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Preprint . 2021
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Other literature type . 2022
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Other literature type . 2022
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Relaxation Sensors

Authors: Zumra P Seidel; John C. K. Wang; Johannes Riegler; Andrew York; Maria Ingaramo;

Relaxation Sensors

Abstract

Zenodo hosts the archival version of this document; for convenient viewing, please visit andrewgyork.github.io/relaxation_sensors Abstract Sensors based on fluorescent proteins or molecules are often used in optical imaging to reveal quantities of interest like pH [Miesenböck 1998], calcium concentration [Nakai 2001], or temperature [Kiyonaka 2013]. Sensor readout is typically based on fluorescence intensity (which is not quantitative), color (which is not robust against sample opacity or autofluorescence), or lifetime (which is not robust against sample autofluoresence, or compatible with simple imaging systems). This makes precise in vivo measurement almost impossible, except in extremely transparent creatures. We've developed a new way to read out quantities of interest [Ingaramo 2020], which we call "relaxation sensing". Relaxation sensors are quantitative, robust against sample opacity and autofluorescence, and compatible with simple timelapse imaging systems. To enable genetically-expressed relaxation sensors, we engineered Countdown, a photoswitchable fluorescent protein which rapidly spontaneously equilibrates ("relaxes") to a nonfluorescent state. To demonstrate relaxation sensing, we further engineered Countdown into pH-Countdown, a relaxation sensor with rapid pH-dependent relaxation rates. pH-Countdown quantitatively reports pH in living creatures like yeast, worms, and mice, despite substantial autofluorescence and opacity. We discuss how Countdown can be adapted to report other quantities of interest, and how other fluorescent domains can be used for relaxation sensing.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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