Peak annotation and data analysis software tools for mass spectrometry imaging

La metabolòmica espacial és la disciplina que estudia les imatges de les distribucions de compostos químics de baix pes (metabòlits) a la superfície dels teixits biològics per revelar interaccions entre molècules. La imatge d'espectrometria de masses (MSI) és actualment la tècnica principal per obtenir informació d'imatges moleculars per a la metabolòmica espacial. MSI és una tecnologia d'imatges moleculars sense marcador que produeix espectres de masses que conserven les estructures espacials de les mostres de teixit. Això s'aconsegueix ionitzant petites porcions d'una mostra (un píxel) en un ràster definit a través de tota la seva superfície, cosa que dona com a resultat una col·lecció d'imatges de distribució de ions (registrades com a relacions massa-càrrega (m/z)) sobre la mostra. Aquesta tesi té com a objectius desenvolupar eines computacionals per a l'anotació de pics de MSI i el disseny de fluxos de treball per a l'anàlisi estadística i multivariant de dades MSI, inclosa la segmentació espacial. El treball realitzat en aquesta tesi es pot separar clarament en dues parts. En primer lloc, el desenvolupament d'una eina d'anotació de pics d'isòtops i adductes adequada per facilitar la identificació de compostos de rang de massa baix. Ara podem trobar fàcilment ions monoisotòpics als nostres conjunts de dades MSI gràcies al paquet de programari rMSIannotation. En segon lloc, el desenvolupament de eines de programari per a l’anàlisi de dades i la segmentació espacial basades en soft clustering per a dades MSI.La metabolómica espacial es la disciplina que estudia las imágenes de las distribuciones de compuestos químicos de bajo peso (metabolitos) en la superficie de los tejidos biológicos para revelar interacciones entre moléculas. Las imágenes de espectrometría de masas (MSI) es actualmente la principal técnica para obtener información de imágenes moleculares para la metabolómica espacial. MSI es una tecnología de imágenes moleculares sin marcador que produce espectros de masas que conservan las estructuras espaciales de las muestras de tejido. Esto se logra ionizando pequeñas porciones de una muestra (un píxel) en un ráster definido a través de toda su superficie, lo que da como resultado una colección de imágenes de distribución de iones (registradas como relaciones masa-carga (m/z)) sobre la muestra. Esta tesis tiene como objetivo desarrollar herramientas computacionales para la anotación de picos en MSI y en el diseño de flujos de trabajo para el análisis estadístico y multivariado de datos MSI, incluida la segmentación espacial. El trabajo realizado en esta tesis se puede separar claramente en dos partes. En primer lugar, el desarrollo de una herramienta de anotación de picos de isótopos y aductos adecuada para facilitar la identificación de compuestos de bajo rango de masa. Ahora podemos encontrar fácilmente iones monoisotópicos en nuestros conjuntos de datos MSI gracias al paquete de software rMSIannotation.Spatial metabolomics is the discipline that studies the images of the distributions of low weight chemical compounds (metabolites) on the surface of biological tissues to unveil interactions between molecules. Mass spectrometry imaging (MSI) is currently the principal technique to get molecular imaging information for spatial metabolomics. MSI is a labelfree molecular imaging technology that produces mass spectra preserving the spatial structures of tissue samples. This is achieved by ionizing small portions of a sample (a pixel) in a defined raster through all its surface, which results in a collection of ion distribution images (registered as mass-to-charge ratios (m/z)) over the sample. This thesis is aimed to develop computational tools for peak annotation in MSI and in the design of workflows for the statistical and multivariate analysis of MSI data, including spatial segmentation. The work carried out in this thesis can be clearly separated in two parts. Firstly, the development of an isotope and adduct peak annotation tool suited to facilitate the identification of the low mass range compounds. We can now easily find monoisotopic ions in our MSI datasets thanks to the rMSIannotation software package. Secondly, the development of software tools for data analysis and spatial segmentation based on soft clustering for MSI data. In this thesis, we have developed tools and methodologies to search for significant ions (rMSIKeyIon software package) and for the soft clustering of tissues (Fuzzy c-means algorithm)

Computational approaches for the multimodal imaging of nanomaterials and their biochemical effects

