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

Paul D. Piehowski, Ying Zhu, Lisa M. Bramer, Kelly G. Stratton, Rui Zhao, Daniel J. Orton, Ronald J. Moore, Jia Yuan, Hugh D. Mitchell, Yuqian Gao, Bobbie-Jo M. Webb-Robertson, Sudhansu K. Dey, Ryan T. Kelly () and Kristin E. Burnum-Johnson ()
Additional contact information
Paul D. Piehowski: Pacific Northwest National Laboratory
Ying Zhu: Pacific Northwest National Laboratory
Lisa M. Bramer: Pacific Northwest National Laboratory
Kelly G. Stratton: Pacific Northwest National Laboratory
Rui Zhao: Pacific Northwest National Laboratory
Daniel J. Orton: Pacific Northwest National Laboratory
Ronald J. Moore: Pacific Northwest National Laboratory
Jia Yuan: Cincinnati Children’s Hospital Medical Center
Hugh D. Mitchell: Pacific Northwest National Laboratory
Yuqian Gao: Pacific Northwest National Laboratory
Bobbie-Jo M. Webb-Robertson: Pacific Northwest National Laboratory
Sudhansu K. Dey: Cincinnati Children’s Hospital Medical Center
Ryan T. Kelly: Pacific Northwest National Laboratory
Kristin E. Burnum-Johnson: Pacific Northwest National Laboratory

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract 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-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.

Date: 2020
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DOI: 10.1038/s41467-019-13858-z

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