluorescence light microscopy, and subsequent EM-imaging for ultrastructure context. These types of studies rely on correlative measurements and datasets of different resolution (~200 nm with classical fluorescence imaging and ~4 nm with electron microscopy). Furthermore, EM and related technologies are expensive, and inaccessible to the broad scientific community. These shortcomings impede scientific progress as a community effort.
Opportunities with Emerging Technologies
Novel light microscopy-based technologies and tissue engineering such as fluorescence nanoscopy or hydrogel-based tissue expansion offer great opportunities to the broader scientific community for multimodal investigation of brain function and dynamic across scales, providing molecular, cellular, electrophysiological and circuit level information, both in fixed and living tissue. By reducing barriers to access and cost, these approaches promise to democratize high-resolution, large-scale brain mapping.
LICONN: A New Paradigm in Connectomics
To address these challenges, we have developed LICONN (Light Microscopy-based Connectomics), a novel framework for molecular-level connectomics in the mammalian brain (Tavakoli et al., 2025). LICONN combines high-fidelity hydrogel-tissue engineering with click chemistry to comprehensively label the cellular proteome for structural readout and utilizes a widely available diffraction-limited spinning disc confocal microscope (although not limited to this modality) for imaging. A single-step, machine-learning based approach is then applied to automatically reconstruct the brain circuit.
Key Advantages and Applications
LICONN enables rapid data acquisition, quantitative phenotyping, and cross-species genotype–phenotype comparisons. This platform supports high-throughput, comparative analysis in both healthy and disease-relevant contexts (Watson et al., 2024; Tavakoli et al., 2025). By lowering technological and cost barriers, LICONN expands the reach of dense circuit mapping and provides a scalable solution for linking molecular architecture to circuit-level function.
Impact
LICONN-based technologies open new avenues for high-resolution, molecular-level interrogation of how neural circuits are (mis)wired in health and disease. By democratizing dense connectomic mapping, LICONN positions the neuroscience community to accelerate discovery and deepen our understanding of the brain across molecular to systems-level scales.