Nanomaterial delivery systems constitute a group of drug delivery vehicles that have been used extensively in biodelivery. The proper characterization of the therapeutic function of these nanomaterials requires analytical methods to track the presence of the cargo and its biochemical effects. In some cases, the detection of the cargo and biochemical changes are not attainable in the same experiment, and more than one technique might be needed for the proper analysis of the drug delivery system. In this case, separate analysis of adjacent tissue sections is performed by techniques that offer complementary information such as MALDI-MS and LA-ICP-MS. However, the approaches to combine the information from these techniques to obtain insights into the mechanism of action of the nanomaterials have been limited to visual inspection and image overlay, which can only provide qualitative information. To advance towards a more quantitative analysis, in this dissertation we have developed computational techniques for image reconstruction, segmentation, and registration of MALDI-MS and LA-ICP-MS images to monitor the biodistribution, excretion and biochemical effects of nanomaterial delivery systems. First, we developed an open-source computational approach for LA-ICP-MS image reconstruction and segmentation using Python, which revealed that nanomaterials distribute in different sub-organ regions based on their chemical and physical properties. For instance, in the analysis of gold nanoparticles and bismuth nanorods, we find that the nanomaterials distribute in different regions of the spleen, suggesting differences in their biochemical interactions within the same organ. Next, we developed a computational workflow in Python to register LA-ICP-MS and MALDI-MS images using image registration approaches, obtaining a method with errors below 50 µm. Finally, we used the developed approaches for registration of LA-ICP-MS and MALDI-MS images to evaluate the delivery vehicles and cargo, obtaining quantitative information about the correlation of the signals obtained in the two image modalities. The use of image registration for the analysis of siRNA delivery via nanoparticle stabilized capsules (NPSC) reveals that expected changes in lipid levels occur at different locations than the NPSC accumulate, thus providing deeper insight into how siRNA delivery by NPSCs influences lipid biochemistry in vivo

Signal and image processing methods for imaging mass spectrometry data

Imaging mass spectrometry (IMS) has evolved as an analytical tool for many biomedical applications. This thesis focuses on algorithms for the analysis of IMS data produced by matrix assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometer. IMS provides mass spectra acquired at a grid of spatial points that can be represented as hyperspectral data or a so-called datacube. Analysis of this large and complex data requires efficient computational methods for matrix factorization and for spatial segmentation. In this thesis, state of the art processing methods are reviewed, compared and improved versions are proposed. Mathematical models for peak shapes are reviewed and evaluated. A simulation model for MALDI-TOF is studied, expanded and developed into a simulator for 2D or 3D MALDI-TOF-IMS data. The simulation approach paves way to statistical evaluation of algorithms for analysis of IMS data by providing a gold standard dataset. [...

Mass spectral imaging of clinical samples using deep learning

A better interpretation of tumour heterogeneity and variability is vital for the improvement of novel diagnostic techniques and personalized cancer treatments. Tumour tissue heterogeneity is characterized by biochemical heterogeneity, which can be investigated by unsupervised metabolomics. Mass Spectrometry Imaging (MSI) combined with Machine Learning techniques have generated increasing interest as analytical and diagnostic tools for the analysis of spatial molecular patterns in tissue samples. Considering the high complexity of data produced by the application of MSI, which can consist of many thousands of spectral peaks, statistical analysis and in particular machine learning and deep learning have been investigated as novel approaches to deduce the relationships between the measured molecular patterns and the local structural and biological properties of the tissues. Machine learning have historically been divided into two main categories: Supervised and Unsupervised learning. In MSI, supervised learning methods may be used to segment tissues into histologically relevant areas e.g. the classification of tissue regions in H&E (Haemotoxylin and Eosin) stained samples. Initial classification by an expert histopathologist, through visual inspection enables the development of univariate or multivariate models, based on tissue regions that have significantly up/down-regulated ions. However, complex data may result in underdetermined models, and alternative methods that can cope with high dimensionality and noisy data are required. Here, we describe, apply, and test a novel diagnostic procedure built using a combination of MSI and deep learning with the objective of delineating and identifying biochemical differences between cancerous and non-cancerous tissue in metastatic liver cancer and epithelial ovarian cancer. The workflow investigates the robustness of single (1D) to multidimensional (3D) tumour analyses and also highlights possible biomarkers which are not accessible from classical visual analysis of the H&E images. The identification of key molecular markers may provide a deeper understanding of tumour heterogeneity and potential targets for intervention.Open Acces

