High-throughput and machine learning optimization of fluorescent sensors for multiplexed imaging in vivo

The National Institute of Mental Health awarded the University of Washington a cooperative agreement totaling $2,305,031 on March 9, 2026, to engineer red fluorescent calcium and neuromodulator sensors for multiplexed neuronal imaging. This work is funded under the Mental Health Research Grants program (CFDA 93.242).

The project leverages the Opto-Mass sensor screening platform and machine learning models to accelerate fluorescent sensor development, addressing limitations in dynamic range, photostability, and kinetics that restrict comprehensive exploration of neuronal interactions in multiplexed dual-imaging scenarios. The recipient will conduct high-throughput screening capable of evaluating over ten thousand sensor variants within an hour, coupled with machine learning models that identify key residues affecting sensor performance, then benchmark resulting sensors against best-in-class properties and validate them in vivo across multiple laboratories using fiber photometry and two and three-photon imaging.

Period of performance runs from March 9, 2026, through December 31, 2030, with place of performance in Washington. The University of Washington Office of Sponsored Programs administers the award.

Three sub-recipients share the work. Northwestern University (Sponsored Research Division), awarded June 3, 2026, participates in the high-throughput optimization and machine learning aspects of fluorescent sensor development. The University of Maryland, Baltimore, awarded June 30, 2026, contributes to sensor optimization and validation. Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, awarded May 19, 2026, supports in vivo validation across multiple imaging modalities and application scenarios.

Abstract

Monitoring neuronal activity modulation is pivotal for elucidating brain functionality and addressing neurological disorders. Despite the advancements brought by green fluorescent calcium indicators like gcamp and neuromodulator sensors, a considerable GAP persists in the development of red fluorescent sensors that match the properties of their green counterparts. This GAP, characterized by limitations in dynamic range, photostability, and kinetics, restricts a more comprehensive exploration of neuronal interactions, especially in multiplexed, dual-imaging imaging scenarios. Additionally, the iterative engineering approach for new sensor development is notoriously slow and labor-intensive. Our central goal is to leverage our sensor screening platform, opto-mass, as well as our recent successes in using machine learning to expedite the optimization of fluorescent sensors. This project aims to engineer red fluorescent calcium and neuromodulator sensors that match the kinetics and dynamic range of green sensors and further enhance their properties. Our objectives include the rigorous benchmarking of these sensors against the best-in-class for properties such as dynamic range, kinetics, and photostability, followed by comprehensive in vivo validation across multiple laboratories and application scenarios using fiber photometry and two and 3-photon imaging. Our project is innovative because IT utilizes a high-throughput screening assay capable of evaluating over 10,000 sensor variants from library collections in under an hour, a significant advancement over current methods. Coupled with pioneering machine learning models that identify key residues affecting sensor performance, we will significantly accelerate fluorescent sensor development, particularly for red calcium and gpcr-based sensors. Importantly, we aim to achieve these goals while reducing time and resource commitments. Our project directly addresses critical needs outlined in this FOA, including a broader range of reliable sensors in neuroscience research that facilitate nuanced, multidimensional studies of brain activity. By developing sensors with improved dynamic ranges, kinetics, and photostability, we aim to overcome existing barriers to multiplexed imaging of neuronal dynamics in vivo. Ultimately, the successful completion of this project would not only fill a vital GAP in neuroscientific research tools but also align with the NIH brain initiative's objectives to advance neurotechnology and set new standards for molecular tool development and in vivo validation in neuroscience.