Improving spatially resolved MSI analysis of tissue sections for DMPK and toxicity studies

The aim of the work presented herein was to re-evaluate the sample preparation pipeline for mass spectrometry imaging (MSI) experiments focusing on metabolite distributions and drug disposition. The work evaluated the steps from sample collection to quantitative interpretation of the results. A major focus of the work was set on the integration of the evaluated and newly developed workflows with orthogonal tissue imaging techniques. The work evaluated the effects of sample collection in formalin and subsequent preparation into formalin-fixed, paraffin embedded tissues. Overall, these treatments were found to substantially alter the tissue metabolome and distort metabolite and drug distributions, validating the current ‘gold standard’ of fresh-frozen tissues for metabolite and drug disposition focused MSI studies. These high-quality tissues require commonly cryo-sectioning to enable MSI analysis. Sample embedding strategies were explored to allow simultaneous preparation and analysis of several tissue specimens at once to increase technical reproducibility. To achieve highest preservation of the specimens, a novel embedding medium based on a hydroxypropyl-methylcellulose and polyvinylpyrrolidone hydrogel was developed. Within the frame of this work, strategies to decontaminate prepared tissue sections prior to MSI analysis will be reviewed, to minimize the infection risk when handling human tissues or specimen from infection models. Irradiation with UV-C light was found to be a suited decontamination as it enables accurate elucidation of endogenous biodistributions whilst only inflicting minor alterations to the tissue metabolome. The utility of a novel DESI setup based on a triple-quadrupole mass spectrometer was described and its application to elucidate drug disposition within tissues. The quantitative relationship of DESI- and MALDI-MSI were explored and some of the newly developed and established workflows were utilized in a multi-omics approach to elucidate the toxicokinetic effects of polymyxin B1 in a model of drug induced nephrotoxicity.Open Acces

Faster, more reproducible DESI-MS for biological tissue imaging

A new, more robust sprayer for desorption electrospray ionization (DESI) mass spectrometry imaging is presented. The main source of variability in DESI is thought to be the uncontrolled variability of various geometric parameters of the sprayer, primarily the position of the solvent capillary, or more specifically, its positioning within the gas capillary or nozzle. If the solvent capillary is off-center, the sprayer becomes asymmetrical, making the geometry difficult to control and compromising reproducibility. If the stiffness, tip quality, and positioning of the capillary are improved, sprayer reproducibility can be improved by an order of magnitude. The quality of the improved sprayer and its potential for high spatial resolution imaging are demonstrated on human colorectal tissue samples by acquisition of images at pixel sizes of 100, 50, and 20 μm, which corresponds to a lateral resolution of 40-60 μm, similar to the best values published in the literature. The high sensitivity of the sprayer also allows combination with a fast scanning quadrupole time-of-flight mass spectrometer. This provides up to 30 times faster DESI acquisition, reducing the overall acquisition time for a 10 mm × 10 mm rat brain sample to approximately 1 h. Although some spectral information is lost with increasing analysis speed, the resulting data can still be used to classify tissue types on the basis of a previously constructed model. This is particularly interesting for clinical applications, where fast, reliable diagnosis is required. Graphical Abstract ᅟ

Mass spectrometry: from imaging to metabolic networks

A deeper understanding of inter-tumorand intra-tumorheterogeneity is a critical factor for the advancement of next generation strategies against cancer. Under the hypothesis that heterogeneous progression of tumorsis mirrored by their metabolic heterogeneity, detection of biochemical mechanisms responsible of the local metabolism becomes crucial.We show that network analysis of co-localized ions from mass spectrometry imaging data provides a detailed chemo-spatial insightinto the metabolic heterogeneity of tumor. Furthermore, module preservation analysis between colorectal cancer patients with and without metastatic recurrence suggests hypotheses on the nature of the different local metabolic pathways

Evaluation of formalin-fixed and FFPE tissues for spatially resolved metabolomics and drug distribution studies

