iScienceBiophysicsPeer-reviewed journal article
Calibration of FRET-based biosensors using multiplexed biosensor barcoding
Jhen-Wei WuiD, Ming Wei YangiD, Chao‐Cheng CheniD, Gabriel AuiD, Su-Yang WangiD, Yichu Xu, Gia-Wei CherniD, Chuan‐Hsiang HuangiD
Abstract
Forster resonance energy transfer (FRET) between fluorescent proteins (FPs) is widely used in the design of genetically encoded fluorescent biosensors, which are powerful tools for monitoring the dynamics of biochemical activities in live cells. FRET ratio, defined as the ratio between acceptor and donor signals, is often used as a proxy for the actual FRET efficiency, which must be corrected for signal crosstalk using donor-only and acceptor-only samples. However, the FRET ratio is highly sensitive to imaging conditions, making direct comparisons across different experiments and over time challenging. Inspired by a method for multiplexed biosensor imaging using barcoded cells, we reasoned that calibration standards with fixed FRET efficiency can be introduced into a subset of cells for normalization of biosensor signals. Our theoretical analysis indicated that the FRET ratio of high-FRET species relative to non-FRET species slightly decreases at high excitation intensity, suggesting the need for calibration using both high and low FRET standards. To test these predictions, we created FRET donor-acceptor pairs locked in "FRET-ON" and "FRET-OFF" conformations and introduced them into a subset of barcoded cells. Our results confirmed the theoretical predictions and showed that the calibrated FRET ratio is independent of imaging settings. We also provided a strategy for calculating the FRET efficiency. Together, our study presents a simple strategy for calibrated and highly multiplexed imaging of FRET biosensors, facilitating reliable comparisons across experiments and supporting long-term imaging applications.
- assay: fluorescence / live-cell microscopy
- research focus: cell type annotation
- approach: experimental assay
- Citations
- 3
- FWCI
- 0.97
- Field percentile
- top 26%
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