Metabolic profiles of human brain parenchyma and glioma for rapid tissue diagnosis by targeted desorption electrospray ionization mass spectrometry.
Metabolic profiles of human brain parenchyma and glioma for rapid tissue diagnosis by targeted desorption electrospray ionization mass spectrometry.
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DOI:
10.1007/s00216-021-03593-0
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发表时间:
2021-10
影响因子:
4.3
通讯作者:
Cooks RG
中科院分区:
文献类型:
--
作者:
Chen R;Brown HM;Cooks RG
Desorption electrospray ionization mass spectrometry (DESI-MS) is well suited for intraoperative tissue analysis since it requires little sample preparation and offers rapid and sensitive molecular diagnostics. Currently, intraoperative assessment of the tumor cell percentage of glioma biopsies can be made by measuring a single metabolite, N-acetylaspartate (NAA). The inclusion of additional biomarkers will likely improve the accuracy when distinguishing brain parenchyma from glioma by DESI-MS. To explore this possibility, mass spectra were recorded for extracts from 32 unmodified human brain samples with known pathology. Statistical analysis of data obtained from full scan and multiple reaction monitoring (MRM) profiles identified discriminatory metabolites, namely gamma-aminobutyric acid (GABA), creatine, glutamic acid, carnitine, hexane-1,2,3,4,5,6-hexol (abbreviated as hexol), as well as the established biomarker NAA. Brain parenchyma was readily differentiated from glioma based on these metabolites as measured both in full scan mass spectra and by the intensities of their characteristic MRM transitions. New DESI-MS methods (5 min acquisition using full scans and MS/MS), developed to measure ion abundance ratios among these metabolites, were tested using smears of 29 brain samples. Ion abundance ratios based on signals for GABA, creatine, carnitine and hexol all had sensitivities >90%, specificities >80%, and accuracies >85%. Prospectively, the implementation of diagnostic ion abundance ratios should strengthen the discriminatory power of individual biomarkers and enhance method robustness against signal fluctuations, resulting in an improved DESI-MS method of glioma diagnosis.
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影响因子:
2.3
作者:
Kampa, Judith M.;Kellner, Udo;Bednarz, Hanna
通讯作者:
Bednarz, Hanna
DOI:
10.1073/pnas.1700677114
发表时间:
2017-03-28
影响因子:
11.1
作者:
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Sonn, Geoffrey A.
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4.1
作者:
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通讯作者:
Cohen-Gadol, Aaron A.
影响因子:
2
作者:
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通讯作者:
Abdollahi, Hamid
影响因子:
4.7
作者:
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通讯作者:
Tran NL