MSI-based mapping strategies in tumour-heterogeneity

Since the early 2000s, considerable innovations in MS technology and associated gene sequencing systems have enabled the "-omics" revolution. The data collected from multiple omics research can be combined to gain a better understanding of cancer's biological activity. Breast and ovarian cancer are among the most common cancers worldwide in women. Despite significant advances in diagnosis, treatment, and subtype identification, breast cancer remains the world's second leading cause of cancer-related deaths in women, with ovarian cancer ranking fifth. Tumour heterogeneity is a significant hurdle in cancer patient prognosis, response to therapy, and metastasis. As such, heterogeneity is one of the most significant and clinically relevant areas of cancer research nowadays. Metabolic reprogramming is a hallmark of malignancy that has been widely acknowledged in recent literature. Metabolic heterogeneity in tumours poses a challenge in developing therapies that exploit metabolic vulnerabilities. Consequently, it is crucial to approach tumour heterogeneity with an unlabeled yet spatially specific read-out of metabolic and genetic information. The advantage of DESI-MSI technology originates from its untargeted nature, which allows for the investigation of thousands of component distributions, at a micrometre scale, in a single experiment. Most notably, using a DESI-MSI clustering approach could potentially offer novel insights into metabolism, providing a method to characterise metabolically distinct sub-regions and subsequently delineate the underlying genetic drivers through genomic analyses. Hence, in this study, we aim to map the inter-and intra-tumour metabolic heterogeneity in breast and ovarian cancer by integrating multimodal MSI-based mapping strategies, comprising DESI and MALDI, with IMC (Imaging Mass Cytometry) analysis of the tumour section, using CyTOF, and high- throughput genetic characterisation of metabolically-distinct regions by transcriptomics. The multimodal analysis workflow was initially performed using sequential breast cancer Patient-Derived Xenografts (PDX) models and was expanded on primary tumour sections. Moreover, a newly developed DESI-MSI friendly, hydroxypropyl-methylcellulose and polyvinylpyrrolidone (HPMC/PVP) hydrogel-based embedding was successfully established to allow simultaneous preparation and analysis of numerous fresh frozen core-size biopsies in the same Tissue Microarray (TMA) block for the investigation of tumour heterogeneity. Additionally, a single section strategy was combined with DESI-MSI coupled to Laser Capture Microdissection (LCM) application to integrate gene expression analysis and Liquid Chromatography-Mass Spectrometry (LC-MS) on the same tissue segment. The developed single section methodology was then tested with multi-region collected ovarian tumours. DESI-MSI-guided spatial transcriptomics was performed for co-registration of different omics datasets on the same regions of interest (ROIs). This co-registration of various omics could unravel possible interactions between distinct metabolic profiles and specific genetic drivers that can lead to intra-tumour heterogeneity. Linking all these findings from MSI-based or guided various strategies allows for a transition from a qualitative approach to a conceptual understanding of the architecture of multiple molecular networks responsible for cellular metabolism in tumour heterogeneity.Open Acces

Improvements in MALDI-Imaging Mass Spectrometry to analyze the lipidome in different tissues. A step forward to clinical application.