Fixation of samples is broadly used prior to the histological evaluation of tissue samples. Though recent reports demonstrated the ability to use fixed tissues for mass spectrometry imaging (MSI) based proteomics, glycomics and tumor classification studies, to date comprehensive evaluation of fixation-related effects for spatially resolved metabolomics and drug disposition studies is still missing. In this study we used matrix assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) MSI to investigate the effect of formalin-fixation and formalin-fixation combined with paraffin embedding on the detectable metabolome including xenobiotics. Formalin fixation was found to cause significant washout of polar molecular species, including inorganic salts, amino acids, organic acids and carnitine species, oxidation of endogenous lipids and formation of reaction products between lipids and fixative ingredients. The slow fixation kinetics under ambient conditions resulted in increased lipid hydrolysis in the tissue core, correlating with the time-dependent progression of the fixation. Paraffin embedding resulted in subsequent partial removal of structural lipids resulting in the distortion of the elucidated biodistributions

Targeted desorption electrospray ionization mass spectrometry imaging for drug distribution, toxicity, and tissue classification studies

With increased use of mass spectrometry imaging (MSI) in support of pharmaceutical research and development, there are opportunities to develop analytical pipelines that incorporate exploratory high-performance analysis with higher capacity and faster targeted MSI. Therefore, to enable faster MSI data acquisition we present analyte-targeted desorption electrospray ionization–mass spectrometry imaging (DESI-MSI) utilizing a triple-quadrupole (TQ) mass analyzer. The evaluated platform configuration provided superior sensitivity compared to a conventional time-of-flight (TOF) mass analyzer and thus holds the potential to generate data applicable to pharmaceutical research and development. The platform was successfully operated with sampling rates up to 10 scans/s, comparing positively to the 1 scan/s commonly used on comparable DESI-TOF setups. The higher scan rate enabled investigation of the desorption/ionization processes of endogenous lipid species such as phosphatidylcholines and a co-administered cassette of four orally dosed drugs—erlotininb, moxifloxacin, olanzapine, and terfenadine. This was used to enable understanding of the impact of the desorption/ionization processes in order to optimize the operational parameters, resulting in improved compound coverage for olanzapine and the main olanzapine metabolite, hydroxy-olanzapine, in brain tissue sections compared to DESI-TOF analysis or matrix-assisted laser desorption/ionization (MALDI) platforms. The approach allowed reducing the amount of recorded information, thus reducing the size of datasets from up to 150 GB per experiment down to several hundred MB. The improved performance was demonstrated in case studies investigating the suitability of this approach for mapping drug distribution, spatially resolved profiling of drug-induced nephrotoxicity, and molecular–histological tissue classification of ovarian tumors specimens

Correlated Heterospectral Lipidomics for Biomolecular Profiling of Remyelination in Multiple Sclerosis

Analyzing lipid composition and distribution within the brain is important to study white matter pathologies that present focal demyelination lesions, such as multiple sclerosis. Some lesions can endogenously re-form myelin sheaths. Therapies aim to enhance this repair process in order to reduce neurodegeneration and disability progression in patients. In this context, a lipidomic analysis providing both precise molecular classification and well-defined localization is crucial to detect changes in myelin lipid content. Here we develop a correlated heterospectral lipidomic (HSL) approach based on coregistered Raman spectroscopy, desorption electrospray ionization mass spectrometry (DESI-MS), and immunofluorescence imaging. We employ HSL to study the structural and compositional lipid profile of demyelination and remyelination in an induced focal demyelination mouse model and in multiple sclerosis lesions from patients ex vivo. Pixelwise coregistration of Raman spectroscopy and DESI-MS imaging generated a heterospectral map used to interrelate biomolecular structure and composition of myelin. Multivariate regression analysis enabled Raman-based assessment of highly specific lipid subtypes in complex tissue for the first time. This method revealed the temporal dynamics of remyelination and provided the first indication that newly formed myelin has a different lipid composition compared to normal myelin. HSL enables detailed molecular myelin characterization that can substantially improve upon the current understanding of remyelination in multiple sclerosis and provides a strategy to assess remyelination treatments in animal models

Targeted Desorption Electrospray Ionization Mass Spectrometry Imaging for Drug Distribution, Toxicity, and Tissue Classification Studies

