Fluorescence lifetime predicts performance of voltage sensitive fluorophores in cardiomyocytes and neurons

Literature Information

Publication Date 2020-12-11
DOI 10.1039/D0CB00152J
Impact Factor 0
Authors

Steven C. Boggess, Julia R. Lazzari-Dean, Benjamin K. Raliski, Dong Min Mun, Amy Y. Li, Joshua L. Turnbull


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Abstract

Voltage imaging with fluorescent indicators offers a powerful complement to traditional electrode or Ca2+-imaging approaches for monitoring electrical activity. Small molecule fluorescent indicators present the unique opportunity for exquisite control over molecular structure, enabling detailed investigations of structure/function relationships. In this paper, we tune the conjugation between aniline donors and aromatic π systems within the context of photoinduced electron transfer (PeT) based voltage indicators. We describe the design and synthesis of four new voltage-sensitive fluorophores (VoltageFluors, or VFs). Three of these dyes have higher relative voltage sensitivities (ΔF/F) than the previously-reported indicator, VF2.1.Cl. We pair these new indicators with existing VFs to construct a library of voltage indicators with varying degrees of conjugation between the aniline nitrogen lone pair and the aromatic π system. Using a combination of steady-state and time-resolved fluorescence spectroscopy, cellular electrophysiology, fluorescence lifetime imaging microscopy (FLIM), and functional imaging in mammalian neurons and human cardiomyocytes, we establish a detailed link between the photophysical properties of VF dyes and their ability to report on membrane potential dynamics with high signal-to-noise. Anilines with intermediate degrees of conjugation to the aromatic π system experience intermediate rates of PeT and possess the highest absolute voltage sensitivities. Measured using FLIM in patch-clamped HEK cells, we find that the absolute voltage sensitivity of fluorescence lifetime (Δτfl per mV), coupled with traditional fluorescence intensity-based metrics like ΔF/F and signal-to-noise ratio (SNR), provides a powerful method to both predict and understand indicator performance in cellular systems.

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