4.8 Article

μMap-Red: Proximity Labeling by Red Light Photocatalysis

期刊

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
卷 144, 期 14, 页码 6154-6162

出版社

AMER CHEMICAL SOC
DOI: 10.1021/jacs.2c01384

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资金

  1. NIH National Institute of General Medical Sciences [R35-GM134897-02]
  2. Princeton Catalysis Initiative
  3. NIH [1F32GM142206-01]
  4. National Science Foundation Graduate Research Fellowship Program [DGE-1656466]
  5. Division of Chemical Sciences, Geosciences, and Biosciences, Office of Basic Energy Sciences of the U.S. Department of Energy (DOE) [DE-SC0019370]
  6. NIGMS of the National Institutes of Health [T32GM007388]
  7. HMEI-STEP fellowship from the from the High Meadows Environmental Institute at Princeton University
  8. Cancer Institute of New Jersey Cancer Center Support Grant [P30CA072720]
  9. Confocal Imaging Facility, a Nikon Center of Excellence, in the Department of Molecular Biology at Princeton University
  10. Merck
  11. BMS
  12. Pfizer
  13. Janssen
  14. Genentech
  15. Eli Lilly

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This study reports a proximity labeling platform called mu Map-Red, which utilizes a red-light-excited catalyst to activate a biotin probe for labeling biomolecules. The platform has been validated for protein labeling in complex biological environments and animal models, and successfully applied to profile erythrocyte cell-surface proteins in whole mouse blood samples.
Modern proximity labeling techniques have enabled significant advances in understanding biomolecular interactions.However, current tools primarily utilize activation modes that are incompatible with complex biological environments, limiting ourability to interrogate cell- and tissue-level microenvironments in animal models. Here, we report mu Map-Red, a proximity labelingplatform that uses a red-light-excited SnIVchlorin e6 catalyst to activate a phenyl azide biotin probe. We validate mu Map-Red bydemonstrating photonically controlled protein labelingin vitrothrough several layers of tissue, and we then apply our platformincelluloto labelEGFRmicroenvironments and validate performance with STED microscopy and quantitative proteomics. Finally, todemonstrate labeling in a complex biological sample, we deploy mu Map-Red in whole mouse blood to profile erythrocyte cell-surfaceproteins. This work represents a significant methodological advance toward light-based proximity labeling in complex tissueenvironments and animal models.

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