Development of Imaging Mass Spectrometry Analysis of Lipids in Biological and Clinically Relevant Applications

La spectrométrie de masse mesure la masse des ions selon leur rapport masse sur charge. Cette technique est employée dans plusieurs domaines et peut analyser des mélanges complexes. L’imagerie par spectrométrie de masse (Imaging Mass Spectrometry en anglais, IMS), une branche de la spectrométrie de masse, permet l’analyse des ions sur une surface, tout en conservant l’organisation spatiale des ions détectés. Jusqu’à présent, les échantillons les plus étudiés en IMS sont des sections tissulaires végétales ou animales. Parmi les molécules couramment analysées par l’IMS, les lipides ont suscité beaucoup d'intérêt. Les lipides sont impliqués dans les maladies et le fonctionnement normal des cellules; ils forment la membrane cellulaire et ont plusieurs rôles, comme celui de réguler des événements cellulaires. Considérant l’implication des lipides dans la biologie et la capacité du MALDI IMS à les analyser, nous avons développé des stratégies analytiques pour la manipulation des échantillons et l’analyse de larges ensembles de données lipidiques. La dégradation des lipides est très importante dans l’industrie alimentaire. De la même façon, les lipides des sections tissulaires risquent de se dégrader. Leurs produits de dégradation peuvent donc introduire des artefacts dans l’analyse IMS ainsi que la perte d’espèces lipidiques pouvant nuire à la précision des mesures d’abondance. Puisque les lipides oxydés sont aussi des médiateurs importants dans le développement de plusieurs maladies, leur réelle préservation devient donc critique. Dans les études multi-institutionnelles où les échantillons sont souvent transportés d’un emplacement à l’autre, des protocoles adaptés et validés, et des mesures de dégradation sont nécessaires. Nos principaux résultats sont les suivants : un accroissement en fonction du temps des phospholipides oxydés et des lysophospholipides dans des conditions ambiantes, une diminution de la présence des lipides ayant des acides gras insaturés et un effet inhibitoire sur ses phénomènes de la conservation des sections au froid sous N2. A température et atmosphère ambiantes, les phospholipides sont oxydés sur une échelle de temps typique d’une préparation IMS normale (~30 minutes). Les phospholipides sont aussi décomposés en lysophospholipides sur une échelle de temps de plusieurs jours. La validation d’une méthode de manipulation d’échantillon est d’autant plus importante lorsqu’il s’agit d’analyser un plus grand nombre d’échantillons. L’athérosclérose est une maladie cardiovasculaire induite par l’accumulation de matériel cellulaire sur la paroi artérielle. Puisque l’athérosclérose est un phénomène en trois dimension (3D), l'IMS 3D en série devient donc utile, d'une part, car elle a la capacité à localiser les molécules sur la longueur totale d’une plaque athéromateuse et, d'autre part, car elle peut identifier des mécanismes moléculaires du développement ou de la rupture des plaques. l'IMS 3D en série fait face à certains défis spécifiques, dont beaucoup se rapportent simplement à la reconstruction en 3D et à l’interprétation de la reconstruction moléculaire en temps réel. En tenant compte de ces objectifs et en utilisant l’IMS des lipides pour l’étude des plaques d’athérosclérose d’une carotide humaine et d’un modèle murin d’athérosclérose, nous avons élaboré des méthodes «open-source» pour la reconstruction des données de l’IMS en 3D. Notre méthodologie fournit un moyen d’obtenir des visualisations de haute qualité et démontre une stratégie pour l’interprétation rapide des données de l’IMS 3D par la segmentation multivariée. L’analyse d’aortes d’un modèle murin a été le point de départ pour le développement des méthodes car ce sont des échantillons mieux contrôlés. En corrélant les données acquises en mode d’ionisation positive et négative, l’IMS en 3D a permis de démontrer une accumulation des phospholipides dans les sinus aortiques. De plus, l’IMS par AgLDI a mis en évidence une localisation différentielle des acides gras libres, du cholestérol, des esters du cholestérol et des triglycérides. La segmentation multivariée des signaux lipidiques suite à l’analyse par IMS d’une carotide humaine démontre une histologie moléculaire corrélée avec le degré de sténose de l’artère. Ces recherches aident à mieux comprendre la complexité biologique de l’athérosclérose et peuvent possiblement prédire le développement de certains cas cliniques. La métastase au foie du cancer colorectal (Colorectal cancer liver metastasis en anglais, CRCLM) est la maladie métastatique du cancer colorectal primaire, un des cancers le plus fréquent au monde. L’évaluation et le pronostic des tumeurs CRCLM sont effectués avec l’histopathologie avec une marge d’erreur. Nous avons utilisé l’IMS des lipides pour identifier les compartiments histologiques du CRCLM et extraire leurs signatures lipidiques. En exploitant ces signatures moléculaires, nous avons pu déterminer un score histopathologique quantitatif et objectif et qui corrèle avec le pronostic. De plus, par la dissection des signatures lipidiques, nous avons identifié des espèces lipidiques individuelles qui sont discriminants des différentes histologies du CRCLM et qui peuvent potentiellement être utilisées comme des biomarqueurs pour la détermination de la réponse à la thérapie. Plus spécifiquement, nous avons trouvé une série de plasmalogènes et sphingolipides qui permettent de distinguer deux différents types de nécrose (infarct-like necrosis et usual necrosis en anglais, ILN et UN, respectivement). L’ILN est associé avec la réponse aux traitements