4.8 Article

Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution

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NATURE COMMUNICATIONS
卷 11, 期 1, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-019-13858-z

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资金

  1. National Institutes of Health (NIH) Eunice Kennedy Shriver National Institute of Child Health and Human Development [R21 HD084788]
  2. NIH Common Fund, through the Office of Strategic Coordination/Office of the NIH Director [UG3HL145593]
  3. Laboratory Directed Research and Development award from Pacific Northwest National Laboratory (PNNL)
  4. PNNL [R33 CA225248]
  5. NIH [R01HD068524, DA006668]
  6. March of Dimes [22-FY17-889]
  7. Department of Energy (DOE) [DE-AC05-76RLO 1830]
  8. Office of Biological and Environmental Research [grid.436923.9]

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Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-mu m spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.

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