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Evaluation of lipid coverage and high spatial resolution MALDI-imaging capabilities of oversampling combined with laser post-ionisation.

ABSTRACT: Matrix-assisted laser desorption/ionisation-mass spectrometry imaging (MALDI-MSI) is a powerful technique for visualising the spatial locations of lipids in biological tissues. However, a major challenge in interpreting the biological significance of local lipid compositions and distributions detected using MALDI-MSI is the difficulty in associating spectra with cellular lipid metabolism within the tissue. By-and-large this is due to the typically limited spatial resolution of MALDI-MSI (30-100 ?m) meaning individual spectra represent the average spectrum acquired from multiple adjacent cells, each potentially possessing a unique lipid composition and biological function. The use of oversampling is one promising approach to decrease the sampling area and improve the spatial resolution in MALDI-MSI, but it can suffer from a dramatically decreased sensitivity. In this work we overcome these challenges through the coupling of oversampling MALDI-MSI with laser post-ionisation (MALDI-2). We demonstrate the ability to acquire rich lipid spectra from pixels as small as 6 ?m, equivalent to or smaller than the size of typical mammalian cells. Coupled with an approach for automated lipid identification, it is shown that MALDI-2 combined with oversampling at 6 ?m pixel size can detect up to three times more lipids and many more lipid classes than even conventional MALDI at 20 ?m resolution in the positive-ion mode. Applying this to mouse kidney and human brain tissue containing active multiple sclerosis lesions, where 74 and 147 unique lipids are identified, respectively, the localisation of lipid signals to individual tubuli within the kidney and lipid droplets with lesion-specific macrophages is demonstrated. Graphical abstract.

SUBMITTER: Bowman AP

PROVIDER: S-EPMC7118047 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Evaluation of lipid coverage and high spatial resolution MALDI-imaging capabilities of oversampling combined with laser post-ionisation.

Bowman Andrew P AP Bogie Jeroen F J JFJ Hendriks Jerome J A JJA Haidar Mansour M Belov Mikhail M Heeren Ron M A RMA Ellis Shane R SR

Analytical and bioanalytical chemistry 20191226 10

Matrix-assisted laser desorption/ionisation-mass spectrometry imaging (MALDI-MSI) is a powerful technique for visualising the spatial locations of lipids in biological tissues. However, a major challenge in interpreting the biological significance of local lipid compositions and distributions detected using MALDI-MSI is the difficulty in associating spectra with cellular lipid metabolism within the tissue. By-and-large this is due to the typically limited spatial resolution of MALDI-MSI (30-100 ...[more]

PMID: 31879798

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Project description:BackgroundCurrent risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF.MethodsA cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF.ResultsAfter normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005).ConclusionsThe results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.

Dataset Information

Evaluation of lipid coverage and high spatial resolution MALDI-imaging capabilities of oversampling combined with laser post-ionisation.

Publications

Evaluation of lipid coverage and high spatial resolution MALDI-imaging capabilities of oversampling combined with laser post-ionisation.

Similar Datasets

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