A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF

Use of tryptic peptide MALDI mass spectrometry imaging to identify the spatial proteomic landscape of colorectal cancer liver metastases

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. CRC liver metastases (CRLM) are often resistant to conventional treatments, with high rates of recurrence. Therefore, it is crucial to identify biomarkers for CRLM patients that predict cancer progression. This study utilised matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to spatially map the CRLM tumour proteome. CRLM tissue microarrays (TMAs) of 84 patients were analysed using tryptic peptide MALDI-MSI to spatially monitor peptide abundances across CRLM tissues. Abundance of peptides was compared between tumour vs stroma, male vs female and across three groups of patients based on overall survival (0-3 years, 4-6 years, and 7+ years). Peptides were then characterised and matched using LC-MS/MS. A total of 471 potential peptides were identified by MALDI-MSI. Our results show that two unidentified m/z values (1589.876 and 1092.727) had significantly higher intensities in tumours compared to stroma. Ten m/z values were identified to have correlation with biological sex. Survival analysis identified three peptides (Histone H4, Haemoglobin subunit alpha, and Inosine-5'-monophosphate dehydrogenase 2) and two unidentified m/z values (1305.840 and 1661.060) that were significantly higher in patients with shorter survival (0-3 years relative to 4-6 years and 7+ years). This is the first study using MALDI-MSI, combined with LC-MS/MS, on a large cohort of CRLM patients to identify the spatial proteome in this malignancy. Further, we identify several protein candidates that may be suitable for drug targeting or for future prognostic biomarker development.

Automated Spatially Targeted Optical Microproteomics Investigates Inflammatory Lesions In Situ

Tissue microenvironment properties like blood flow, extracellular matrix, or proximity to immune-infiltrate are important regulators of cell biology. However, methods to study regional protein expression in the native tissue environment are limited. To address this need, we developed a novel approach to visualize, purify, and measure proteins in situ using automated spatially targeted optical microproteomics (AutoSTOMP). Here, we report custom codes to specify regions of heterogeneity in a tissue section and UV-biotinylate proteins within those regions. We have developed liquid chromatography–mass spectrometry (LC–MS)/MS-compatible biochemistry to purify those proteins and label-free quantification methodology to determine protein enrichment in target cell types or structures relative to nontarget regions in the same sample. These tools were applied to (a) identify inflammatory proteins expressed by CD68⁺ macrophages in rat cardiac infarcts and (b) characterize inflammatory proteins enriched in IgG4⁺ lesions in human esophageal tissues. These data indicate that AutoSTOMP is a flexible approach to determine regional protein expression in situ on a range of primary tissues and clinical biopsies where current tools and sample availability are limited.

Developing mass spectrometry for the quantitative analysis of neuropeptides

INTRODUCTION

Neuropeptides are signaling molecules originating in the neuroendocrine system that can act as neurotransmitters and hormones in many biochemical processes. Their exact function is difficult to characterize, however, due to dependence on concentration, post-translational modifications, and the presence of other comodulating neuropeptides. Mass spectrometry enables sensitive, accurate, and global peptidomic analyses that can profile neuropeptide expression changes to understand their roles in many biological problems, such as neurodegenerative disorders and metabolic function.

AREAS COVERED

We provide a brief overview of the fundamentals of neuropeptidomic research, limitations of existing methods, and recent progress in the field. This review is focused on developments in mass spectrometry and encompasses labeling strategies, post-translational modification analysis, mass spectrometry imaging, and integrated multi-omic workflows, with discussion emphasizing quantitative advancements.

EXPERT OPINION

Neuropeptidomics is critical for future clinical research with impacts in biomarker discovery, receptor identification, and drug design. While advancements are being made to improve sensitivity and accuracy, there is still room for improvement. Better quantitative strategies are required for clinical analyses, and these methods also need to be amenable to mass spectrometry imaging, post-translational modification analysis, and multi-omics to facilitate understanding and future treatment of many diseases.

Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges

Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.

Enzymes to unravel bioproducts architecture

Enzymes are essential and ubiquitous biocatalysts involved in various metabolic pathways and used in many industrial processes. Here, we reframe enzymes not just as biocatalysts transforming bioproducts but also as sensitive probes for exploring the structure and composition of complex bioproducts, like meat tissue, dairy products and plant materials, in both food and non-food bioprocesses. This review details the global strategy and presents the most recent investigations to prepare and use enzymes as relevant probes, with a focus on glycoside-hydrolases involved in plant deconstruction and proteases and lipases involved in food digestion. First, to expand the enzyme repertoire to fit bioproduct complexity, novel enzymes are mined from biodiversity and can be artificially engineered. Enzymes are further characterized by exploring sequence/structure/dynamics/function relationships together with the environmental factors influencing enzyme interactions with their substrates. Then, the most advanced experimental and theoretical approaches developed for exploring bioproducts at various scales (from nanometer to millimeter) using active and inactive enzymes as probes are illustrated. Overall, combining multimodal and multiscale approaches brings a better understanding of native-form or transformed bioproduct architecture and composition, and paves the way to mainstream the use of enzymes as probes.

