Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture

化学 质谱成像 脂类学 质谱法 轨道轨道 规范化(社会学) 脂质代谢 色谱法 马尔迪成像 分析化学(期刊) 生物化学 基质辅助激光解吸/电离 社会学 吸附 解吸 有机化学 人类学
作者
Michiel Vandenbosch,Shadrack M. Mutuku,Maria José Q. Mantas,Nathan Heath Patterson,Tucker Hallmark,Marc Claesen,Ron M. A. Heeren,Nathan G. Hatcher,Nico Verbeeck,Kim Ekroos,Shane R. Ellis
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:95 (51): 18719-18730 被引量:9
标识
DOI:10.1021/acs.analchem.3c02724
摘要

Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm2) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.
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