An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue

一种从人体肺组织获取基于质谱的免费组学数据的优化方法和充气介质

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作者:Jessica K Lukowski, Heather Olson, Marija Velickovic, Juan Wang, Jennifer E Kyle, Young-Mo Kim, Sarah M Williams, Ying Zhu, Heidi L Huyck, Matthew D McGraw, Cory Poole, Lisa Rogers, Ravi Misra, Theodore Alexandrov, Charles Ansong, Gloria S Pryhuber, Geremy Clair, Joshua N Adkins, James P Carson, Chr

Abstract

Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.

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