Li-En Lin, Adrian Colazo, Xiaotian Bi, Jiajun Du, Lu Wei
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引用次数: 0
Abstract
Comprehensive visualization of tissue architecture in large organs such as the brain is crucial for understanding functional relationships across key tissue regions. However, the large size of whole organs makes it challenging to image their entirety with subcellular resolution, often requiring prolonged imaging sessions, volume reconstruction, and compromises in spatial coverage. Here, Scalable Hydrogel-embedded Rapid Imaging of tissue NetworK (SHRINK) is reported to address this challenge through active tissue shrinkage and clearing. Utilizing the identified hydrogel network to preserve the spatial pattern of proteins in situ and remove the uncrosslinked biomolecules to create space, it is shown that SHRINK isotropically drives the reduction of sample sizes down to 16% of their original volume while maintaining high cellular and tissue-level integrity in a reversible manner. The size reduction and the corresponding 3D concentrating of the biomolecules render a more than sixfold enhancement for throughput and signal respectively, which addresses a key bottleneck for the stimulated Raman scattering (SRS) microscopy, ideal for 3D, label-free and super-multiplex tissue mapping. It is further demonstrated that SHRINK-SRS achieves organ-scale mapping of brain, intestine, heart, and kidney tissues. SHRINK offers a powerful approach to overcome traditional imaging barriers, enabling rapid and detailed visualization of large organs.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.