chimiothérapiques, alors que l’UN est associé au fonctionnement normal de la tumeur.Mass spectrometry is the measurement of the mass over charge ratio of ions. It is broadly applicable and capable of analyzing complex mixtures. Imaging mass spectrometry (IMS) is a branch of mass spectrometry that analyses ions across a surface while conserving their spatial organization on said surface. At this juncture, the most studied IMS samples are thin tissue sections from plants and animals. Among the molecules routinely imaged by IMS, lipids have generated significant interest. Lipids are important in disease and normal cell function as they form cell membranes and act as signaling molecules for cellular events among many other roles. Considering the potential of lipids in biological and clinical applications and the capability of MALDI to ionize lipids, we developed analytical strategies for the handling of samples and analysis of large lipid MALDI IMS datasets. Lipid degradation is massively important in the food industry with oxidized products producing a bad smell and taste. Similarly, lipids in thin tissue sections cut from whole tissues are subject to degradation, and their degradation products can introduce IMS artifacts and the loss of normally occurring species to degradation can skew accuracy in IMS measures of abundance. Oxidized lipids are also known to be important mediators in the progression of several diseases and their accurate preservation is critical. As IMS studies become multi-institutional and collaborations lead to sample exchange, the need for validated protocols and measures of degradation are necessary. We observed the products of lipid degradation in tissue sections from multiple mouse organs and reported on the conditions promoting and inhibiting their presence as well as the timeline of degradation. Our key findings were the increase in oxidized phospholipids and lysophospholipids from degradation at ambient conditions, the decrease in the presence of lipids containing unsaturations on their fatty acyl chains, and the inhibition of degradation by matrix coating and cold storage of sections under N2 atmosphere. At ambient atmospheric and temperature, lipids degraded into oxidized phospholipids on the time-scale of a normal IMS experiment sample preparation (within 30 min). Lipids then degraded into lysophospholipids’ on a time scale on the order of several days. Validation of sample handling is especially important when a greater number of samples are to be analyzed either through a cohort of samples, or analysis of multiple sections from a single tissue as in serial 3D IMS. Atherosclerosis is disease caused by accumulation of cellular material at the arterial wall. The accumulation implanted in the cell wall grows and eventually occludes the blood vessel, or causes a stroke. Atherosclerosis is a 3D phenomenon and serial 3D IMS is useful for its ability to localize molecules throughout the length of a plaque and help to define the molecular mechanisms of plaque development and rupture. Serial 3D IMS has many challenges, many of which are simply a matter of producing 3D reconstructions and interpreting them in a timely fashion. In this aim and using analysis of lipids from atherosclerotic plaques from a human carotid and mouse aortic sinuses, we described 3D reconstruction methods using open-source software. Our methodology provides means to obtain high quality visualizations and demonstrates strategies for rapid interpretation of 3D IMS datasets through multivariate segmentation. Mouse aorta from model animals provided a springboard for developing the methods on lower risk samples with less variation with interesting molecular results. 3D MALDI IMS showed localized phospholipid accumulation in the mouse aortic sinuses with correlation between separate positive and negative ionization datasets. Silver-assisted LDI imaging presented differential localization of free fatty acids, cholesterol / cholesterol esters, and triglycerides. The human carotid’s 3D segmentation shows molecular histologies (spatial groupings of imaging pixels with similar spectral fingerprints) correlating to the degree of arterial stenosis. Our results outline the potential for 3D IMS in atherosclerotic research. Molecular histologies and their 3D spatial organization, obtained from the IMS techniques used herein, may predict high-risk features, and particularly identify areas of plaque that have higher-risk of rupture. These investigations would help further unravel the biological complexities of atherosclerosis, and predict clinical outcomes. Colorectal cancer liver metastasis (CRCLM) is the metastatic disease of primary colorectal cancer, one of the most common cancers worldwide. CRC is a cancer of the endothelial lining of the colon or rectum. CRC itself is often cured with surgery, while CRCLM is more deadly and treated with chemotherapy with more limited efficacy. Prognosticating and assessment of tumors is performed using classical histopathology with a margin of error. We have used lipid IMS to identify the histological compartments and extract their signatures. Using these IMS signatures we obtained a quantitative and objective histopathological score that correlates with prognosis. Additionally, by dissecting out the lipid signatures we have identified single lipid moieties that are unique to different histologies that could potentially be used as new biomarkers for assessing response to therapy. Particularly, we found a series of plasmalogen and sphingolipid species that differentiate infarct-like and usual necrosis, typical of chemotherapeutic response and normal tumor function, respectively

