103 research outputs found
2.5D mass spectrometry imaging of N-glycans in esophageal adenocarcinoma and precursor lesions
No full textData of publication: Vos DRN, Meijer SL, Pouw RE, Ellis SR, Heeren RMA and Balluff B (2022), 2.5D
mass spectrometry imaging of Nglycans in esophageal adenocarcinoma and precursor lesions. Front. Anal. Sci. 2:1010317.doi: 10.3389/frans.2022.101031
MALDI imaging mass spectrometry in clinical proteomics research of gastric cancer tissues
No full textIn the presented thesis, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry was used for the proteomic analysis of gastric cancer tissue samples, with the aims of 1) identifying proteins that predict disease outcome of patients with intestinal-type gastric cancer after surgical resection, and 2) generating a proteomic classifier that determines HER2-status in order to aid in therapy decision with regard to trastuzumab (Herceptin) administration.
In the first study, a seven-protein signature was found to be associated with an unfavorable overall survival independent of major clinical covariates after analyzing 63 intestinal-type primary resected gastric cancer samples by MALDI imaging. Of these seven proteins, three could be identified as CRIP1, HNP-1, and S100-A6, and validated immunohistochemically on tissue microarrays of an independent validation cohort (n=118). While HNP-1 and S100-A6 were found to further subdivide early (UICC-I) and late stage (UICC-II-III) patients into different prognostic groups, CRIP1, a protein previously unknown in gastric cancer, was confirmed as a novel and independent prognostic factor for all patients in the validation cohort. The protein pattern described here serves as a new independent indicator of patient survival complementing the previously known clinical parameters in terms of prognostic relevance.
In the second study, we hypothesized that MALDI imaging mass spectrometry may be useful for generating a classifier that may determine HER2-status in gastric cancer. This assumption was based on previous results where HER2-status could be reliably predicted in breast cancer patients. Here, 59 gastric cryo tissue samples were analyzed by MALDI imaging and the obtained proteomic profiles were used to create HER2 prediction models using different classification algorithms. Astonishingly, the breast cancer proteomic classifier from the previous study was able to correctly predict HER2-status in gastric cancers with a sensitivity of 65% and a specificity of 92%. In order to create a universal classifier for HER2-status, breast and non-breast cancer samples were combined, which increased sensitivity to 78%; specificity was 88%. This study provides evidence that HER2-status can be identified on a proteomic level across different cancer types suggesting that HER2 overexpression may constitute a widely spread molecular event independent of the tumor entity.Im Rahmen dieser Doktorarbeit wurden zwei Arbeiten publiziert, in denen die bildgebende Massenspektrometrie als zentrale Methode zur proteomischen Analyse von Magenkarzinomgeweben eingesetzt wurde. Dabei wurden folgende Ziele verfolgt: 1. die Identifizierung prognostischer Proteinmarker für Patienten mit intestinalem Magenkarzinom, und 2. die Generierung eines proteomischen Klassifikators zur Bestimmung des HER2-Status zur Entscheidungshilfe für eine Behandlung mit Trastuzumab (Herzeptin).
In der ersten Studie wurde eine Signatur bestehend aus sieben Proteinsignalen gefunden, deren Überexpression unabhängig von anderen klinischen Parametern ein schlechtes Gesamtüberleben der Patienten indizieren. Hierzu wurden 63 Gewebeproben von Patienten mit Magenkarzinom intestinalen Typs mittels MALDI Imaging analysiert. Drei der sieben Proteinsignale konnten als CRIP1, HNP-1 und S100-A6 identifiziert werden. Diese wurden anschließend an einem unabhängigen Patientenkollektiv (n=118) immunhistochemisch anhand von Tissue Microarrays validiert. Dabei zeigte sich, dass die beiden Proteine HNP-1 und S100-A6 bestehende klinische Gruppen nach ihrem Risiko weiter aufstratifizieren konnten; HNP-1 Magenkarzinompatienten im frühen Stadium (UICC I) und S100-A6 Patienten im fortgeschrittenen Stadium (UICC II-III). Darüber hinaus konnte CRIP1 als unabhängiger prognostischer Faktor für alle Patienten des Validierungskollektives bestätigt werden. Perspektivisch könnte die hier beschriebene Proteinsignatur vorhandene klinische Parameter als neuer und unabhängiger Indikator für das Überleben von Magenkrebspatienten ergänzen.
