Meystre Julie, Jacquemier Jean, Burri Olivier, Zsolnai Csaba, Frank Nicolas, Vieira João Prado, Shi Ying, Perin Rodrigo, Keller Daniel, Markram Henry
Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Blue Brain Project, École Polytechnique Fédérale de Lausanne, Genève, Switzerland.
Front Neuroanat. 2024 Dec 18;18:1463632. doi: 10.3389/fnana.2024.1463632. eCollection 2024.
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.
尼氏组织学是我们理解脑解剖结构和构造的基础。尽管其很重要，但目前文献中尚无14日龄大鼠的高分辨率数据集。为解决这一问题并展示此类数据集的效用，我们从八只经甲酚紫染色的幼年大鼠大脑中获取了2000多张高分辨率图像（每像素0.346μm）。为分析该数据集，我们使用开源软件开发了一个半自动流程，以对初级体感后肢（S1HL）皮质柱中的细胞密度进行量化。此外，我们手动并使用机器学习模型进行皮质层注释，以增加注释样本的数量。在训练模型后，我们将其应用于S1HL的262张图像，追溯性地将分割后的细胞分配到特定的皮质层，从而能够对每层的细胞密度进行量化，而不仅仅是对整个脑区进行量化。该流程提高了细胞密度量化的效率和可靠性，同时准确地划分了皮质层边界。此外，该方法适用于不同的脑区和细胞形态。完整的数据集、注释和分析工具已公开提供，以供进一步研究和应用。