305 p.A pesar de los grandes avances de la imagen por espectrometría de masas (IMS), esta técnica aún requiere superar algunas limitaciones antes de despegar como una tecnología potente para su uso en clínica. El objetivo de esta tesis es mejorar algunos aspectos involucrados en el flujo de trabajo MALDI-IMS y enfocar su implementación para resolver cuestiones biológicas relacionadas con la lipidómica. Así, en el Capítulo 4 implantaremos ciertas mejoras en la preparativa de la muestra, así como en el set up óptico del espectrómetro de masas utilizado. El Capítulo 5 presenta la caracterización de las diferencias en el perfil lipídico entre el riñón normal de ratón, un modelo de lesión renal aguda (AKI) y el riñón AKI tratado con ferrostatina (Fer-1). En el Capítulo 6 se muestra la primera caracterización de los diferentes segmentos de la nefrona humana mediante MALDI-IMS de lípidos combinado con protocolos de inmunofluorescencia. Una vez caracterizado el riñón normal murino y humano, en el Capítulo 7 se realizó una comparación de los perfiles lipídicos obtenidos de estas dos especies con el fin de evaluar la calidad del ratón como un buen modelo animal para enfermedades humanas. El Capítulo 8 se centra en el estudio del lipidoma del cáncer renal de células claras (ccRCC) mediante MALDI-IMS y uHPLC y, por último, en el Capítulo 9, se examinará el lipidoma del Glioblastoma además del efecto del agente quimioterapéutico temozolomide (TMZ) sobre tejido cerebral humano

To salt or not to salt : three MALDI-TOF IMS protocols where (de)salting proved essential

Présentement, la désorption ionisation laser assistée par la matrice (MALDI) est la méthode d’ionisation préférentielle pour étudier les lipides par l’imagerie par spectrométrie de masse (IMS). Bien qu’il existe les matrices spécifiques aux lipides, tel que la 1,5-DAN pour les phospholipides et la 2,5-DHB pour les triacylglycérols, il est toujours nécessaire d’augmenter la sensibilité de cette technique pour les échantillons atypiques ou certaines classes de lipides. Dans la première étude, nous avons amélioré la sensitivité pour les phospholipides sur les tubes de Malpighi de mouches prélevés par microdissection dans un tampon physiologique à base de sodium et potassium. Un protocole de lavage à deux étapes a était trouvé favorable : un premier rinçage dans le glycérol suivi d’un second rinçage dans l’acétate d’ammonium. Ce protocole permet de réduire au maximum la présence de sels sans délocalisation notoire des phospholipides. La détection et l’imagerie des phospholipides en ionisation négative et positive ont suggéré une distribution uniforme sur toute la longueur des tubes. Ces résultats ont été comparés à ceux obtenus sur des sections tissulaires minces de mouche entière acquis avec les deux polarités. Néanmoins, la structure tridimensionnelle complexe des tubes rénaux suggère que la microdissection est l’approche la plus favorable pour en étudier leur lipidome. Dans la deuxième étude, nous avons déterminé que l’addition de formate d’ammonium (AF) peut améliorer la détection des gangliosides par IMS dans le cerveau. Curieusement, il est nécessaire de rincer l’échantillon dans une solution d’AF avant l’addition de ce même sel suivit d’une conservation de l’échantillon dans un congélateur pendant 24 heures après la déposition de la matrice afin d’obtenir la meilleure augmentation de sensibilité. En moyenne, cette approche a permis d’augmenter l’intensité d’un facteur dix avec trois fois plus d’espèces de gangliosides détectées. De plus, malgré l’étape de lavage, nous n’avons pas observé la délocalisation des gangliosides puisqu’il est toujours possible d’obtenir les résultats d’IMS de qualité avec une résolution spatiale de 20 µm. Finalement, nous avons établi que le nitrate d’argent permet l’analyse des oléfines par IMS, en particulier du cholestérol. En optimisant le protocole de déposition par nébulisation, il est possible de générer une couche mince et homogène de nitrate d’argent ce qui rend la possibilité d’effectuer l’IMS à haute résolution spatiale, jusqu’à 10 µm, sans perte de qualité comparativement aux autres approches publiées. L’ensemble de ce travail démontre l’effet du sel sur la sélectivité et la sensibilité pour cibler les familles de lipides désirées, ce qui nécessite les études ultérieures sur le rôle de ces sels lors du processus de la désorption-ionisation.Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is currently the ionization method of choice for elucidating the spatial distribution of lipids on thin tissue sections. Despite the discovery of lipid friendly matrices such as 1,5-DAN for phospholipids and 2,5-DHB for triacylglycerols, there is a continued need to improve sensitivity. In the first study, we improved the overall sensitivity for phospholipids of entire fly Malpighian tubules microdissected in PBS with a two-step wash in glycerol followed by ammonium acetate that removed the bulk of the salt with minimal species delocalization and tubule displacement. We were able to detect phospholipids in both positive and negative ion modes and revealed an even distribution of most phospholipids along the length of this organ. We compared the method to the results from whole body fly sections acquired in dual-polarity mode at the same spatial resolution and found it to be more suitable for studying the tubules because of the complex three-dimensional structure of this organ within the fly. In the second study, we observed a marked improvement in ganglioside signals on mouse brain tissue sections with ammonium salt addition. Specifically, when the sample was first desalted in a low concentration ammonium formate solution, spray-coated with the same salt, coated with matrix and finally left in the freezer overnight before data acquisition, we observed an average overall improvement in ganglioside signal intensity by ten-fold and the number of species detected by three-fold. This method also did not affect the spatial distribution of the gangliosides, as high spatial resolution IMS results acquired at 20 µm showed no species delocalization. Finally, we sought to determine if salts could be employed directly as matrices. In this work, we tested silver-based metal salts and discovered that spray depositing silver nitrate alone is a viable method for the IMS detection of olefins, particularly cholesterol. With the optimized dry spray parameter, the overall deposition is homogeneous and composed of microscopic salt crystals that allow for high spatial resolution IMS down to 10 µm while maintaining acceptable overall signal quality comparable to that of previously published protocols. Overall, this thesis demonstrates we can manipulate the local salt distribution to influence the sensitivity and selectivity to target specific lipid subfamilies, opening the door for future research to understanding the role salts play during the laser desorption/ionization process