In der zweiten Studie wurden Proteinexpressionsmuster benutzt, um den HER2-Status in Magenkrebsgeweben vorauszusagen; denn seit kurzem ist der epidermale Wachstumsfaktor-Rezeptor HER2 eine wichtige tumorbiologische Zielstruktur bei der Behandlung von Magenkrebspatienten mit dem therapeutischen Antikörper Trastuzumab. In einer vorherigen Studie konnten wir die Machbarkeit der HER2-Status-Bestimmung durch MALDI Imaging erfolgreich anhand von Brustkrebsproben demonstrieren. Unter der Annahme, dass der HER2-Überexpression – unabhängig vom Tumortyp – charakteristische molekulare Veränderungen zugrunde liegen, wurde untersucht, ob eine Bestimmung des HER2-Status in Magenkrebspatienten mit Hilfe von Proteinexpressionsmustern aus Brustkrebspatienten erfolgen kann. Hierzu wurden, zusätzlich zu den bereits vorhandenen 48 Brustkrebsgeweben, 59 Magenkrebsfälle mittels MALDI Imaging analysiert und verschiedene HER2-Klassifikationsmodelle erstellt und verglichen. Der HER2-Status in Magenkrebsfällen konnte mit einem Mammakarzinom-spezifischen Profil mit einer Sensitivität von 65% und einer Spezifität von 92% bestimmt werden. Zusätzlich wurden die Expressionsprofile aller vorhandenen Tumorarten zusammengeführt, um einen universellen HER2-Klassifikator zu erstellen. Dies führte zu einer verbesserten Vorhersagequalität (Sensitivität: 78%, Spezifität: 88%). Dass sich der HER2-Status über verschiedene Tumorentitäten hinweg auf proteomischer Ebene bestimmen lässt, legt nahe, dass die Überexpression von HER2 ein unabhängiges molekulares Ereignis darstellt, ungeachtet der Herkunft des Tumors. Zudem unterstreichen die Ergebnisse das diagnostische Potential der bildgebenden Massenspektrometrie zur schnellen und zuverlässigen Bestimmung von tumorbiologischen Zielstrukturen, wie HER2
Mass Spectrometry Imaging of Metabolites
No full textMass spectrometry imaging (MSI) is a technique which is gaining increasing interest in biomedical research due to its capacity to visualize molecules in tissues. First applied to the field of clinical proteomics, its potential for metabolite imaging in biomedical studies is now being recognized. Here we describe how to set up experiments for mass spectrometry imaging of metabolites in clinical tissues and how to tackle most of the obstacles in the subsequent analysis of the data
Comprehensive identification of proteins from MALDI imaging
No full textMatrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful tool for the visualization of proteins in tissues and has demonstrated considerable diagnostic and prognostic value. One main challenge is that the molecular identity of such potential biomarkers mostly remains unknown. We introduce a generic method that removes this issue by systematically identifying the proteins embedded in the MALDI matrix using a combination of bottom-up and top-down proteomics. The analyses of ten human tissues lead to the identification of 1400 abundant and soluble proteins constituting the set of proteins detectable by MALDI IMS including >90% of all IMS biomarkers reported in the literature. Top-down analysis of the matrix proteome identified 124 mostly N- and C-terminally fragmented proteins indicating considerable protein processing activity in tissues. All protein identification data from this study as well as the IMS literature has been deposited into MaTisse, a new publically available database, which we anticipate will become a valuable resource for the IMS community.Stefan K. Maier, Hannes Hahne, Amin Moghaddas Gholami, Benjamin Balluff, Stephan Meding, Cedrik Schoene, Axel K. Walch, and Bernhard Kuste
Systems biology approaches applied in mass spectrometry imaging
No full textAt the core of this research lies the technology of Mass Spectrometry Imaging (MSI). While MSI is a novel, powerful research tool for mapping the molecular composition directly from tissue whilst preserving spatial morphology, it does not hold all the answers to the complex (clinical) biological questions. Thereto, this thesis presents various efforts to integrate MSI data with other valuable, complementary, imaging or non-imaging data sources in order to investigate systematically the complex tissue biology in health and disease. For instance, this work delivers workflows that accelerate histological annotation for rapid correlation with the molecular information provided by high-spatial-resolution MSI and as such lays out the future of high throughput, automated digital pathology-based diagnosis. The highlight of this PhD is a study conducted in collaboration with Johns Hopkins, where the aforementioned multi-disciplinary strategies were employed to investigate molecular signature of metastatic breast cancer in a unique multi-organ sample set from several breast cancer patients. The findings emphasize the clinical importance and benefits of spatial tissue analysis by MSI, which combined with extensive pathology annotation leads to research opportunities with unprecedented translational potential
Mass spectrometry-based imaging
