Dataset Information

Hierarchical Cluster and Region of Interest Analyses Based on Mass Spectrometry Imaging of Human Brain Tumours.

ABSTRACT: Imaging mass spectrometry (IMS) has been rarely used to examine specimens of human brain tumours. In the current study, high quality brain tumour samples were selected by tissue observation. Further, IMS analysis was combined with a new hierarchical cluster analysis (IMS-HCA) and region of interest analysis (IMS-ROI). IMS-HCA was successful in creating groups consisting of similar signal distribution images of glial fibrillary acidic protein (GFAP) and related multiple proteins in primary brain tumours. This clustering data suggested the relation of GFAP and these identified proteins in the brain tumorigenesis. Also, high levels of histone proteins, haemoglobin subunit ?, tubulins, and GFAP were identified in a metastatic brain tumour using IMS-ROI. Our results show that IMS-HCA and IMS-ROI are promising techniques for identifying biomarkers using brain tumour samples.

SUBMITTER: Hiratsuka T

PROVIDER: S-EPMC7113320 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Hierarchical Cluster and Region of Interest Analyses Based on Mass Spectrometry Imaging of Human Brain Tumours.

Hiratsuka Takuya T Arakawa Yoshiki Y Yajima Yuka Y Kakimoto Yu Y Shima Keisuke K Yamazaki Yuzo Y Ikegami Masahiro M Yamamoto Takushi T Fujiwake Hideshi H Fujimoto Koichi K Yamada Norishige N Tsuruyama Tatsuaki T

Scientific reports 20200401 1

Imaging mass spectrometry (IMS) has been rarely used to examine specimens of human brain tumours. In the current study, high quality brain tumour samples were selected by tissue observation. Further, IMS analysis was combined with a new hierarchical cluster analysis (IMS-HCA) and region of interest analysis (IMS-ROI). IMS-HCA was successful in creating groups consisting of similar signal distribution images of glial fibrillary acidic protein (GFAP) and related multiple proteins in primary brain ...[more]

PMID: 32238824

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Project description:Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in all data sets. This work highlights the importance of technical replication in mass spectrometry-based studies, and presents a new application of HCA as a tool for evaluating the effectiveness of data filtering prior to statistical analysis.

Dataset Information

Hierarchical Cluster and Region of Interest Analyses Based on Mass Spectrometry Imaging of Human Brain Tumours.

Publications

Hierarchical Cluster and Region of Interest Analyses Based on Mass Spectrometry Imaging of Human Brain Tumours.

Similar Datasets

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