High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo
Author(s) -
Michael E. Xie,
Yoav Adam,
Linlin Z. Fan,
Urs L. Böhm,
Ian Kinsella,
Ding Zhou,
Márton Rózsa,
Amrita Singh,
Karel Svoboda,
Liam Paninski,
Adam E. Cohen
Publication year - 2021
Publication title -
cell reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.264
H-Index - 154
eISSN - 2639-1856
pISSN - 2211-1247
DOI - 10.1016/j.celrep.2021.108954
Subject(s) - subthreshold conduction , signal (programming language) , in vivo , noise (video) , non negative matrix factorization , preclinical imaging , physics , pipeline (software) , computer science , matrix decomposition , biological system , artificial intelligence , voltage , biology , eigenvalues and eigenvectors , microbiology and biotechnology , transistor , quantum mechanics , image (mathematics) , programming language
The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved a high signal-to-noise ratio in vivo with 1-photon fluorescence imaging. However, these recordings exhibit several sources of noise and signal extraction remains a challenge. We present an improved signal extraction pipeline, spike-guided penalized matrix decomposition-nonnegative matrix factorization (SGPMD-NMF), which resolves supra- and subthreshold voltages in vivo. The method incorporates biophysical and optical constraints. We validate the pipeline with simultaneous patch-clamp and optical recordings from mouse layer 1 in vivo and with simulated and composite datasets with realistic noise. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon1, mouse cortex expressing SomArchon1 or Voltron, and zebrafish spines expressing zArchon1.
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