Machine learning-assisted fluoroscopy of bladder function in awake mice

  1. Helene De Bruyn
  2. Nikky Corthout
  3. Sebastian Munck
  4. Wouter Everaerts
  5. Thomas Voets  Is a corresponding author
  1. VIB-KU Leuven Center for Brain & Disease Research, Belgium
  2. KU Leuven, Belgium

Abstract

Understanding the lower urinary tract (LUT) and development of highly needed novel therapies to treat LUT disorders depends on accurate techniques to monitor LUT (dys)function in preclinical models. We recently developed videocystometry in rodents, which combines intravesical pressure measurements with X-ray-based fluoroscopy of the LUT, allowing the in vivo analysis of the process of urine storage and voiding with unprecedented detail. Videocystometry relies on the precise contrast-based determination of the bladder volume at high temporal resolution, which can readily be achieved in anesthetized or otherwise motion-restricted mice but not in awake and freely moving animals. To overcome this limitation, we developed a machine-learning method, in which we trained a neural network to automatically detect the bladder in fluoroscopic images, allowing the automatic analysis of bladder filling and voiding cycles based on large sets of time-lapse fluoroscopic images (>3 hours at 30 images/second) from behaving mice and in a non-invasive manner. With this approach, we found that urethane, an injectable anesthetic that is commonly used in preclinical urological research, has a profound, dose-dependent effect on urethral relaxation and voiding duration. Moreover, both in awake and in anaesthetized mice, the bladder capacity was decreased ~4-fold when cystometry was performed acutely after surgical implantation of a suprapubic catheter. Our findings provide a paradigm for the non-invasive, in vivo monitoring of a hollow organ in behaving animals and pinpoint important limitations of the current gold standard techniques to study the LUT in mice.

Data availability

Raw data for Figures 1-4 are available via https://doi.org/10.6084/m9.figshare.19826050.v1.

The following data sets were generated

Article and author information

Author details

  1. Helene De Bruyn

    Laboratory of Ion Channel Research, VIB-KU Leuven Center for Brain & Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  2. Nikky Corthout

    VIB BioImaging Core, VIB-KU Leuven Center for Brain & Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  3. Sebastian Munck

    VIB BioImaging Core, VIB-KU Leuven Center for Brain & Disease Research, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  4. Wouter Everaerts

    Department of Development and Regeneration, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3157-7115
  5. Thomas Voets

    Laboratory of Ion Channel Research, VIB-KU Leuven Center for Brain & Disease Research, Leuven, Belgium
    For correspondence
    thomas.voets@kuleuven.vib.be
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5526-5821

Funding

Fonds Wetenschappelijk Onderzoek (I001322N)

  • Sebastian Munck

Fonds Wetenschappelijk Onderzoek (G0B7620N)

  • Thomas Voets

Fonds Wetenschappelijk Onderzoek (I000321N)

  • Sebastian Munck

KU Leuven (KA/20/085)

  • Sebastian Munck

KU Leuven (IDN/19/039)

  • Sebastian Munck

Fonds Wetenschappelijk Onderzoek (Senior Clinical Investigator fellowship)

  • Wouter Everaerts

Fonds Wetenschappelijk Onderzoek (G066322N)

  • Wouter Everaerts

KU Leuven (C24M/21/028)

  • Wouter Everaerts

Queen Elisabeth Medical Foundation

  • Thomas Voets

Vlaams Instituut voor Biotechnologie (Unrestricted grant)

  • Thomas Voets

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Anne M.J. Verstegen, BIDMC, Harvard Medical School, United States

Ethics

Animal experimentation: All animal experiments were carried out after approval of the Ethical Committee Laboratory Animals of the Faculty of Biomedical Sciences of the KU Leuven under project number P035/2018.

Version history

  1. Received: April 9, 2022
  2. Preprint posted: April 13, 2022 (view preprint)
  3. Accepted: September 5, 2022
  4. Accepted Manuscript published: September 6, 2022 (version 1)
  5. Version of Record published: October 11, 2022 (version 2)

Copyright

© 2022, De Bruyn et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Helene De Bruyn
  2. Nikky Corthout
  3. Sebastian Munck
  4. Wouter Everaerts
  5. Thomas Voets
(2022)
Machine learning-assisted fluoroscopy of bladder function in awake mice
eLife 11:e79378.
https://doi.org/10.7554/eLife.79378

Share this article

https://doi.org/10.7554/eLife.79378

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