Lysolipids are prominent in subretinal drusenoid deposits, a high-risk phenotype in age-related macular degeneration

IntroductionAge related macular degeneration (AMD) causes legal blindness worldwide, with few therapeutic targets in early disease and no treatments for 80% of cases. Extracellular deposits, including drusen and subretinal drusenoid deposits (SDD; also called reticular pseudodrusen), disrupt cone and rod photoreceptor functions and strongly confer risk for advanced disease. Due to the differential cholesterol composition of drusen and SDD, lipid transfer and cycling between photoreceptors and support cells are candidate dysregulated pathways leading to deposit formation. The current study explores this hypothesis through a comprehensive lipid compositional analysis of SDD.MethodsHistology and transmission electron microscopy were used to characterize the morphology of SDD. Highly sensitive tools of imaging mass spectrometry (IMS) and nano liquid chromatography tandem mass spectrometry (nLC-MS/MS) in positive and negative ion modes were used to spatially map and identify SDD lipids, respectively. An interpretable supervised machine learning approach was utilized to compare the lipid composition of SDD to regions of uninvolved retina across 1873 IMS features and to automatically discern candidate markers for SDD. Immunohistochemistry (IHC) was used to localize secretory phospholipase A2 group 5 (PLA2G5). ResultsAmong the 1873 detected features in IMS data, three lipid classes, including lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE) and lysophosphatidic acid (LysoPA) were observed nearly exclusively in SDD while presumed precursors, including phosphatidylcholine (PC), phosphatidylethanolamine (PE) and phosphatidic acid (PA) lipids were detected in SDD and adjacent photoreceptor outer segments. Molecular signals specific to SDD were found in central retina and elsewhere. IHC results indicated abundant PLA2G5 in photoreceptors and retinal pigment epithelium (RPE). DiscussionThe abundance of lysolipids in SDD implicates lipid remodeling or degradation in deposit formation, consistent with ultrastructural evidence of electron dense lipid-containing structures distinct from photoreceptor outer segment disks and immunolocalization of secretory PLA2G5 in photoreceptors and RPE. Further studies are required to understand the role of lipid signals observed in and around SDD

