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

Authors :
Daniel J. Orton
Rui Zhao
Kelly L. Stratton
Ronald J. Moore
Jia Yuan
Hugh D. Mitchell
Ryan T. Kelly
Paul D. Piehowski
Kristin E. Burnum-Johnson
Ying Zhu
Sudhansu K. Dey
Lisa M. Bramer
Bobbie-Jo M. Webb-Robertson
Yuqian Gao
Source :
Nature Communications, Vol 11, Iss 1, Pp 1-12 (2020), Nature Communications
Publication Year :
2020
Publisher :
Nature Portfolio, 2020.

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.<br />Imaging mass spectrometry is a powerful emerging tool for mapping the spatial distribution of biomolecules across tissue surfaces. Here the authors showcase an automated technology for deep proteome imaging that utilizes ultrasensitive microfluidics and a mass spectrometry workflow to analyze tissue voxels, generating quantitative cell-type-specific images.

Details

Language :
English
ISSN :
20411723
Volume :
11
Issue :
1
Database :
OpenAIRE
Journal :
Nature Communications
Accession number :
edsair.doi.dedup.....f4d990c659184a61d9650297894dec13