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Classifying human brain tumors by lipid imaging with mass spectrometry.

ABSTRACT: Brain tissue biopsies are required to histologically diagnose brain tumors, but current approaches are limited by tissue characterization at the time of surgery. Emerging technologies such as mass spectrometry imaging can enable a rapid direct analysis of cancerous tissue based on molecular composition. Here, we illustrate how gliomas can be rapidly classified by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI-MS imaging was carried out on 36 human glioma samples, including oligodendroglioma, astrocytoma, and oligoastrocytoma, all of different histologic grades and varied tumor cell concentration. Gray and white matter from glial tumors were readily discriminated and detailed diagnostic information could be provided. Classifiers for subtype, grade, and concentration features generated with lipidomic data showed high recognition capability with more than 97% cross-validation. Specimen classification in an independent validation set agreed with expert histopathology diagnosis for 79% of tested features. Together, our findings offer proof of concept that intraoperative examination and classification of brain tissue by mass spectrometry can provide surgeons, pathologists, and oncologists with critical and previously unavailable information to rapidly guide surgical resections that can improve management of patients with malignant brain tumors.

SUBMITTER: Eberlin LS

PROVIDER: S-EPMC3271168 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Classifying human brain tumors by lipid imaging with mass spectrometry.

Eberlin Livia S LS Norton Isaiah I Dill Allison L AL Golby Alexandra J AJ Ligon Keith L KL Santagata Sandro S Cooks R Graham RG Agar Nathalie Y R NY

Cancer research 20111202 3

Brain tissue biopsies are required to histologically diagnose brain tumors, but current approaches are limited by tissue characterization at the time of surgery. Emerging technologies such as mass spectrometry imaging can enable a rapid direct analysis of cancerous tissue based on molecular composition. Here, we illustrate how gliomas can be rapidly classified by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging, multivariate statistical analysis, and machine learning. DESI- ...[more]

PMID: 22139378

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Project description:IntroductionAlzheimer's disease (AD), the leading cause of dementia, is pathologically characterized by β-amyloid plaques and tau tangles. However, there is also evidence of lipid dyshomeostasis-mediated AD pathology. Given the structural diversity of lipids, mass spectrometry is a useful tool for studying lipid changes in AD. Although there have been a few studies investigating lipid changes in the human hippocampus in particular, there are few reports on how lipids change in each hippocampal subfield (e.g., Cornu Ammonis [CA] 1-4, dentate gyrus [DG] etc.). Since each subfield has its own function, we postulated that there could be lipid changes that are unique to each.MethodsWe used matrix-assisted laser desorption/ionization-imaging mass spectrometry to investigate specific lipid changes in each subfield in AD. Data from the hippocampus region of six age- and gender-matched normal and AD pairs were analyzed with SCiLS lab 2015b software (SCiLS GmbH, Germany; RRID:SCR_014426), using an analysis workflow developed in-house. Hematoxylin, eosin, and luxol fast blue staining were used to precisely delineate each anatomical hippocampal subfield. Putative lipid identities, which were consistent with published data, were assigned using MS/MS.ResultsBoth positively and negatively charged lipid ion species were abundantly detected in normal and AD tissue. While the distribution pattern of lipids did not change in AD, the abundance of some lipids changed, consistent with trends that have been previously reported. However, our results indicated that the majority of these lipid changes specifically occur in the CA1 region. Additionally, there were many lipid changes that were specific to the DG.ConclusionsMatrix-assisted laser desorption/ionization-imaging mass spectrometry and our analysis workflow provide a novel method to investigate specific lipid changes in hippocampal subfields. Future work will focus on elucidating the role that specific lipid differences in each subfield play in AD pathogenesis.

Dataset Information

Classifying human brain tumors by lipid imaging with mass spectrometry.

Publications

Classifying human brain tumors by lipid imaging with mass spectrometry.

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