Chem Phys Lipids

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has emerged as a novel powerful MS methodology that has the ability to generate both molecular and spatial information within a tissue section. Application of this technology as a new type of biochemical lipid microscopy may lead to new discoveries of the lipid metabolism and biomarkers associated with area-specific alterations or damage under stress/disease conditions such as traumatic brain injury or acute lung injury, among others. However there are limitations in the range of what it can detect as compared with liquid chromatography-MS (LC-MS) of a lipid extract from a tissue section. The goal of the current work was to critically consider remarkable new opportunities along with the limitations and approaches for further improvements of MALDI-MSI. Based on our experimental data and assessments, improvements of the spectral and spatial resolution, sensitivity and specificity towards low abundance species of lipids are proposed. This is followed by a review of the current literature, including methodologies that other laboratories have used to overcome these challenges.ES020693/ES/NIEHS NIH HHS/United StatesES021068/ES/NIEHS NIH HHS/United StatesHL094488/HL/NHLBI NIH HHS/United StatesHL70755/HL/NHLBI NIH HHS/United StatesNS076511/NS/NINDS NIH HHS/United StatesOH008282/OH/NIOSH CDC HHS/United StatesP30 CA047904/CA/NCI NIH HHS/United StatesP30 CA047904/CA/NCI NIH HHS/United StatesP50 NS030318/NS/NINDS NIH HHS/United StatesR01 ES020693/ES/NIEHS NIH HHS/United StatesR01 HL070755/HL/NHLBI NIH HHS/United StatesR01 HL094488/HL/NHLBI NIH HHS/United StatesR01 NS061817/NS/NINDS NIH HHS/United StatesR01 NS076511/NS/NINDS NIH HHS/United StatesR21 ES021068/ES/NIEHS NIH HHS/United StatesR37 HL065697/HL/NHLBI NIH HHS/United StatesU19 AI068021/AI/NIAID NIH HHS/United StatesU19AI068021/AI/NIAID NIH HHS/United States2013-07-01T00:00:00Z22692104PMC364277

On-Tissue Chemical Derivatization in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) combines molecular and spatial information in a valuable tool for a wide range of applications. Matrix‐assisted laser desorption/ionization (MALDI) is at the forefront of MSI ionization due to its wide availability and increasing improvement in spatial resolution and analysis speed. However, ionization suppression, low concentrations, and endogenous and methodological interferences cause visualization problems for certain molecules. Chemical derivatization (CD) has proven a viable solution to these issues when applied in mass spectrometry platforms. Chemical tagging of target analytes with larger, precharged moieties aids ionization efficiency and removes analytes from areas of potential isobaric interferences. Here, we address the application of CD on tissue samples for MSI analysis, termed on‐tissue chemical derivatization (OTCD). MALDI MSI will remain the focus platform due to its popularity, however, alternative ionization techniques such as liquid extraction surface analysis and desorption electrospray ionization will also be recognized. OTCD reagent selection, application, and optimization methods will be discussed in detail. MSI with OTCD is a powerful tool to study the spatial distribution of poorly ionizable molecules within tissues. Most importantly, the use of OTCD−MSI facilitates the analysis of previously inaccessible biologically relevant molecules through the adaptation of existing CD methods. Though further experimental optimization steps are necessary, the benefits of this technique are extensive

Enhanced separation in ambient mass spectrometry imaging:towards quantification of pharmaceuticals

In the pharmaceutical industry, the development and application of good separation methods is important to study the distribution and the effect of a drug candidate. Mass spectrometry imaging is a technique that is often applied to study the distribution of a drug candidate. This technique is not always able to separate the drug candidate from other molecules that are present in the sample. Therefore, we need better separation techniques in addition to mass spectrometry imaging. This PhD research investigates novel technological developments as possible tools for the pharmaceutical industry to use. The techniques presented in this thesis allow for better separation of molecules that are structurally alike. Because these techniques separate the drug candidate better from other molecules in the sample, their addition to mass spectrometry imaging is used for quantification of two drug candidates in the last chapter of this thesis

