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ORIGINAL RESEARCH article

Front. Neuroanat.
Volume 18 - 2024 | doi: 10.3389/fnana.2024.1463632

Cell density quantification of high resolution Nissl images of the juvenile rat brain

Provisionally accepted
Julie Meystre Julie Meystre 1*Jean Jacquemier Jean Jacquemier 2Olivier Burri Olivier Burri 3Csaba Zsolnai Csaba Zsolnai 2Nicolas Frank Nicolas Frank 2Joâo Prado Vieira Joâo Prado Vieira 1Rodrigo Perin Rodrigo Perin 1Daniel Keller Daniel Keller 2Henry Markram Henry Markram 1,2
  • 1 Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Geneva, Switzerland
  • 2 Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Geneva, Switzerland
  • 3 Bioimaging and Optics Core Facility, Ecole polytechnique fédérale de Lausanne, Lausanne, Vaud, Switzerland

The final, formatted version of the article will be published soon.

    Nissl histology underpins our understanding of brain anatomy and architecture.We have acquired over 2000 high-resolution images (0.346 µm per pixel) from eight juvenile rat brains stained with cresyl violet, representing the highest resolution dataset ever publicly released and the only available dataset for 14-day-old rats in the current literature.To demonstrate the utility of this dataset, we developed a semi-automated pipeline using open-source software to perform cell density quantification in the primary somatosensory hindlimb (S1HL) cortical column. Traditionally, this is a time-consuming and subjective process. In addition, we performed cortical layer annotations both manually and using a machine learning model to expand the number of annotated samples. After training the model, we applied it to 262 images of the S1HL, retroactively assigning segmented cells to specific cortical layers, enabling cell density quantification per layer rather than just for entire brain regions. This pipeline enhances the efficiency and reliability of cell density quantification while accurately assigning cortical layer boundaries. Furthermore, the method 1 Meystre et al.Cell density quantification of high resolution Nissl images of the juvenile rat brain is adaptable to different brain regions and cell morphologies. The full dataset, annotations, and analysis tools are made publicly available for further research and applications.

    Keywords: rodent, somatosensory hind-limb, cell density, Stereology, cortical layering, Cellpose, machine learning

    Received: 12 Jul 2024; Accepted: 13 Nov 2024.

    Copyright: © 2024 Meystre, Jacquemier, Burri, Zsolnai, Frank, Prado Vieira, Perin, Keller and Markram. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Julie Meystre, Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Geneva, Switzerland

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.