Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Accurate action potential inference from a calcium sensor protein through biophysical modeling

David S Greenberg, Damian J Wallace, Kay-Michael Voit, Silvia Wuertenberger, Uwe Czubayko, Arne Monsees, Takashi Handa, Joshua T Vogelstein, Reinhard Seifert, Yvonne Groemping, Jason ND Kerr
doi: https://doi.org/10.1101/479055
David S Greenberg
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Damian J Wallace
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kay-Michael Voit
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Silvia Wuertenberger
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Uwe Czubayko
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Arne Monsees
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Takashi Handa
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joshua T Vogelstein
2Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Reinhard Seifert
3Molecular Sensory Systems, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yvonne Groemping
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jason ND Kerr
1Department of Brain and Behavior Organization, Research Institute CAESAR, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Multiphoton imaging of genetically encoded calcium indicators is routinely used to report activity from populations of spatially resolved neurons in vivo. However, since the relationship between fluorescence and action potentials (APs) is nonlinear and varies over neurons, quantitatively inferring AP discharge is problematic. To address this we developed a biophysical model of calcium binding kinetics for the indicator GCaMP6s that accurately describes AP-evoked fluorescence changes in vivo. The model’s physical interpretation allowed the same parameters to describe GCaMP6s binding kinetics for both in vitro binding assays and in vivo imaging. Using this model, we developed an algorithm to infer APs from fluorescence and measured its accuracy with cell-attached electrical recordings. This approach consistently inferred more accurate AP counts and times than alternative methods for firing rates from 0 to >20 Hz, while requiring less training data. These results demonstrate the utility of quantitative, biophysically grounded models for complex biological data.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted November 29, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Accurate action potential inference from a calcium sensor protein through biophysical modeling
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Accurate action potential inference from a calcium sensor protein through biophysical modeling
David S Greenberg, Damian J Wallace, Kay-Michael Voit, Silvia Wuertenberger, Uwe Czubayko, Arne Monsees, Takashi Handa, Joshua T Vogelstein, Reinhard Seifert, Yvonne Groemping, Jason ND Kerr
bioRxiv 479055; doi: https://doi.org/10.1101/479055
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Accurate action potential inference from a calcium sensor protein through biophysical modeling
David S Greenberg, Damian J Wallace, Kay-Michael Voit, Silvia Wuertenberger, Uwe Czubayko, Arne Monsees, Takashi Handa, Joshua T Vogelstein, Reinhard Seifert, Yvonne Groemping, Jason ND Kerr
bioRxiv 479055; doi: https://doi.org/10.1101/479055

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3574)
  • Biochemistry (7517)
  • Bioengineering (5479)
  • Bioinformatics (20675)
  • Biophysics (10257)
  • Cancer Biology (7931)
  • Cell Biology (11583)
  • Clinical Trials (138)
  • Developmental Biology (6563)
  • Ecology (10135)
  • Epidemiology (2065)
  • Evolutionary Biology (13537)
  • Genetics (9498)
  • Genomics (12788)
  • Immunology (7871)
  • Microbiology (19451)
  • Molecular Biology (7614)
  • Neuroscience (41873)
  • Paleontology (306)
  • Pathology (1252)
  • Pharmacology and Toxicology (2179)
  • Physiology (3249)
  • Plant Biology (7007)
  • Scientific Communication and Education (1291)
  • Synthetic Biology (1942)
  • Systems Biology (5406)
  • Zoology (1107)