Mass spectrometry imaging with high resolution in mass and space

Mass spectrometry (MS) imaging links molecular information and the spatial distribution of analytes within a sample. In contrast to most histochemical techniques, mass spectrometry imaging can differentiate molecular modifications and does not require labeling of targeted compounds. We have recently introduced the first mass spectrometry imaging method that provides highly specific molecular information (high resolution and accuracy in mass) at cellular dimensions (high resolution in space). This method is based on a matrix-assisted laser desorption/ionization (MALDI) imaging source working at atmospheric pressure which is coupled to an orbital trapping mass spectrometer. Here, we present a number of application examples and demonstrate the benefit of ‘mass spectrometry imaging with high resolution in mass and space.’ Phospholipids, peptides and drug compounds were imaged in a number of tissue samples at a spatial resolution of 5–10 μm. Proteins were analyzed after on-tissue tryptic digestion at 50-μm resolution. Additional applications include the analysis of single cells and of human lung carcinoma tissue as well as the first MALDI imaging measurement of tissue at 3 μm pixel size. MS image analysis for all these experiments showed excellent correlation with histological staining evaluation. The high mass resolution (R = 30,000) and mass accuracy (typically 1 ppm) proved to be essential for specific image generation and reliable identification of analytes in tissue samples. The ability to combine the required high-quality mass analysis with spatial resolution in the range of single cells is a unique feature of our method. With that, it has the potential to supplement classical histochemical protocols and to provide new insights about molecular processes on the cellular level

Using Novel Data Analysis Methods to Extract Information from Mass Spectrometry Imaging and Single Cell Mass Spectrometry Results

Mass spectrometry (MS) is a powerful tool for qualitative and quantitative biological sample analysis, with its high sensitivity, broad applicability, and strong robustness. While insights into ‘what’ and ‘how’ can be provided by mass spectrometry, MS technique coupled with traditional separation methods, such as liquid chromatography LC-MS, do not provide a satisfactory key to the question ‘where’ with high enough resolution. Accordingly, two different types of MS methods are being developed to analyze sample species with spatial resolution, one being single cell mass spectrometry (SCMS), and the other as mass spectrometry imaging (MSI). In general, SCMS provides chemical insight of individual cells, whereas MSI can reveal the spatial distributions of chemical substances at a micrometer resolution. With the in-house microscale sampling device developed in the Yang group, the Single-probe, SCMS and MSI could be conducted. Both methods require specific protocols for sample treatment, experiment operation, data acquisition, and data analysis. Both SCMS and MSI studies are focused on analysis of small molecules (e.g., metabolites, lipids, and drug compounds). Different from SCMS studies, MSI measurements allow for acquiring spatial information on top of the chemical information provided by MS, and the spatial information has brought more complexity in the output data which require more advanced data analysis tools. In this dissertation, the background of spatially resolved MS methods, i.e., MSI techniques, are first introduced, followed by a summary of previously published studies on quantitative SCMS metabolomics projects. In Chapter 3, MSI attempts on three different types of samples, including mice retina, patient breast, and co-cultured cancer cell spheroids, are introduced. In Chapter 4, the metabolomic profile changes of heart tissues upon Trypanosoma Cruzi infection have been investigated with the Single-probe MSI technique. The compatibility between MS and two commonly adopted strategies, fixation and staining, is studied, suggesting that X-gal staining has significantly altered the chemical profile. In Chapter 5, to handle SCMS data with better efficiency and higher mass accuracy, a Python-based MS data pretreatment platform with an easy-to-use graphical user interface (GUI) and an innovative peak alignment algorithm is developed to be compatible with improvised SCMS experiments. In Chapter 6, advanced statistical methods used for MS data processing are discussed in the context of MSI study of mice brain with Alzheimer's Disease as an example. The fusion of ion images from MSI and fluorescence images from immunohistology staining has improved the spatial resolution to a higher level, leading to more precise mapping of chemical substances and more findings involved in Alzheimer’s Disease development

