MALDI imaging mass spectrometry: Discrimination of pathophysiological regions in traumatized skeletal muscle by characteristic peptide signatures

Klein, Oliver; Strohschein, Kristin; Nebrich, Grit; Oetjen, Janina; Trede, Dennis; Thiele, Herbert; Alexandrov, Theodore; Giavalisco, Patrick; Duda, Georg N.; Roth, Philipp von; Geißler, Sven; Klose, Joachim; Winkler, Tobias

doi:10.1002/pmic.201400088

Cited by 51 publications

(65 citation statements)

References 61 publications

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“…After MALDI IMS analysis, spectra and images acquired from the oxaliplatin treated and untreated MCTS were analyzed by calculating co-localized and anti-correlated m/z values as well as using the ROC tool to find discriminating m/z signals. ROC analysis is a univariate measurement determining how well a selected m/z value distinguishes two different samples38. It includes estimating specificity and sensitivity values for a trivial threshold classifier, followed by plotting a curve for the computed results.…”

Section: Resultsmentioning

confidence: 99%

Chemical Imaging of Platinum-Based Drugs and their Metabolites

Liu

Hummon

2016

Sci Rep

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Platinum-based drugs (cisplatin, carboplatin, and oxaliplatin) are widely used therapeutic agents for cancer treatment. Even though the platinum (Pt)-drugs are routinely used clinically, a clear picture of their distribution within tumor tissues is lacking. The current methods to image the distribution of Pt drugs are limited and do not enable the discrimination of the drug from its metabolites. In this manuscript, we demonstrate a methodology that enables chemical imaging of a Pt drug and its metabolites simultaneously and specifically. Matrix-Assisted Laser Desorption/Ionization (MALDI) Mass Spectrometry Imaging (MSI) is combined with an on-tissue chemical derivatization using diethyldithiocarbamate (DDTC). DDTC abstracts the Pt atom to generate ionizable complexes that can be imaged by MALDI MSI. We demonstrate that Pt drugs and their metabolites can be specifically imaged. This approach was successfully applied to map the penetration and metabolism of oxaliplatin in hyperthermic intraperitoneal chemotherapy (HIPEC)-like treated 3D colorectal tumor mimics. The distribution of cisplatin and carboplatin was mapped in additional 3D tumor mimics. We demonstrate that the approach can also be used to image the distribution of copper ions in cells. This method has the potential to be used to evaluate the penetration and distribution of a wide range of compounds.

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Section: Resultsmentioning

confidence: 99%

Chemical Imaging of Platinum-Based Drugs and their Metabolites

Liu

Hummon

2016

Sci Rep

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“…An Anderson-Darling test was performed to determine that these values were not normally distributed (p-value > 0.05) and therefore a non-parametric, Wilcoxon test was chosen for statistical analysis. Molecules with an AUC value of >0.8 and a p-value of <0.05 were deemed to be discriminative of the hypocellular, bacteria present regions 58 . Data processing used to calculate the co-localisation of low cell density regions of the MLNS with the MSI data is described in detail in the supplementary information.…”

Section: Methodsmentioning

confidence: 99%

Mass spectrometry imaging identifies palmitoylcarnitine as an immunological mediator during Salmonella Typhimurium infection

Hulme

Meikle

Wessel

et al. 2017

Sci Rep

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Salmonella Typhimurium causes a self-limiting gastroenteritis that may lead to systemic disease. Bacteria invade the small intestine, crossing the intestinal epithelium from where they are transported to the mesenteric lymph nodes (MLNs) within migrating immune cells. MLNs are an important site at which the innate and adaptive immune responses converge but their architecture and function is severely disrupted during S. Typhimurium infection. To further understand host-pathogen interactions at this site, we used mass spectrometry imaging (MSI) to analyse MLN tissue from a murine model of S. Typhimurium infection. A molecule, identified as palmitoylcarnitine (PalC), was of particular interest due to its high abundance at loci of S. Typhimurium infection and MLN disruption. High levels of PalC localised to sites within the MLNs where B and T cells were absent and where the perimeter of CD169+ sub capsular sinus macrophages was disrupted. MLN cells cultured ex vivo and treated with PalC had reduced CD4+CD25+ T cells and an increased number of B220+CD19+ B cells. The reduction in CD4+CD25+ T cells was likely due to apoptosis driven by increased caspase-3/7 activity. These data indicate that PalC significantly alters the host response in the MLNs, acting as a decisive factor in infection outcome.

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“…Furthermore, the FastMap (Faloutsos & Lin, ) transformation is introduced for dimensionality reduction, and makes the calculation of the distances between pixels more efficient. The

k

‐means method, and later bisecting

k

‐means (Trede et al, ; Klein et al, ; Thiele et al, ; Krasny et al, ), have been used to cluster the data after the dimensionality reduction step. When comparing the newly developed methods to HC combined with PCA, the spatially aware clustering approaches produced smoother, more homogeneous segmentation maps.…”

Section: Clusteringmentioning

confidence: 99%

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Verbeeck

Caprioli

Plas

2019

Mass Spectrometry Reviews

201

190

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Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high‐dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data‐driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry‐based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1–47, 2019.

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MALDI imaging mass spectrometry: Discrimination of pathophysiological regions in traumatized skeletal muscle by characteristic peptide signatures

Cited by 51 publications

References 61 publications

Chemical Imaging of Platinum-Based Drugs and their Metabolites

Chemical Imaging of Platinum-Based Drugs and their Metabolites

Mass spectrometry imaging identifies palmitoylcarnitine as an immunological mediator during Salmonella Typhimurium infection

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

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