A high-throughput multiparameter screen for accelerated development and optimization of soluble genetically encoded fluorescent biosensors

Dorothy Koveal, Paul C. Rosen, Dylan J. Meyer, Carlos Manlio Díaz-García, Yongcheng Wang, Li-Heng Cai, Peter J. Chou, David A. Weitz and Gary Yellen ()
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Dorothy Koveal: Harvard Medical School
Paul C. Rosen: Harvard Medical School
Dylan J. Meyer: Harvard Medical School
Carlos Manlio Díaz-García: Harvard Medical School
Yongcheng Wang: Harvard University
Li-Heng Cai: Harvard University
Peter J. Chou: Harvard Medical School
David A. Weitz: Harvard University
Gary Yellen: Harvard Medical School

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Genetically encoded fluorescent biosensors are powerful tools used to track chemical processes in intact biological systems. However, the development and optimization of biosensors remains a challenging and labor-intensive process, primarily due to technical limitations of methods for screening candidate biosensors. Here we describe a screening modality that combines droplet microfluidics and automated fluorescence imaging to provide an order of magnitude increase in screening throughput. Moreover, unlike current techniques that are limited to screening for a single biosensor feature at a time (e.g. brightness), our method enables evaluation of multiple features (e.g. contrast, affinity, specificity) in parallel. Because biosensor features can covary, this capability is essential for rapid optimization. We use this system to generate a high-performance biosensor for lactate that can be used to quantify intracellular lactate concentrations. This biosensor, named LiLac, constitutes a significant advance in metabolite sensing and demonstrates the power of our screening approach.

Date: 2022
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DOI: 10.1038/s41467-022-30685-x

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