A rapid workflow for neuron counting in combined light sheet microscopy and magnetic resonance histology
Open accessAbstract
Information on regional variation in cell numbers and densities in the CNS provides critical insight into structure, function, and the progression of CNS diseases. However, variability can be real or a consequence of methods that do not account for technical biases, including morphologic deformations, errors in the application of cell type labels and boundaries of regions, errors of counting rules and sampling sites. We address these issues in a mouse model by introducing a workflow that consists of the following steps: 1. Magnetic resonance histology (MRH) to establish the size, shape, and regional morphology of the mouse brain in situ. 2. Light-sheet microscopy (LSM) to selectively label neurons or other cells in the entire brain without sectioning artifacts. 3. Register LSM volumes to MRH volumes to correct for dissection errors and both global and regional deformations. 4. Implement stereological protocols for automated sampling and counting of cells in 3D LSM volumes. This workflow can analyze the cell densities of one brain region in less than 1 min and is highly replicable in cortical and subcortical gray matter regions and structures throughout the brain. This method demonstrates the advantage of not requiring an extensive amount of training data, achieving a F1 score of approximately 0.9 with just 20 training nuclei. We report deformation-corrected neuron (NeuN) counts and neuronal density in 13 representative regions in 5 C57BL/6J cases and 2 BXD strains. The data represent the variability among specimens for the same brain region and across regions within the specimen. Neuronal densities estimated with our workflow are within the range of values in previous classical stereological studies. We demonstrate the application of our workflow to a mouse model of aging. This workflow improves the accuracy of neuron counting and the assessment of neuronal density on a region-by-region basis, with broad applications for studies of how genetics, environment, and development across the lifespan impact cell numbers in the CNS.
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Densities of aβ plaques and NeuN+ cells can be estimated in anatomically corrected stereotaxic space using a digital version of direct 3-dimensional counting (optical disector).
“We can estimate densities of aβ plaques and NeuN+ cells in anatomically corrected stereotaxic space using a digital version of direct 3-dimensional counting, now often referred to as the optical disector23,99.”
From Magnetic resonance microscopy maps widespread effects of Alzheimer’s disease on brain structures and behavior in mice (2026) · cites this paper as [23]
The Abstract (page not specified) describes deformation-corrected NeuN densities and automated sampling and counting in 3D volumes; the Discussion says densities are calculated in morphology-corrected space. The paper does not report aβ-plaque densities or identify its digital workflow as an optical disector, so those parts of the attributed claim are unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe numerical analyses are relatively robust, and in both cases and controls the counts align reasonably well with well validated estimates of neuron numbers.
“Nonetheless, the numerical analyses are relatively robust, and in both cases and controls our counts align reasonably well with well validated estimates of neuron numbers23.”
From Magnetic resonance microscopy maps widespread effects of Alzheimer’s disease on brain structures and behavior in mice (2026) · cites this paper as [23]
The Abstract (page unavailable) says the paper’s mouse neuronal-density estimates fall within the range of previous stereological studies. The Discussion (page unavailable) says most cortical-region densities were consistent with earlier reports, but identifies several regions with higher estimates. The paper does not establish that the citing authors’ counts align with validated neuron-number estimates in both cases and controls; its comparison is primarily of density in mice. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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The Duke Mouse Brain Atlas: MRI and light sheet microscopy stereotaxic atlas of the mouse brain
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Quantitative Neurohistology and Its Applications
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- Authors
- Tian, Y., Johnson, G. A., Williams, R. W., & White, L. E.
- Affiliations
- Duke University (US)
- University of Tennessee Health Science Center (US)
- Corresponding author
- White, Leonard E.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2023-09-27
- Volume
- 17
- Pages
- 1223226-1223226
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC10569694
- OpenAlex
- W4387139390
- Cited by
- 9
- References
- 49
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 2 · 2023: 1
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Keywords
- neuron counting, neuronal density, light-sheet microscopy, stereology, brain registration, deformation correction, NeuN, mouse brain, aging
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