ArticleFrontiers in neuroanatomy2024
Cell density quantification of high resolution Nissl images of the juvenile rat brain.
Article in Frontiers in neuroanatomy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Hindlimb Representation in the Spiny Mouse Sensorimotor Cortex.The Journal of comparative neurology · 2026Article
- Plant-derived mitochondria mitigate aging-related neurodegeneration by reprogramming microglial mitochondrial energy metabolism.Translational neurodegeneration · 2026Article
- Effect of Gelatin-Peptide Complex from Sturgeon Skin on Behavioral, Antioxidant, and Neuroprotective Functions in D-Galactose-Induced Aging Mice: Thermal Degradation vs. Enzymatic Hydrolysis.Foods (Basel, Switzerland) · 2026Article
- Downregulation of the deubiquitinating enzyme USP10 correlates with neuronal apoptosis in HTLV-1-associated myelopathy.Scientific reports · 2026Article
- Molecular hydrogen-mediated SIRT1 activation alleviates sepsis-associated encephalopathy by promoting mitophagy.European journal of medical research · 2025Article
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10 authors.
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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.
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