Deep-prior ODEs augment fluorescence imaging with chemical sensors DOI Creative Commons
Thanh-an Pham, Aleix Boquet-Pujadas,

Sandip Mondal

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 24, 2024

To study biological signalling, great effort goes into designing sensors whose fluorescence follows the concentration of chemical messengers as closely possible. However, binding kinetics are often overlooked when interpreting cell signals from resulting measurements. We propose a method to reconstruct spatiotemporal underlying in consideration process. Our fits data under constraint corresponding reactions and with help deep-neural-network prior. test it on several GCaMP calcium sensors. The recovered concentrations concur common temporal waveform regardless sensor kinetics, whereas assuming equilibrium introduces artifacts. also show that our can reveal distinct events distribution single neurons. work augments current highlights importance incorporating physical constraints computational imaging.

Language: Английский

Deep-prior ODEs augment fluorescence imaging with chemical sensors DOI Creative Commons
Thanh-an Pham, Aleix Boquet-Pujadas,

Sandip Mondal

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 24, 2024

To study biological signalling, great effort goes into designing sensors whose fluorescence follows the concentration of chemical messengers as closely possible. However, binding kinetics are often overlooked when interpreting cell signals from resulting measurements. We propose a method to reconstruct spatiotemporal underlying in consideration process. Our fits data under constraint corresponding reactions and with help deep-neural-network prior. test it on several GCaMP calcium sensors. The recovered concentrations concur common temporal waveform regardless sensor kinetics, whereas assuming equilibrium introduces artifacts. also show that our can reveal distinct events distribution single neurons. work augments current highlights importance incorporating physical constraints computational imaging.

Language: Английский

Citations

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