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- W4297263723 abstract "Super-resolution microscopy can capture spatiotemporal organizations of protein interactions with resolution down to 10 nm; however, the analyses of more than two proteins involving low-abundance protein are challenging because spectral crosstalk and heterogeneities of individual fluorescent labels result in molecular misidentification. Here we developed a deep learning-based imaging analysis method for spectroscopic single-molecule localization microscopy to minimize molecular misidentification in three-color super-resolution imaging. We characterized the 3-fold reduction of molecular misidentification in the new imaging method using pure samples of different photoswitchable fluorophores and visualized three distinct subcellular proteins in U2-OS cell lines. We further validated the protein counts and interactions of TOMM20, DRP1, and SUMO1 in a well-studied biological process, Staurosporine-induced apoptosis, by comparing the imaging results with Western-blot analyses of different subcellular portions." @default.
- W4297263723 created "2022-09-28" @default.
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- W4297263723 date "2022-09-27" @default.
- W4297263723 modified "2023-10-16" @default.
- W4297263723 title "Minimizing Molecular Misidentification in Imaging Low-Abundance Protein Interactions Using Spectroscopic Single-Molecule Localization Microscopy" @default.
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- W4297263723 doi "https://doi.org/10.1021/acs.analchem.2c02417" @default.
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