Cell density quantification of high resolution Nissl images of the juvenile rat brain - Meystre, Julie; Jacquemier, Jean; Burri, Olivier; Zsolnai, Csaba; Frank, Nicolas; Vieira, João Prado; Shi, Ying; Perin, Rodrigo; Keller, Daniel; Markram, Henry (Frontiers in Neuroanatomy, 2024)
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.