MALDI imaging directed laser ablation tissue microsampling for data independent acquisition proteomics

A multimodal workflow for mass spectrometry imaging was developed that combines MALDI imaging with protein identification and quantification by liquid chromatography tandem mass spectrometry (LC-MS/MS). Thin tissue sections were analyzed by MALDI imaging, and the regions of interest (ROI) were identified using a smoothing and edge detection procedure. A midinfrared laser at 3-μm wavelength was used to remove the ROI from the brain tissue section after MALDI mass spectrometry imaging (MALDI MSI). The captured material was processed using a single-pot solid-phase-enhanced sample preparation (SP3) method and analyzed by LC-MS/MS using ion mobility (IM) enhanced data independent acquisition (DIA) to identify and quantify proteins; more than 600 proteins were identified. Using a modified database that included isoform and the post-translational modifications chain, loss of the initial methionine, and acetylation, 14 MALDI MSI peaks were identified. Comparison of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the identified proteins was achieved through an evolutionary relationships classification system.

Mass spectrometry imaging reveals lipid upregulation and bile acid changes indicating amitriptyline induced steatosis in a rat model

As a consequence of the detoxification process, drugs and drug related metabolites can accumulate in the liver, resulting in drug induced liver injury (DILI), which is the major cause for dose limitation. Amitriptyline, a commonly used tricyclic anti-depressant, is known to cause DILI. The mechanism of Amitriptyline induced liver injury is not yet completely understood. However, as it undergoes extensive hepatic metabolism, unraveling the molecular changes in the liver upon Amitriptyline treatment can help understand Amitriptyline's mode of toxicity. In this study, Amitriptyline treated male rat liver tissue was analyzed using Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging (MALDI-MSI) to investigate the spatial abundances of Amitriptyline, lipids, and bile acids. The metabolism of Amitriptyline in liver tissue was successfully demonstrated, as the spatial distribution of Amitriptyline and its metabolites localize throughout treatment group liver samples. Several lipids appear upregulated, from which nine were identified as distinct phosphatidylcholine (PC) species. The detected bile acids were found to be lower in Amitriptyline treatment group. The combined results from histological findings, Oil Red O staining, and lipid zonation by MSI revealed lipid upregulation in the periportal area indicating drug induced macrovesicular steatosis (DIS).

Automated Spatially Targeted Optical Microproteomics (autoSTOMP) to Determine Protein Complexity of Subcellular Structures

Spatially targeted optical microproteomics (STOMP) is a method to study region-specific protein complexity in primary cells and tissue samples. STOMP uses a confocal microscope to visualize structures of interest and to tag the proteins within those structures by a photodriven cross-linking reaction so that they can be affinity purified and identified by mass spectrometry (eLife 2015, 4, e09579). However, the use of a custom photo-cross-linker and the requirement for extensive user intervention during sample tagging have posed barriers to the utilization of STOMP. To address these limitations, we built automated STOMP (autoSTOMP) which uses a customizable code in SikuliX to coordinate image capture and cross-linking functions in Zeiss Zen Black with image processing in FIJI. To increase protocol accessibility, we implemented a commercially available biotin-benzophenone photo-cross-linking and purification protocol. Here we demonstrate that autoSTOMP can efficiently label, purify, and identify proteins belonging to 1-2 μm structures in primary human foreskin fibroblasts or mouse bone marrow-derived dendritic cells infected with the protozoan parasite Toxoplasma gondii (Tg). AutoSTOMP can easily be adapted to address a range of research questions using Zeiss Zen Black microscopy systems and LC-MS protocols that are standard in many research cores.

High-Performance Molecular Imaging with MALDI Trapped Ion-Mobility Time-of-Flight (timsTOF) Mass Spectrometry

