A high-throughput multiparameter screen for accelerated development and optimization of soluble genetically encoded fluorescent biosensors.
A high-throughput multiparameter screen for accelerated development and optimization of soluble genetically encoded fluorescent biosensors.
复制标题
高通量多参数筛选,用于加速可溶性遗传编码荧光生物传感器的开发和优化。
DOI:
10.1038/s41467-022-30685-x
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发表时间:
2022-05-25
影响因子:
16.6
通讯作者:
中科院分区:
文献类型:
--
作者:
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. Fluorescent biosensors are important tools for studying cellular metabolism, but development and optimization are challenging. Koveal et al. present a high-throughput multiparameter screen for sensor performance, and used it to generate LiLac, a high-performance, quantitative lactate sensor.
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影响因子:
4.6
作者:
Harada K;Chihara T;Hayasaka Y;Mita M;Takizawa M;Ishida K;Arai M;Tsuno S;Matsumoto M;Ishihara T;Ueda H;Kitaguchi T;Tsuboi T
通讯作者:
Tsuboi T
影响因子:
16.6
作者:
通讯作者:
--
影响因子:
29
作者:
Díaz-García CM;Mongeon R;Lahmann C;Koveal D;Zucker H;Yellen G
通讯作者:
Yellen G
影响因子:
7.4
作者:
Galaz, Alex;Cortes-Molina, Francisca;San Martin, Alejandro
通讯作者:
San Martin, Alejandro
影响因子:
48
作者:
Cameron, William D.;Bui, Cindy V.;Rocheleau, Jonathan V.
通讯作者:
Rocheleau, Jonathan V.