Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges

被引:84
作者
Scupakova, Klara [1 ]
Balluff, Benjamin [1 ]
Tressler, Caitlin [2 ]
Adelaja, Tobi [2 ]
Heeren, Ron M. A. [1 ]
Glunde, Kristine [2 ,3 ]
Ertaylan, Gokhan [4 ]
机构
[1] Univ Maastricht, Maastricht MultiModal Mol Imaging Inst M4I, Maastricht, Netherlands
[2] Johns Hopkins Univ, Russell H Morgan Dept Radiol & Radiol Sci, Div Canc Imaging Res, Sch Med, Baltimore, MD USA
[3] Johns Hopkins Univ, Sidney Kimmel Comprehens Canc Ctr, Sch Med, Baltimore, MD USA
[4] Flemish Inst Technol Res VITO, Unit Hlth, Mol, Belgium
基金
美国国家卫生研究院; 欧盟地平线“2020”;
关键词
data analysis; data integration and interpretation; histopathology; mass spectrometry imaging; molecular analysis; spatial resolution; DIRECT TISSUE-ANALYSIS; AUTOMATED ANATOMICAL INTERPRETATION; MATRIX APPLICATION; TOF; LOCALIZATION; REGISTRATION; PROTEINS; LIPIDS; VISUALIZATION; SEGMENTATION;
D O I
10.1515/cclm-2019-0858
中图分类号
R446 [实验室诊断]; R-33 [实验医学、医学实验];
学科分类号
1001 ;
摘要
Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextual-ization of the-information obtained.
引用
收藏
页码:914 / 929
页数:16
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