Spatially resolved proteomics via tissue expansion

Lu Li, Cuiji Sun, Yaoting Sun, Zhen Dong, Runxin Wu, Xiaoting Sun, Hanbin Zhang, Wenhao Jiang, Yan Zhou, Xufeng Cen, Shang Cai, Hongguang Xia, Yi Zhu, Tiannan Guo () and Kiryl D. Piatkevich ()
Additional contact information
Lu Li: Westlake University
Cuiji Sun: Westlake University
Yaoting Sun: Westlake University
Zhen Dong: Westlake University
Runxin Wu: Westlake University
Xiaoting Sun: Westlake University
Hanbin Zhang: Westlake University
Wenhao Jiang: Westlake University
Yan Zhou: Westlake University
Xufeng Cen: Zhejiang University School of Medicine
Shang Cai: Westlake University
Hongguang Xia: Zhejiang University School of Medicine
Yi Zhu: Westlake University
Tiannan Guo: Westlake University
Kiryl D. Piatkevich: Westlake University

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity of biological samples, however, it remains technically challenging due to the complexity of the tissue microsampling techniques and mass spectrometry analysis of nanoscale specimen volumes. Here, we describe a spatially resolved proteomics method based on the combination of tissue expansion with mass spectrometry-based proteomics, which we call Expansion Proteomics (ProteomEx). ProteomEx enables quantitative profiling of the spatial variability of the proteome in mammalian tissues at ~160 µm lateral resolution, equivalent to the tissue volume of 0.61 nL, using manual microsampling without the need for custom or special equipment. We validated and demonstrated the utility of ProteomEx for streamlined large-scale proteomics profiling of biological tissues including brain, liver, and breast cancer. We further applied ProteomEx for identifying proteins associated with Alzheimer’s disease in a mouse model by comparative proteomic analysis of brain subregions.

Date: 2022
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DOI: 10.1038/s41467-022-34824-2

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