No full textDie massenspektrometrische Bildgebung (MSI) ist ein leistungsfähiges bildgebendes Verfahren zur Visualisierung der räumlichen Verteilung chemischer Komponenten in Proben, von anorganischen und organischen Materialien bis hin zu biologischen Geweben und einzelnen Zellen. Im Gegensatz zu anderen bildgebenden Verfahren wie immunhistochemischer Bildgebung, chemischer Färbung und Fluoreszenzmikroskopie ist MSI eine markierungsfreie Technik und ermöglicht die Überwachung von Tausenden von Verbindungen in einem einzigen Experiment. Aufgrund seiner einzigartigen Eigenschaften, insbesondere der nicht zielgerichteten Detektion, der hohen chemischen Spezifität und der hohen Genauigkeit für die Strukturaufklärung, wurde MSI schnell und kontinuierlich weiterentwickelt, um die Erwartungen und Bedürfnisse verschiedener Forschungsbereiche zu erfüllen.Mass spectrometry imaging (MSI) is a powerful imaging technique for visualising
the spatial distribution of chemical components in samples, from inorganic and
organic materials to biological tissues and single cells. Contrary to other imaging
techniques such as immunohistochemical imaging, chemical staining, and fluores cence microscopy, MSI is a label-free technique and enables monitoring thousands
of compounds in a single experiment. Owing to its unique characteristics, especially
the non-targeted detection, high chemical specificity, and high accuracy for
structural elucidation, MSI has been rapidly and continuously developed to meet
the expectations and needs of various research area
Spatial omics to quantitatively study tissue heterogeneity
No full textMass spectrometry imaging (MSI) is an analytical technique for the analysis of the spatial distribution of molecules in biological tissues. It has been shown that it constitutes a unique tool for the investigation of intratumour heterogeneity by being able to segment histologically undistinguishable tumour areas into distinct tumour subpopulations. There is a strong interest in understanding the biology of these tumour populations. However, the analytical depth of MSI (meaning the identification of molecules) is limited. Therefore, in this project, the researchers wanted to couple MSI that enables to highlight tumour subpopulations with another mass spectrometry-based technique (LC-MS) to identify thousands of molecules. This will allow the comprehensive molecular characterization of intratumor heterogeneity to obtain deeper insights into the biological processes of cancer
MALDI imaging mass spectrometry for direct tissue analysis: Technological advancements and recent applications.
No full textMatrix assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a method that allows the investigation of the molecular content of tissues within its morphological context. Since it is able to measure the distribution of hundreds of analytes at once, while being label free, this method has great potential which has been increasingly recognized in the field of tissue-based research. In the last few years, MALDI-IMS has been successfully used for the molecular assessment of tissue samples mainly in biomedical research and also in other scientific fields. The present article will give an update on the application of MALDI-IMS in clinical and preclinical research. It will also give an overview of the multitude of technical advancements of this method in recent years. This includes developments in instrumentation, sample preparation, computational data analysis and protein identification. It will also highlight a number of emerging fields for application of MALDI-IMS like drug imaging where MALDI-IMS is used for studying the spatial distribution of drugs in tissues
Integrative Clustering in Mass Spectrometry Imaging for Enhanced Patient Stratification
No full textScope In biomedical research, mass spectrometry imaging (MSI) can obtain spatially-resolved molecular information from tissue sections. Especially matrix-assisted laser desorption/ionization (MALDI) MSI offers, depending on the type of matrix, the detection of a broad variety of molecules ranging from metabolites to proteins, thereby facilitating the collection of multilevel molecular data. Lately, integrative clustering techniques have been developed that make use of the complementary information of multilevel molecular data in order to better stratify patient cohorts, but which have not yet been applied in the field of MSI. Materials and Methods In this study, the potential of integrative clustering is investigated for multilevel molecular MSI data to subdivide cancer patients into different prognostic groups. Metabolomic and peptidomic data are obtained by MALDI-MSI from a tissue microarray containing material of 46 esophageal cancer patients. The integrative clustering methods Similarity Network Fusion, iCluster, and moCluster are applied and compared to non-integrated clustering. Conclusion The results show that the combination of multilevel molecular data increases the capability of integrative algorithms to detect patient subgroups with different clinical outcome, compared to the single level or concatenated data. This underlines the potential of multilevel molecular data from the same subject using MSI for subsequent integrative clustering
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