Decoding Cellular Temperature via Neural Network-Aided Fluorescent Thermometry
Tong Zhang, Tian-Tian Li, Jing-Ru Wang, Yu-Wen Zhang, Chao Sun, Zheng Huang, Jing-Juan Xu, Bin Kang

TL;DR
This study introduces a neural network-enhanced fluorescent thermometry method to accurately measure cellular temperatures, revealing that mitochondrial temperature does not exceed 43°C when viscosity effects are accounted for, thus clarifying longstanding debates.
Contribution
The paper presents a novel neural network approach to decouple viscosity effects from fluorescent thermometry, improving accuracy in cellular temperature measurements and resolving conflicting reports on mitochondrial temperatures.
Findings
Cell viscosity significantly affects temperature measurements.
Mitochondrial temperature does not exceed 43°C after viscosity correction.
A temperature gradient exists within cells under stimulation.
Abstract
The temperature distribution within cells, especially the debates on mitochondrial temperature, has recently attracted widespread attention. Some studies have claimed that the temperature of mitochondria can reach up to 50-53 degrees Celsius. Yet others have questioned that this is due to measurement errors from fluorescent thermometry caused by other factors, like cell viscosity. Here we present a neural network-aided fluorescent thermometry and decouple the effect of cellular viscosity on temperature measurements. We found that cellular viscosity may cause significant deviations in temperature measurements. We investigated the dynamic temperature changes in different organelles within the cell under stimulation and observed a distinct temperature gradient within the cell. Eliminating the influence of viscosity, the upper limit of mitochondrial temperature does not exceed 42-43 degrees…
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Taxonomy
TopicsInfrared Thermography in Medicine
