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

Julie Meystre; Jean Jacquemier; Olivier Burri; Csaba Zsolnai; Nicolas Frank; João Prado Vieira; Ying Shi; Rodrigo Perin; Daniel Keller; Henry Markram

doi:10.3389/fnana.2024.1463632

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

Front Neuroanat. 2024 Dec 18:18:1463632. doi: 10.3389/fnana.2024.1463632. eCollection 2024.

Authors

Julie Meystre^#¹, Jean Jacquemier^#², Olivier Burri³, Csaba Zsolnai², Nicolas Frank², João Prado Vieira¹, Ying Shi^{1

2}, Rodrigo Perin¹, Daniel Keller², Henry Markram^{1

2}

Affiliations

¹ Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
² Blue Brain Project, École Polytechnique Fédérale de Lausanne, Genève, Switzerland.
³ Bioimaging and Optics Platform, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

^# Contributed equally.

Abstract

Nissl histology underpins our understanding of brain anatomy and architecture. Despite its importance, no high-resolution datasets are currently available in the literature for 14-day-old rats. To remedy this issue and demonstrate the utility of such a dataset, we have acquired over 2000 high-resolution images (0.346 μm per pixel) from eight juvenile rat brains stained with cresyl violet. To analyze 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. 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. The pipeline improved the efficiency and reliability of cell density quantification while accurately assigning cortical layer boundaries. Furthermore, the method 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: Cellpose; cell density; cortical layering; machine learning; rodent; somatosensory hindlimb; stereology.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by funding from the EPFL to the Laboratory of Neural Microcircuitry (LNMC), and by funding to the Blue Brain Project, a research center of the École Polytechnique Fédérale de Lausanne (EPFL), from the Swiss Government's ETH Board of the Swiss Federal Institutes of Technology.