3D confocal imaging reveals quantitative bias in cerebral microhemorrhage counts from standard histology

Standard histological quantification of cerebral microhemorrhages (CMH), the pathologic substrate of cerebral microbleeds, may systematically undercount lesion burden due to sparse tissue sampling. CMH are visualized using Prussian blue-stained 2D tissue sections. This method provides a snapshot of CMH lesions within a brain but does not capture comprehensive quantification across the entire brain. Tissue clearing combined with confocal microscopy can visualize intact CMH lesions in large tissue samples without tissue sectioning. We collected three-dimensional confocal images of optically-cleared, 1-mm-thick brain sections. We compared CMH counts from comprehensive three-dimensional imaging to those obtained from simulated standard two-dimensional histological sectioning. Sparse sampling (one 40-μm-thick section out of every six) resulted in the omission of 38% of individual CMH, disproportionately affecting smaller lesions. Increasing sectioning frequency to one in every 2–3 sections lowered the miss rate to 13%–20% but concurrently increased the likelihood of counting single lesions multiple times. These results demonstrate that tissue sampling frequency substantially affects CMH quantification. Our data suggest using 40-μm sections at a 1-every-4 frequency to provide a balance between omission of CMH, repeat counting of CMH, and workload. This recommendation may change for different study designs and/or experimental models.

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Publication Details

Journal
Frontiers in Neuroscience
Published
2026-10-27
DOI
https://doi.org/10.3389/fnins.2026.1940336
Primary Topic
Intracerebral and Subarachnoid Hemorrhage Research
Type
article
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article

3D confocal imaging reveals quantitative bias in cerebral microhemorrhage counts from standard histology

Mark Fisher, David H. Cribbs, Christian Crouzet, Danny F. Xie et al.
Frontiers in Neuroscience
Intracerebral and Subarachnoid Hemorrhage Research
article

3D confocal imaging reveals quantitative bias in cerebral microhemorrhage counts from standard histology

Mark Fisher, David H. Cribbs, Christian Crouzet, Danny F. Xie, Bernard Choi, Wei Ling Lau
article en

Abstract

Standard histological quantification of cerebral microhemorrhages (CMH), the pathologic substrate of cerebral microbleeds, may systematically undercount lesion burden due to sparse tissue sampling. CMH are visualized using Prussian blue-stained 2D tissue sections. This method provides a snapshot of CMH lesions within a brain but does not capture comprehensive quantification across the entire brain. Tissue clearing combined with confocal microscopy can visualize intact CMH lesions in large tissue samples without tissue sectioning. We collected three-dimensional confocal images of optically-cleared, 1-mm-thick brain sections. We compared CMH counts from comprehensive three-dimensional imaging to those obtained from simulated standard two-dimensional histological sectioning. Sparse sampling (one 40-μm-thick section out of every six) resulted in the omission of 38% of individual CMH, disproportionately affecting smaller lesions. Increasing sectioning frequency to one in every 2–3 sections lowered the miss rate to 13%–20% but concurrently increased the likelihood of counting single lesions multiple times. These results demonstrate that tissue sampling frequency substantially affects CMH quantification. Our data suggest using 40-μm sections at a 1-every-4 frequency to provide a balance between omission of CMH, repeat counting of CMH, and workload. This recommendation may change for different study designs and/or experimental models.

Frontiers in NeuroscienceVol. 20
University of California, Irvine (US), Beckman Laser Institute and Medical Clinic (US)
Good health and well-being
Openalex Percentile: Top 14%
Intracerebral and Subarachnoid Hemorrhage Research
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3D confocal imaging reveals quantitative bias in cerebral microhemorrhage counts from standard histology — Mark Fisher, David H. Cribbs, et al. · Frontiers in Neuroscience (2026) | TGRS Research Map | TGRS