During the reporting period, the project was implemented through a coordinated set of experimental, methodological, and analytical activities aimed at developing and validating multimodal imaging strategies for the brain. The work focused on three interconnected directions: (i) development of genetically encoded tools, (ii) chemical and hybrid probe design, and (iii) quantitative characterization of imaging constraints in brain tissue.
*Development of genetically encoded tools for spatially resolved signaling- A major effort was devoted to establishing genetically encoded systems capable of generating localized signals within defined subcellular compartments. This resulted in the development of split genetically encoded calcium indicators targeted to interorganellar junctions (PNAS, 2025), which demonstrated that functional signals can be restricted to nanoscale cellular domains.This work provides a key conceptual advance by showing that spatial confinement of signal generation can compensate for limitations in detection sensitivity, an important principle for multimodal imaging approaches. In parallel, a novel stop-codon–mediated polycistronic translation strategy (under review, 2026) was developed, enabling coordinated expression of multiple proteins from a single transcript in neurons in vivo. This platform establishes a foundation for implementing complex, multi-component imaging systems with precise stoichiometric control.
*Design and validation of multimodal chemical–genetic probes. The project advanced the development of hybrid imaging probes combining genetic targeting with chemical functionality. In particular, SNAP-tag-targeted MRI–fluorescent probes (ChemBioChem, 2023) demonstrated the feasibility of directing synthetic contrast agents to defined cellular targets using genetically encoded tags. This work establishes a modular strategy for linking molecular specificity with externally delivered probes, enabling flexible design of multimodal imaging systems.
*Quantitative characterization of imaging constraints in brain tissue. A central component of the project involved defining the physical and biological constraints governing imaging signal generation. The study “Revealing the MRI Contrast in Optically Cleared Brains” (Advanced Science, 2024) provided a systematic analysis of how tissue composition, probe distribution, and imaging parameters influence detectable contrast. This work yielded important insights into the limits of sensitivity and spatial resolution, informing the design of more realistic and effective imaging strategies. Complementary to this, the dataset on lipid content in optically cleared brains (Data in Brief, 2023) provides a quantitative resource for understanding how tissue composition affects imaging performance, supporting both experimental interpretation and computational modeling.
*Integration of findings and methodological advances. Across these activities, the project has established: Validated strategies for subcellularly confined signal generation, reducing reliance on bulk accumulation of probes; Modular chemical–genetic platforms for targeted probe delivery; Quantitative frameworks for understanding imaging constraints in brain tissue; New genetic tools enabling multiplexed and coordinated expression in neurons. Together, these advances significantly refine the conceptual and technical basis for multimodal brain imaging.
**Dissemination and exploitation of results. The results of the project have been actively disseminated through peer-reviewed publications in high-impact journals, including PNAS, Advanced Science, ChemBioChem, and Data in Brief, as well as through ongoing work currently under review. The tools and datasets generated in this project provide a foundation for future development of imaging technologies and molecular delivery systems, with potential applications in neuroscience research, drug delivery, and precision medicine. Briefly, genetically encoded systems developed here can be directly adopted by the neuroscience community. Multimodal probe strategies offer a platform for further technological innovation. Quantitative datasets support broader modeling and methodological development
Overall, the work performed during this period has successfully established key technological and conceptual building blocks, while also clarifying critical constraints that must be addressed in future developments. These findings provide a solid and realistic foundation for advancing toward robust, biologically compatible multimodal imaging approaches.
