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

Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology

Krisztian Koos, Gáspár Oláh, Tamas Balassa, Norbert Mihut, Márton Rózsa, Attila Ozsvár, Ervin Tasnadi, Pál Barzó, Nóra Faragó, László Puskás, Gábor Molnár, József Molnár, Gábor Tamás, Peter Horvath
doi: https://doi.org/10.1101/2020.05.05.078162
Krisztian Koos
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gáspár Oláh
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tamas Balassa
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Norbert Mihut
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Márton Rózsa
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Attila Ozsvár
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ervin Tasnadi
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Pál Barzó
3Department of Neurosurgery, University of Szeged, Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nóra Faragó
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
4Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre, Szeged, Hungary
5Avidin Ltd, Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
László Puskás
4Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre, Szeged, Hungary
5Avidin Ltd, Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gábor Molnár
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
József Molnár
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gábor Tamás
2MTA-SZTE Research Group for Cortical Microcircuits of the Hungarian Academy of Sciences, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Hungary
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter Horvath
1Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
6Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: horvath.peter@brc.hu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. We have developed a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropipette movement, approach to the cell with the pipette, formation of the whole-cell configuration, and recording. The cell detection is based on deep learning. The model was trained on a new image database of neurons in unlabeled brain tissue slices. The pipette tip detection and approaching phase use image analysis techniques for precise movements. High-quality measurements were performed on hundreds of human and rodent neurons. We also demonstrate that further molecular and anatomical analysis can be performed on the recorded cells. The software has a diary module that automatically logs patch clamp events. Our tool can multiply the number of daily measurements to help brain research.

ONE SENTENCE SUMMARY Novel deep learning and image analysis algorithms for automated patch clamp systems to reliably measure neurons in human and rodent brain slices.

Competing Interest Statement

The authors have declared no competing interest.

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-ND 4.0 International license.
Back to top
PreviousNext
Posted May 05, 2020.
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.
Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology
(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
Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology
Krisztian Koos, Gáspár Oláh, Tamas Balassa, Norbert Mihut, Márton Rózsa, Attila Ozsvár, Ervin Tasnadi, Pál Barzó, Nóra Faragó, László Puskás, Gábor Molnár, József Molnár, Gábor Tamás, Peter Horvath
bioRxiv 2020.05.05.078162; doi: https://doi.org/10.1101/2020.05.05.078162
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology
Krisztian Koos, Gáspár Oláh, Tamas Balassa, Norbert Mihut, Márton Rózsa, Attila Ozsvár, Ervin Tasnadi, Pál Barzó, Nóra Faragó, László Puskás, Gábor Molnár, József Molnár, Gábor Tamás, Peter Horvath
bioRxiv 2020.05.05.078162; doi: https://doi.org/10.1101/2020.05.05.078162

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 (2543)
  • Biochemistry (4994)
  • Bioengineering (3497)
  • Bioinformatics (15279)
  • Biophysics (6926)
  • Cancer Biology (5427)
  • Cell Biology (7771)
  • Clinical Trials (138)
  • Developmental Biology (4558)
  • Ecology (7180)
  • Epidemiology (2059)
  • Evolutionary Biology (10261)
  • Genetics (7532)
  • Genomics (9826)
  • Immunology (4899)
  • Microbiology (13304)
  • Molecular Biology (5165)
  • Neuroscience (29569)
  • Paleontology (203)
  • Pathology (842)
  • Pharmacology and Toxicology (1470)
  • Physiology (2153)
  • Plant Biology (4780)
  • Scientific Communication and Education (1015)
  • Synthetic Biology (1343)
  • Systems Biology (4022)
  • Zoology (771)