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Molecule Differentiation Encoding Microscopy to Dissect Dense Biomolecules in Cellular Nanoenvironments below Spatial Resolution
Author(s) -
Fan Siyue,
Li Xinyin,
Liu Huan,
Ye Mengying,
He Yan,
Fu Wenhao,
Chen Feng,
Zhao Yongxi
Publication year - 2025
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.202425136
Subject(s) - biomolecule , super resolution microscopy , microscopy , chemistry , nanotechnology , resolution (logic) , biophysics , biology , materials science , scanning confocal electron microscopy , physics , computer science , optics , artificial intelligence
Abstract Cellular biomolecules may exhibit dense distribution and organization at the nanoscale to govern vital biological processes. However, it remains a common challenge to digitize the spatially dense biomolecules under the spatial resolution of microscopies. Here, a proof‐of‐principle method, molecule differentiation encoding microscopy by orthogonal tandem repeat DNA identifiers is reported, to resolve the copy numbers of dense biomolecules in cellular nanoenvironments. The method encodes each copy of the same biomolecules into different types of DNA barcodes based on stochastic multiplexed reactions. It can transform the overlap of the same spectrum into the overlap of different spectra. Furthermore, an algorithm is developed to automatically quantitate overlapping spots and individual spots. Using this method, RNAs in the cytoplasm, DNA epigenetic modifications in the cell nucleus, and glycans and glycoRNAs on the cell surface are dissected, respectively. It is found that all these biomolecules present dense distribution with diverse degrees in crowded cellular nanoenvironments. Especially, an average 17% copies of U1 glycoRNA of single cells are gathered in various nano environments on the cell surface. The strategy provides a powerful tool for digitally quantitative visualization of dense biomolecules below the spatial resolution of microscopies and can provide insights into underlying functions and mechanisms of the dense distribution information.

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