Combining MALDI-TOF and molecular imaging with principal component analysis for biomarker discovery and clinical diagnosis of cancer

Cho, Yi‐Tzu; Chiang, Yi-Yan; Shiea, Jentaie; Hou, Ming‐Feng

doi:10.1016/j.gmbhs.2012.04.022

Cited by 9 publications

(12 citation statements)

References 15 publications

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“…When compared to MALDI-TOF-MS, MALDI-IMS-MS was reported to reduce chemical noise, increase sequence coverage (up to 70% sequence coverage for a cytochrome C tryptic digest) and allow high-throughput separation of a complex mixture. 12–14 There are many reports in the literature of the use of MALDI-TOF-MS 17–19 and MALDI-IMS-(MS/MS) 20–23 both in combination with MS imaging for in-situ detection of cancer biomarkers (peptides, proteins and lipids). However, to the best of our knowledge, MALDI-IMS-MS has not been used for the discovery of biomarkers within the serum glycome.…”

Section: Introductionmentioning

confidence: 99%

Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans

Gaye

Ding

Shion

et al. 2017

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N-linked glycans, extracted from patient sera and healthy control individuals, are analyzed by Matrix-assisted laser desorption ionization (MALDI) in combination with ion mobility spectrometry (IMS), mass spectrometry (MS) and pattern recognition methods. MALDI-IMS-MS data were collected in duplicate for 58 serum samples obtained from individuals diagnosed with Barrett’s esophagus (BE, 14 patients), high-grade dysplasia (HGD, 7 patients), esophageal adenocarcinoma (EAC, 20 patients) and disease-free control (NC, 17 individuals). A combined mobility distribution of 9 N-linked glycans is established for 90 MALDI-IMS-MS spectra (training set) and analyzed using a genetic algorithm for feature selection and classification. Two models for phenotype delineation are subsequently developed and as a result, the four phenotypes (BE, HGD, EAC and NC) are unequivocally differentiated. Next, the two models are tested against 26 blind measurements. Interestingly, these models allowed for the correct phenotype prediction of as many as 20 blinds. Although applied to a limited number of blind samples, this methodology appears promising as a means of discovering molecules from serum that may have capabilities as markers of disease.

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Section: Introductionmentioning

confidence: 99%

Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans

Gaye

Ding

Shion

et al. 2017

Analyst

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“…[35], in which MS data were collected and statistical tools were used to identify notable features in the data. MALDI-MS data were used without retention time separation to identify significantly changing peaks.…”

Section: Discussionmentioning

confidence: 99%

Changes in Cancer Cell Metabolism Revealed by Direct Sample Analysis with MALDI Mass Spectrometry

et al. 2013

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Biomarker discovery using mass spectrometry (MS) has recently seen a significant increase in applications, mainly driven by the rapidly advancing field of metabolomics. Instrumental and data handling advancements have allowed for untargeted metabolite analyses which simultaneously interrogate multiple biochemical pathways to elucidate disease phenotypes and therapeutic mechanisms. Although most MS-based metabolomic approaches are coupled with liquid chromatography, a few recently published studies used matrix-assisted laser desorption (MALDI), allowing for rapid and direct sample analysis with minimal sample preparation. We and others have reported that prostaglandin E3 (PGE3), derived from COX-2 metabolism of the omega-3 fatty acid eicosapentaenoic acid (EPA), inhibited the proliferation of human lung, colon and pancreatic cancer cells. However, how PGE3 metabolism is regulated in cancer cells, particularly human non-small cell lung cancer (NSCLC) cells, is not fully understood. Here, we successfully used MALDI to identify differences in lipid metabolism between two human non-small-cell lung cancer (NSCLC) cell lines, A549 and H596, which could contribute to their differential response to EPA treatment. Analysis by MALDI-MS showed that the level of EPA incorporated into phospholipids in H596 cells was 4-fold higher than A549 cells. Intriguingly, H596 cells produced much less PGE3 than A549 cells even though the expression of COX-2 was similar in these two cell lines. This appears to be due to the relatively lower expression of cytosolic phospholipase A2 (cPLA2) in H596 cells than that of A549 cells. Additionally, the MALDI-MS approach was successfully used on tumor tissue extracts from a K-ras transgenic mouse model of lung cancer to enhance our understanding of the mechanism of action of EPA in the in vivo model. These results highlight the utility of combining a metabolomics workflow with MALDI-MS to identify the biomarkers that may regulate the metabolism of omega-3 fatty acids and ultimately affect their therapeutic potentials.

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“…Cho et al () used PCA to compare the lipid, peptide, and protein profiles of various biological matrices, including MS images of tissue sections. They used PCA loadings to select the ions differentiating biological regions in a tissue sample.…”

Section: Multivariate Analysis Of Imagesmentioning

confidence: 99%

Signal preprocessing, multivariate analysis and software tools for MA(LDI)‐TOF mass spectrometry imaging for biological applications

Ràfols

Vilalta

Brezmes

et al. 2016

Mass Spectrometry Reviews

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Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.

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Combining MALDI-TOF and molecular imaging with principal component analysis for biomarker discovery and clinical diagnosis of cancer

Cited by 9 publications

References 15 publications

Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans

Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans

Changes in Cancer Cell Metabolism Revealed by Direct Sample Analysis with MALDI Mass Spectrometry

Signal preprocessing, multivariate analysis and software tools for MA(LDI)‐TOF mass spectrometry imaging for biological applications

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