Anwendung Massenspektrometrie basierter Technologie zur Entdeckung räumlicher Peptidsignaturen in der Krebsforschung

Cancer is one of the leading causes of death worldwide, within the molecular and structure complexity of tumors are causal factors for disease progression and treatment standards. With the development of molecular biological techniques, physicians could use genetic variation or protein and metabolic expression profile besides histo-morphologicial evaluation to classify more accurate risk assessment and to guide treatment decisions. The biomarker-driven personalized therapies might improve clinical care, avoid unnecessary treatments and reduce the duration and costs for hospital stay. Therefore, there is a strong demand for more reliable molecular biomarker profiles. In this dissertation, a novel technique called imaging mass spectrometry (MADLI-MSI) is used to investigate the potential of spatially resolved peptide signatures (directly from tumor tissue; in situ) for (i) discrimination of subtypes of serous ovarian cancer (HGSOC) and (ii) risk assessment of neuroblastoma. Univariate and multivariate static methods were used to determine associated peptide signatures. Using complementary methods, liquid chromatography-based mass spectrometry the corresponding proteins to the peptides were identified and verified by immunohistology. Consequently, peptide signatures were identified to predict disease recurrence in early-stage HGSOC patients and to distinguish high-risk neuroblastoma patients from other risk groups. These results suggest that the MALDI-MSI technique is a promising analytical method that facilitates diagnosis and treatment decision-making. It has also provided new biological insights into tumor heterogeneity, that could benefit the development of molecular biomarker profiles. The data of this dissertation have been really published in Journal “Cancers (MDPI)” 2020 and 2021.Onkologische Erkrankungen (Krebs) sind weltweit eine der häufigsten Todesursachen. Die molekulare und strukturelle Komplexität von Tumoren sind ursächlich für die Krankheitsprogression und Therapieanspruch. Mit der Entwicklung von neuen molekularbiologischen Verfahren könnten Ärzte neben der histo-morphologischen Bewertung auch genetische Variationen oder Protein- und Metabolit-Expressionsprofile nutzen, um eine genauere Risikobewertung vorzunehmen und die Behandlungsentscheidung zu treffen. Die personalisierten Therapien können die klinische Versorgung verbessern durch Vermeidung unnötiger Behandlungen und verringerte Dauer und Kosten des Krankenhausaufenthalts. Daher besteht ein starker Bedarf an zuverlässigeren molekularen Biomarker Profilen. In dieser Dissertation wird ein neuartiges Verfahren, die sogenannten bildgebenden Massenspektrometrie (MADLI-MSI) eingesetzte um das Potential von räumlich aufgelösten Peptide-Signaturen (direkt aus dem Tumorgewebe; in situ) für (i) die Diskriminierung von Subtypen des serösen Ovarialkarzinom (HGSOC) zu untersuchen und (ii) die Risikoabschätzung des Neuroblastomes. Dabei wurden univariate und multivariate statischer Verfahren eingesetzt, um assoziierten Peptide- Signaturen zu bestimmen. Mittels komplementärer Verfahren, Flüssigkeitschromatographie basierte Massenspektrometrie wurden die korrespondierenden Proteine zu den Peptiden identifiziert und Immunhistologisch verifiziert. Folglich wurden Peptidsignaturen zur Vorhersage des Wiederauftretens der Krankheit bei HGSOC-Patienten im Frühstadium und zur Unterscheidung von Hochrisiko-Neuroblastom Patienten von anderen Risikogruppen identifiziert. Diese Ergebnisse deuten darauf hin, dass die MALDI-MSI-Technik eine vielversprechende Analysemethode ist, die die Diagnose und die Entscheidung über die Behandlung erleichtert. Außerdem hat sie neue biologische Erkenntnisse über die Heterogenität des Tumors geliefert, die der Entwicklung von molekularen Biomarker-Profilen zu Gute kommen könnten. Die Daten dieser Dissertation wurden in der Zeitschrift „Cancers (MDPI)" 2020 und 2021 veröffentlicht