Information processing for mass spectrometry imaging

Mass Spectrometry Imaging (MSI) is a sensitive analytical tool for detecting and spatially localising thousands of ions generated across intact tissue samples. The datasets produced by MSI are large both in the number of measurements collected and the total data volume, which effectively prohibits manual analysis and interpretation. However, these datasets can provide insights into tissue composition and variation, and can help identify markers of health and disease, so the development of computational methods are required to aid their interpretation. To address the challenges of high dimensional data, randomised methods were explored for making data analysis tractable and were found to provide a powerful set of tools for applying automated analysis to MSI datasets. Random projections provided over 90% dimensionality reduction of MALDI MSI datasets, making them amenable to visualisation by image segmentation. Randomised basis construction was investigated for dimensionality reduction and data compression. Automated data analysis was developed that could be applied data compressed to 1% of its original size, including segmentation and factorisation, providing a direct route to the analysis and interpretation of MSI datasets. Evaluation of these methods alongside established dimensionality reduction pipelines on simulated and real-world datasets showed they could reproducibly extract the chemo-spatial patterns present

Development of On-Tissue Mass Spectrometric Strategies for Protein Identification, Quantification and Mapping

Résumé : L’imagerie par spectrométrie de masse est une technique sans marquage permettant la détection et la localisation de protéines à partir de coupes de tissus. Afin de répondre à des problématiques biologiques, le nombre de protéines identifiées doit être amélioré. Une stratégie consiste à réaliser une micro-jonction liquide sur des régions particulières des coupes de tissus afin d’extraire les peptides issus de la digestion in situ des protéines. Plus de 1500 protéines ont identifié sur une zone de 650µm, correspondant à environ 1900 cellules. Une corrélation entre ces données avec celles générées par MSI a augmenté le nombre de protéines localisées. Afin d’obtenir dans le même temps, la localisation et l’identification de protéines, une méthode consiste à réaliser la microdissection de l’ensemble de la coupe après l’avoir déposée sur une lame recouverte de prafilm. Parafilm-Assisted Microdissection (PAM) a également été appliquée à l’étude de l'expression différentielle de protéines dans des tumeurs de prostate. Les résultats identifiés glutamate oxaloacétate transférase 2 (GOT2) en tant que biomarqueur de protéine candidate impliquée dans le métabolisme du glucose, en plus de celles qui ont déjà été indiqué précédemment. Réunis ensemble, ces méthodes MS d'analyses directes fournissent un moyen robuste d’étude de protéines dans leur état natif afin de fournir des indications sur leur rôle dans des systèmes biologiques. // Abstract : Mass spectrometry-based methods for direct tissue analysis, such as MS imaging, are label-free techniques that permit the detection and localization of proteins on tissue sections. There is a need to improve the number of protein identifications in these techniques for them to comprehensively address biological questions. One strategy to obtain high protein IDs is to realize liquid microjunction on localized regions of tissue sections to extract peptides from the in situ digestion of proteins. More than 1500 proteins were identified in a 650μm spot, corresponding to about 1900 cells. Matching these IDs with those from MSI increased the number of localized proteins. In order to achieve simultaneous identification and localization of proteins, a method consisting of microdissecting entire tissue sections mounted on parafilmcovered slides was developed. Spectral counting was then used to quantify identified proteins, and the values were used to generate images. Parafilm-Assisted Microdissection (PAM) was also used to examine the differential expression of proteins on prostate tumors. Results identified glutamate oxaloacetate transferase 2 (GOT2) as a candidate protein biomarker involved in glucose metabolism, in addition to those that have already been reported previously. Taken together, these direct MS analysis methods provide a robust means of analyzing proteins in their native state and are expected to provide insights to their role in biological systems