With increased use of mass spectrometry imaging (MSI) in support of pharmaceutical research and development, there are opportunities to develop analytical pipelines that incorporate exploratory high-performance analysis with higher capacity and faster targeted MSI. Therefore, to enable faster MSI data acquisition we present analyte-targeted desorption electrospray ionization–mass spectrometry imaging (DESI-MSI) utilizing a triple-quadrupole (TQ) mass analyzer. The evaluated platform configuration provided superior sensitivity compared to a conventional time-of-flight (TOF) mass analyzer and thus holds the potential to generate data applicable to pharmaceutical research and development. The platform was successfully operated with sampling rates up to 10 scans/s, comparing positively to the 1 scan/s commonly used on comparable DESI-TOF setups. The higher scan rate enabled investigation of the desorption/ionization processes of endogenous lipid species such as phosphatidylcholines and a co-administered cassette of four orally dosed drugs—erlotininb, moxifloxacin, olanzapine, and terfenadine. This was used to enable understanding of the impact of the desorption/ionization processes in order to optimize the operational parameters, resulting in improved compound coverage for olanzapine and the main olanzapine metabolite, hydroxy-olanzapine, in brain tissue sections compared to DESI-TOF analysis or matrix-assisted laser desorption/ionization (MALDI) platforms. The approach allowed reducing the amount of recorded information, thus reducing the size of datasets from up to 150 GB per experiment down to several hundred MB. The improved performance was demonstrated in case studies investigating the suitability of this approach for mapping drug distribution, spatially resolved profiling of drug-induced nephrotoxicity, and molecular–histological tissue classification of ovarian tumors specimens

LungStage - Die Zukunft in der Befundung von Lungenkrebs

## Ziel Diese Arbeit untersuchte, wie die automatisierte Erkennung von Lungenkrebs durch künstliche Intelligenz (nachfolgend «KI» genannt) in einem User Interface (nachfolgend «UI» genannt) bereitgestellt werden kann. Die Diagnose von Lungenkrebs sollte beschleunigt und die Radiologen entlastet werden. ## Hintergrund Durch die Computertomographie entstehen in der Schweiz jährlich Petabytes an radiologischen Bilddaten, welche von Fachärzten für Diagnostik und Behandlungsplanung analysiert werden. Diese Analyse ist kostspielig und zeitaufwändig. Eine PET/ CT Tomographie eines Lungenkrebspatienten beinhaltet rund 6‘000 Einzelbilder. Eine KI kann darauf trainiert werden, Tumore und Metastasen in den Bildern zu finden und so den Prozess zu beschleunigen. Da die Ergebnisse der KI aber nicht deterministisch sind, musste ein Weg gefunden werden, wie das Resultat der Maschine dem Menschen vertrauenswürdig kommuniziert werden konnte. ## Methode Mit qualitativen Methoden sollte untersucht werden, wie Radiologen Lungenkrebs befunden. In einem theoretischen Teil wurde untersucht, welche Aspekte wichtig waren, damit der Radiologe dem Output der KI vertrauen konnte. Danach wurde anhand von drei Iterationen mit Prototypen in steigender Detailtreue untersucht, wie das UI für LungStage aussehen sollte. Die Prototypen wurden mit Radiologen und Nuklearmedizinern in drei Spitälern evaluiert. ## Resultat Das Hauptergebnis der Arbeit war ein klickbarer, animierter Hi-Fi Prototyp. Er demonstrierte, wie die Benutzer in Zukunft mit Hilfe von KI Lungenkrebs befunden könnten und ermöglichte dem Auftraggeber bereits während dem Projekt, weitere Partner für die Zusammenarbeit und Weiterentwicklung zu finden. ## Anwendung und nächste Schritte Das erarbeitete Design bildete die Grundlage für die weitere Entwicklung durch den Auftraggeber. Das Projektteam empfahl, das Design mit der KI zu verbinden und mit weiteren Testpersonen zu evaluieren, die bisher noch nicht am Projekt beteiligt waren