Development of a complete advanced computational workflow for high-resolution LDI-MS metabolomics imaging data processing and visualization

La imatge per espectrometria de masses (MSI) mapeja la distribució espacial de les molècules en una mostra. Això permet extreure informació Metabolòmica espacialment corralada d'una secció de teixit. MSI no s'usa àmpliament en la metabolòmica espacial a causa de diverses limitacions relacionades amb les matrius MALDI, incloent la generació d'ions que interfereixen en el rang de masses més baix i la difusió lateral dels compostos. Hem desenvolupat un flux de treball que millora l'adquisició de metabòlits en un instrument MALDI utilitzant un "sputtering" per dipositar una nano-capa d'Au directament sobre el teixit. Això minimitza la interferència dels senyals del "background" alhora que permet resolucions espacials molt altes. S'ha desenvolupat un paquet R per a la visualització d'imatges i processament de les dades MSI, tot això mitjançant una implementació optimitzada per a la gestió de la memòria i la programació concurrent. A més, el programari desenvolupat inclou també un algoritme per a l'alineament de masses que millora la precisió de massa.La imagen por espectrometría de masas (MSI) mapea la distribución espacial de las moléculas en una muestra. Esto permite extraer información metabolòmica espacialmente corralada de una sección de tejido. MSI no se usa ampliamente en la metabolòmica espacial debido a varias limitaciones relacionadas con las matrices MALDI, incluyendo la generación de iones que interfieren en el rango de masas más bajo y la difusión lateral de los compuestos. Hemos desarrollado un flujo de trabajo que mejora la adquisición de metabolitos en un instrumento MALDI utilizando un “sputtering” para depositar una nano-capa de Au directamente sobre el tejido. Esto minimiza la interferencia de las señales del “background” a la vez que permite resoluciones espaciales muy altas. Se ha desarrollado un paquete R para la visualización de imágenes y procesado de los datos MSI, todo ello mediante una implementación optimizada para la gestión de la memoria y la programación concurrente. Además, el software desarrollado incluye también un algoritmo para el alineamiento de masas que mejora la precisión de masa.Mass spectrometry imaging (MSI) maps the spatial distributions of molecules in a sample. This allows extracting spatially-correlated metabolomics information from tissue sections. MSI is not widely used in spatial metabolomics due to several limitations related with MALDI matrices, including the generation of interfering ions and in the low mass range and the lateral compound delocalization. We developed a workflow to improve the acquisition of metabolites using a MALDI instrument. We sputter an Au nano-layer directly onto the tissue section enabling the acquisition of metabolites with minimal interference of background signals and ultra-high spatial resolution. We developed an R package for image visualization and MSI data processing, which is optimized to manage datasets larger than computer’s memory using a mutli-threaded implementation. Moreover, our software includes a label-free mass alignment algorithm for mass accuracy enhancement