Deep-prior ODEs augment fluorescence imaging with chemical sensors.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
24 Oct 2024
24 Oct 2024
Historique:
received:
21
12
2023
accepted:
07
10
2024
medline:
25
10
2024
pubmed:
25
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
To study biological signalling, great effort goes into designing sensors whose fluorescence follows the concentration of chemical messengers as closely as possible. However, the binding kinetics of the sensors are often overlooked when interpreting cell signals from the resulting fluorescence measurements. We propose a method to reconstruct the spatiotemporal concentration of the underlying chemical messengers in consideration of the binding process. Our method fits fluorescence data under the constraint of the corresponding chemical reactions and with the help of a deep-neural-network prior. We test it on several GCaMP calcium sensors. The recovered concentrations concur in a common temporal waveform regardless of the sensor kinetics, whereas assuming equilibrium introduces artifacts. We also show that our method can reveal distinct spatiotemporal events in the calcium distribution of single neurons. Our work augments current chemical sensors and highlights the importance of incorporating physical constraints in computational imaging.
Identifiants
pubmed: 39448575
doi: 10.1038/s41467-024-53232-2
pii: 10.1038/s41467-024-53232-2
doi:
Substances chimiques
Calcium
SY7Q814VUP
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9172Subventions
Organisme : United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research)
ID : 6950053
Informations de copyright
© 2024. The Author(s).
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