Abstracts

Mohammadreza Shariatgorji1, Luke Odell2, Anna Nilsson1, Elva Fridjonsdottir1, Luay Katan2, Jonas Sävmarker2, Mattias Witt4, Per Svenningsson3, Per E. Andren1

(1) Biomolecular Mass Spectrometry Imaging, National Resource for Mass Spectrometry Imaging, Department of Pharmaceutical Biosciences, (2) Organic Pharmaceutical Chemistry, Department of Medicinal Chemistry, Uppsala Biomedical Center, Uppsala University, Uppsala University, Box 591, SE-75124 Uppsala, Sweden, (3) Center for Molecular Medicine, Department of Neurology and Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden, and (4) Bruker Daltonik GmbH,
28359 Bremen, Germany

Small-molecule neurotransmitters, their precursors and metabolites are involved in the brain chemical network and transmit signals between neurons. Changes in their concentrations are associated with numerous normal neuronal processes, such as sleep and aging, but also in several disease states, including neurological disorders (e.g., Parkinson’s and Alzheimer’s disease), depression and drug addiction. Knowledge about their relative abundance and spatial distribution would provide insights into these complex neurological processes and disorders. At present, researchers rely on indirect histochemical, immunohistochemical, and ligand-based assays to detect these small-molecule transmitter substances or tissue homogenates analyzed by HPLC. Current neuroimaging techniques have very limited abilities to directly identify and quantify neurotransmitters from brain sections.

By performing matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI) analysis directly on the surface of a tissue section, the technique has quickly been established as a powerful in situ visualization tool for measuring abundance and spatial distribution of endogenous and pharmaceutical compounds, lipids, peptides and small proteins. We have recently introduced a reactive MALDI matrix, which selectively targets the primary amine group on neurotransmitters, metabolites and neuroactive substances but also function as a matrix for the ionization (Neuron, 2014, 84:697-707). However, the limitation of using such reactive matrix to study the full molecular pathways of for example dopaminergic or serotonergic biosynthesis and metabolism is its limitation to target all downstream dopamine metabolites derived from monoamine oxidase (MAO) or catechol-O-methyltransferase (COMT) enzymes. The majority of small molecule neurotransmitters such as catecholamines, amino acids and trace amines possess phenolic hydroxyl and/or primary or secondary amines, which are proper nucleophilic groups. We therefore developed a new reactive matrix that can selectively target and charge-tag both phenolic and primary amine groups, thus enabling MALDI-MSI of both MAO and COMT downstream metabolites. We have focused on a nucleophilic aromatic substitution reaction with such functional groups. By this developed reactive matrix, we were able to detect and map the localization of most of the neurotransmitters and metabolites involved in the dopaminergic and serotonergic network in a single brain tissue section and it represents a novel methodology, which assists their identification through the selectivity of the reaction. The sensitivity and specificity of the imaging approach of neurochemicals has a great potential in many diverse applications in fields such as neuroscience, pharmacology, drug discovery, neurochemistry, and medicine.

MALDI Imaging Mass Spectrometry (IMS) produces molecular maps of peptides, proteins, lipids and metabolites present in intact tissue sections. It employs desorption of molecules by direct laser irradiation to map the location of specific molecules from fresh frozen and formalin fixed tissue sections without the need of target specific reagents such as antibodies. Molecular images of this nature are produced in specific m/z (mass-to-charge) values, or ranges of values. Each imaged specimen gives rise to many hundreds of specific molecular images from a single raster of the tissue. In a complementary approach where only discrete areas within the tissue are of interest, we have developed a histology-directed approach that integrates mass spectrometry and microscopy.

We have employed IMS in studies of a variety of biologically and medically relevant research projects, several of which will be presented including studies in diabetic nephropathy involving both proteins and lipids and the differentiation of benign skin lesions from melanomas. In addition, IMS has been applied to drug targeting and metabolic studies both in specific organs and also in intact whole animal sections following drug administration.

This presentation describes recent technological advances both in sample preparation and instrumental performance to achieve images at high spatial resolution (1-10 microns) and at high speeds so that a typical sample tissue once prepared can be imaged in minutes. Instrumentation used in these studies includes both MALDI FTICR MS and MALDI TOF mass spectrometers. Applications utilize MS/MS, ultra-high mass resolution, and ion accumulation devices for IMS studies. Finally, new biocomputational approaches will be discussed that deals with the high data dimensionality of IMS and our implementation of ‘image fusion’ in terms of predictive integration of MS images with microscopy and other imaging modalities.

