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超高质量分辨率 MALDI 成像质谱法在胶质母细胞瘤小鼠模型中对蛋白质和代谢物的分析。

Ultra-High Mass Resolution MALDI Imaging Mass Spectrometry of Proteins and Metabolites in a Mouse Model of Glioblastoma.

机构信息

Fondazione Pisana per la Scienza ONLUS - Via Panfilo Castaldi 2, 56121, Pisa, Italy.

Department of Chemistry and Industrial Chemistry - Università di Pisa - Via Giuseppe Moruzzi 13, 56124, Pisa, Italy.

出版信息

Sci Rep. 2017 Apr 4;7(1):603. doi: 10.1038/s41598-017-00703-w.

Abstract

MALDI mass spectrometry imaging is able to simultaneously determine the spatial distribution of hundreds of molecules directly from tissue sections, without labeling and without prior knowledge. Ultra-high mass resolution measurements based on Fourier-transform mass spectrometry have been utilized to resolve isobaric lipids, metabolites and tryptic peptides. Here we demonstrate the potential of 15T MALDI-FTICR MSI for molecular pathology in a mouse model of high-grade glioma. The high mass accuracy and resolving power of high field FTICR MSI enabled tumor specific proteoforms, and tumor-specific proteins with overlapping and isobaric isotopic distributions to be clearly resolved. The protein ions detected by MALDI MSI were assigned to proteins identified by region-specific microproteomics (0.8 mm regions isolated using laser capture microdissection) on the basis of exact mass and isotopic distribution. These label free quantitative experiments also confirmed the protein expression changes observed by MALDI MSI and revealed changes in key metabolic proteins, which were supported by in-situ metabolite MALDI MSI.

摘要

基质辅助激光解吸电离傅里叶变换离子回旋共振质谱成像能够直接从组织切片中同时确定数百种分子的空间分布,无需标记且无需事先了解。基于傅里叶变换质谱的超高质量分辨率测量已被用于解析同量异位脂质、代谢物和胰蛋白酶肽。在这里,我们展示了在高级别神经胶质瘤的小鼠模型中,15T MALDI-FTICR MSI 在分子病理学中的潜力。高场 FTICR MSI 的高质量精度和分辨率能够清晰地区分肿瘤特异性蛋白异构体以及具有重叠和同量异位同位素分布的肿瘤特异性蛋白。基于精确质量和同位素分布,通过 MALDI MSI 检测到的蛋白质离子被分配到通过区域特异性微量蛋白质组学(使用激光捕获显微切割分离的 0.8mm 区域)鉴定的蛋白质。这些无标记定量实验还证实了 MALDI MSI 观察到的蛋白质表达变化,并揭示了关键代谢蛋白的变化,这些变化得到了原位代谢物 MALDI MSI 的支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de4c/5429601/5524e8e87f03/41598_2017_703_Fig1_HTML.jpg

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