Imaging mass spectrometry (IMS) enables the spatially targeted molecular assessment of biological tissues at cellular resolutions. New developments and technologies are essential for uncovering the molecular drivers of native physiological function and disease. Instrumentation must maximize spatial resolution, throughput, sensitivity, and specificity, because tissue imaging experiments consist of thousands to millions of pixels. Here, we report the development and application of a matrix-assisted laser desorption/ionization (MALDI) trapped ion-mobility spectrometry (TIMS) imaging platform. This prototype MALDI timsTOF instrument is capable of 10 μm spatial resolutions and 20 pixels/s throughput molecular imaging. The MALDI source utilizes a Bruker SmartBeam 3-D laser system that can generate a square burn pattern of <10 × 10 μm at the sample surface. General image performance was assessed using murine kidney and brain tissues and demonstrate that high-spatial-resolution imaging data can be generated rapidly with mass measurement errors <5 ppm and ∼40 000 resolving power. Initial TIMS-based imaging experiments were performed on whole-body mouse pup tissue demonstrating the separation of closely isobaric [PC(32:0) + Na]+ and [PC(34:3) + H]+ (3 mDa mass difference) in the gas phase. We have shown that the MALDI timsTOF platform can maintain reasonable data acquisition rates (>2 pixels/s) while providing the specificity necessary to differentiate components in complex mixtures of lipid adducts. The combination of high-spatial-resolution and throughput imaging capabilities with high-performance TIMS separations provides a uniquely tunable platform to address many challenges associated with advanced molecular imaging applications.

Correlated Materials Characterization via Multimodal Chemical and Functional Imaging

Multimodal chemical imaging simultaneously offers high-resolution chemical and physical information with nanoscale and, in select cases, atomic resolution. By coupling modalities that collect physical and chemical information, we can address scientific problems in biological systems, battery and fuel cell research, catalysis, pharmaceuticals, photovoltaics, medicine, and many others. The combined systems enable the local correlation of material properties with chemical makeup, making fundamental questions of how chemistry and structure drive functionality approachable. In this Review, we present recent progress and offer a perspective for chemical imaging used to characterize a variety of samples by a number of platforms. Specifically, we present cases of infrared and Raman spectroscopies combined with scanning probe microscopy; optical microscopy and mass spectrometry; nonlinear optical microscopy; and, finally, ion, electron, and probe microscopies with mass spectrometry. We also discuss the challenges associated with the use of data originated by the combinatorial hardware, analysis, and machine learning as well as processing tools necessary for the interpretation of multidimensional data acquired from multimodal studies.

MALDI mass spectrometry imaging and in situ microproteomics of Listeria monocytogenes biofilms

MALDI-TOF Mass spectrometry Imaging (MSI) is a surface-sampling technology that can determine spatial information and relative abundance of analytes directly from biological samples. Human listeriosis cases are due to the ingestion of contaminated foods with the pathogenic bacteria Listeria monocytogenes. The reduction of water availability in food workshops by decreasing the air relative humidity (RH) is one strategy to improve the control of bacterial contamination. This study aims to develop and implement an MSI approach on L. monocytogenes biofilms and proof of concept using a dehumidified stress condition. MSI allowed examining the distribution of low molecular weight proteins within the biofilms subjected to a dehumidification environment, mimicking the one present in a food workshop (10 °C, 75% RH). Furthermore, a LC-MS/MS approach was made to link the dots between MSI and protein identification. Five identified proteins were assigned to registered MSI m/z, including two cold-shock proteins and a ligase involved in cell wall biogenesis. These data demonstrate how imaging can be used to dissect the proteome of an intact bacterial biofilm giving new insights into protein expression relating to a dehumidification stress adaptation. Data are available via ProteomeXchange with identifier PXD010444.

BIOLOGICAL SIGNIFICANCE

The ready-to-eat food processing industry has the daily challenge of controlling the contamination of surfaces and machines with spoilage and pathogenic microorganisms. In some cases, it is a lost cause due to these microorganisms' capacity to withstand the cleaning treatments, like desiccation procedures. Such a case is the ubiquitous Gram-positive Bacterium Listeria monocytogenes. Its surface proteins have particular importance for the interaction with its environment, being important factors contributing to adaptation to stress conditions. There are few reproducibly techniques to obtain the surface proteins of Gram-positive cells. Here, we developed a workflow that enables the use of MALDI imaging on Gram-positive bacterium biofilms to study the impact of dehumidification on sessile cells. It will be of the most interest to test this workflow with different environmental conditions and potentially apply it to other biofilm-forming bacteria.

Myofiber metabolic type determination by mass spectrometry imaging

Matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging is a powerful tool that opens new research opportunities in the field of biology. In this work, predictive model was developed to discriminate metabolic myofiber types using the MALDI spectral data. Rat skeletal muscles are constituted of type I and type IIA fiber, which have an oxidative metabolism for glycogen degradation, and type IIX and type IIB fiber which have a glycolytic metabolism, present in different proportions according to the muscle function and physiological state. So far, myofiber type is determined by histological methods that are time consuming. Thanks to the predictive model, we were able to predict not only the metabolic fiber type but also their location, on the same muscle section that was used for MALDI imaging. Copyright © 2017 John Wiley & Sons, Ltd.