School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
J Am Soc Mass Spectrom. 2020 Apr 1;31(4):986-989. doi: 10.1021/jasms.9b00094. Epub 2020 Mar 19.
Multimodal mass spectrometry imaging (MSI) data presents unique big data challenges in handling and analysis. Here, we present a pipeline for co-registering matrix-assisted laser desorption/ionization MSI and confocal immunofluorescence imaging data for extracting single-cell metabolite signatures. We further describe methods and introduce software for the simultaneous analysis of these concatenated data sets, which are designed to establish a connection between cell traits of interest (shape metrics, position within sample) and the cells' own metabolic signatures.
多模态质谱成像 (MSI) 数据在处理和分析方面带来了独特的大数据挑战。在这里，我们提出了一种用于共配准基质辅助激光解吸/电离 MSI 和共聚焦免疫荧光成像数据以提取单细胞代谢特征的工作流程。我们进一步描述了用于同时分析这些串联数据集的方法和软件，这些方法和软件旨在建立感兴趣的细胞特征（形状度量、在样本中的位置）与细胞自身代谢特征之间的联系。
