ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Near-Freezing-Temperature Golgi Neuronal Staining for X-ray Imaging of Human Brain.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- The Use of Synchrotron Radiation in the Medical Sciences.Photon science · 2026Article
- High-Resolution Single-Neuron Reconstruction Analysis in Golgi-Stained Brain Tissues.Cell proliferation · 2026Article
- Near-Freezing-Temperature Golgi Neuronal Staining for X-ray Imaging of Human Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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13 authors.
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Abstract
Achieving detailed neuronal structural information in large-volume brain tissue has been a longstanding challenge in human brain imaging. A key obstacle arises from the trade-off between staining efficiency and tissue autolysis. Traditional Golgi staining, typically conducted at room temperature or 37 °C to optimize staining efficiency, leads to rapid autolysis of brain tissue, resulting in the loss of fine structural details. Here, a near-freezing temperature (NFT) staining strategy in post-mortem frozen (PMF) human brain samples are presented, using a mercury chloride-based method under ice-water bath conditions. In contrast to the 37 °C Golgi staining, this NFT-based method significantly reduces tissue autolysis, preserving fine neuronal structures. Notably, neuronal counts in the same field of view increased by 5.5-fold, and dendritic spine density increases by 22-fold. Using this approach, uniform staining of millimeter-thick is achieved, centimeter-scale human brain slices and integrated it with synchrotron-based X-ray microscopy to perform micrometer resolution 3D reconstructions of the cerebellum and frontal lobe. This novel technique offers a powerful tool for the fine-structural imaging of large-volume brain tissue, providing new insights into the intricate organization of neural networks.
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