Delivering safe and efficacious drugs is tied to our ability to understand the complex mechanistic relationships between molecular initiation events of pharmacologically active compounds and the cascade of biological consequences. MALDI imaging MS (IMS) technology has taken us beyond “plasma centric” studies and allows us to directly map the tissue disposition and molecular changes associated with drug pharmacology. With this approach, we can achieve greater insight into the mechanisms of drug absorption, improve and innovate delivery strategies, observe drugs reaching their intended targets, as well as unintended targets and correlate these events with PK/PD data.

We have explored the application MALDI IMS to investigate the distribution of drugs and their metabolites as well as endogenous compounds in tissue samples without the need for labeling techniques. This methodology allows for the correlation of analyte tissue distributions with histology images, thereby integrating chemical structures with tissue morphology. MALDI IMS provides high spatial resolution (10µm), is highly sensitive, and can be quantitative. Furthermore, this imaging modality offers the potential to further our mechanistic understanding of drug disposition, disease progression and pharmacology (including toxicology) by providing snap shots of temporal and causal changes. While MALDI IMS is primarily being employed to determine the tissue distribution of drugs and their metabolites, it is becoming evident that more detailed understanding of biological system can be gained by including the changes in endogenous compound distribution as a function of disease and pharmacology.

This presentation will focus on our efforts to couple MALDI IMS and histology in drug development to better understand tissue disposition and gain mechanistic insights into drug correlated toxicities and efficacy. Case studies from early and late stage drug development, where MALDI IMS was employed to investigate the mechanisms of adverse events, provide insights into disposition, and PK/PD relationships will be presented. The potential of MALDI IMS beyond just studying drugs and their metabolites will also be presented. In addition, the dependence of realizing the full potential of MALDI IMS on enhanced software tools and improved data handling will be presented.

Drug discovery and development is a lengthy, high risk and competitive business. It can take a decade and costs billions of dollars to get new medicines to market. High attrition rates, together with rising R&D and clinical trial costs, have presented significant challenges to the pharmaceutical industry over the last two decades. The primary reasons for drug attrition have consistently been lack of efficacy and toxicological or clinical safety risk. Mass spectrometry imaging is now impacting on drug discovery programs and helping reduce later stage compound attrition. By providing insights into the biodistribution of compounds, while simultaneously generating data on the pharmacodynamic biomarkers, the suite of imaging modalities can support project along the drug discovery pipeline. Data will be presented showing how we use a range of multimodal imaging techniques to understand compound efficacy, safety and targeted drug delivery across the portfolio with particular attention to oncology. Highlighted will be the challenges and opportunities arising from the significant quantities of molecular imaging data generated, from a cellular to patient level.

Imaging mass spectrometry (IMS) has become a powerful tool that provides spatially-resolved molecular information directly from tissues. Molecular classes such as proteins, lipids, and metabolites can be localized in tissues with spatial resolutions down to 1 micron and below. Ocular tissues provide an ideal medium to demonstrate the utility of the technique where morphological features on the scale of single cells, e.g. retina pigment epithelium, can be analyzed to produce molecular profiles. Moreover, a range of human diseases including glaucoma, age-related macular degeneration, cataract, corneal cataract, among others affect ocular tissues. Molecular mechanisms of disease in these tissues as well as aging mechanisms remain intense areas of research and IMS is actively being applied to derive mechanistic information. In this presentation, IMS data from ocular tissues including optic nerve, retina, lens, and cornea will be presented with special attention to diseases affecting these tissues. Data from both animal models of disease and human tissues will be discussed. In addition, key methodological details for successful imaging of ocular tissues will be discussed.

Mass Spectrometry Imaging (MSI) augments digital pathologic analysis with highly robust big data on cellular metabolic and proteomic molecular content, generating a sheer amount of unrefined data (10s-100s GBs per tissue section). However, managing, analyzing and interpreting this data is a challenge and a major barrier to its clinical translation. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for reproducible processing of hundreds of biological specimens. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. In this talk, I present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly high-throughput system for translational MSI.