High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo
About This Grant
PROJECT SUMMARY / 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.
Grant Summary
High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo is a NIMH - National Institute of Mental Health grant providing up to $2.3M for university, nonprofit, healthcare org. Applications are due 2030-12-31 (open). Check eligibility and apply with FindGrants.
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How to Apply
Up to $2.3M
2030-12-31
- 1Confirm your organization is eligible for High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo from NIMH - National Institute of Mental Health, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIMH - National Institute of Mental Health before the deadline.
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High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo: Frequently Asked Questions
Who is eligible for the High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo?
High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo is offered by NIMH - National Institute of Mental Health and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo provide?
High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo provides up to $2.3M per award from NIMH - National Institute of Mental Health. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo deadline?
Applications for High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo are due 2030-12-31 (open). Because deadlines can change, verify the date with the funder, NIMH - National Institute of Mental Health, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo?
To apply for High-throughput and Machine Learning Optimization of Fluorescent Sensors for Multiplexed Imaging in vivo, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIMH - National Institute of Mental Health.