Precise co-registration of mass spectrometry imaging, histology, and laser microdissection-based omics

MSIght: A Modular Platform for Improved Confidence in Global, Untargeted Mass Spectrometry Imaging Annotation

Mass spectrometry imaging (MSI) has gained popularity in clinical analyses due to its high sensitivity, specificity, and throughput. However, global profiling experiments are often still restricted to LC-MS/MS analyses that lack spatial localization due to low-throughput methods for on-tissue peptide identification and confirmation. Additionally, the integration of parallel LC-MS/MS peptide confirmation, as well as histological stains for accurate mapping of identifications, presents a large bottleneck for data analysis, limiting throughput for untargeted profiling experiments. Here, we present a novel platform, termed MSIght, which automates the integration of these multiple modalities into an accessible and modular platform. Histological stains of tissue sections are coregistered to their respective MSI data sets to improve spatial localization and resolution of identified peptides. MS/MS peptide identifications via untargeted LC-MS/MS are used to confirm putative MSI identifications, thus generating MS images with greater confidence in a high-throughput, global manner. This platform has the potential to enable large-scale clinical cohorts to utilize MSI in the future for global proteomic profiling that uncovers novel biomarkers in a spatially resolved manner, thus widely expanding the utility of MSI in clinical discovery.

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have recently been developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform the region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a new multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. This workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a poly(ethylene naphthalate)-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed the downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 μm × 200 μm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial- and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

Mass spectrometry imaging methods for visualizing tumor heterogeneity

Profiling spatial distributions of lipids, metabolites, and proteins in tumors can reveal unique cellular microenvironments and provide molecular evidence for cancer cell dysfunction and proliferation. Mass spectrometry imaging (MSI) is a label-free technique that can be used to map biomolecules in tumors in situ. Here, we discuss current progress in applying MSI to uncover molecular heterogeneity in tumors. First, the analytical strategies to profile small molecules and proteins are outlined, and current methods for multimodal imaging to maximize biological information are highlighted. Second, we present and summarize biological insights obtained by MSI of tumor tissue. Finally, we discuss important considerations for designing MSI experiments and several current analytical challenges.

MALDI-MSI-LC-MS/MS Workflow for Single-Section Single Step Combined Proteomics and Quantitative Lipidomics

We introduce a novel approach for comprehensive molecular profiling in biological samples. Our single-section methodology combines quantitative mass spectrometry imaging (Q-MSI) and a single step extraction protocol enabling lipidomic and proteomic liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis on the same tissue area. The integration of spatially correlated lipidomic and proteomic data on a single tissue section allows for a comprehensive interpretation of the molecular landscape. Comparing Q-MSI and Q-LC-MS/MS quantification results sheds new light on the effect of MSI and related sample preparation. Performing MSI before Q-LC-MS on the same tissue section led to fewer protein identifications and a lower correlation between lipid quantification results. Also, the critical role and influence of internal standards in Q-MSI for accurate quantification is highlighted. Testing various slide types and the evaluation of different workflows for single-section spatial multiomics analysis emphasized the need for critical evaluation of Q-MSI data. These findings highlight the necessity for robust quantification methods comparable to current gold-standard LC-MS/MS techniques. The spatial information from MSI allowed region-specific insights within heterogeneous tissues, as demonstrated for glioblastoma multiforme. Additionally, our workflow demonstrated the efficiency of a single step extraction for lipidomic and proteomic analyses on the same tissue area, enabling the examination of significantly altered proteins and lipids within distinct regions of a single section. The integration of these insights into a lipid-protein interaction network expands the biological information attainable from a tissue section, highlighting the potential of this comprehensive approach for advancing spatial multiomics research.

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Spatial MS multiomics on clinical prostate cancer tissues

Mass spectrometry (MS) and MS imaging (MSI) are used extensively for both the spatial and bulk characterization of samples in lipidomics and proteomics workflows. These datasets are typically generated independently due to different requirements for sample preparation. However, modern omics technologies now provide higher sample throughput and deeper molecular coverage, which, in combination with more sophisticated bioinformatic and statistical pipelines, make generating multiomics data from a single sample a reality. In this workflow, we use spatial lipidomics data generated by matrix-assisted laser desorption/ionization MSI (MALDI-MSI) on prostate cancer (PCa) radical prostatectomy cores to guide the definition of tumor and benign tissue regions for laser capture microdissection (LCM) and bottom-up proteomics all on the same sample and using the same mass spectrometer. Accurate region of interest (ROI) mapping was facilitated by the SCiLS region mapper software and dissected regions were analyzed using a dia-PASEF workflow. A total of 5525 unique protein groups were identified from all dissected regions. Lysophosphatidylcholine acyltransferase 1 (LPCAT1), a lipid remodelling enzyme, was significantly enriched in the dissected regions of cancerous epithelium (CE) compared to benign epithelium (BE). The increased abundance of this protein was reflected in the lipidomics data with an increased ion intensity ratio for pairs of phosphatidylcholines (PC) and lysophosphatidylcholines (LPC) in CE compared to BE.

In Situ Characterization of Human Follicular Helper CD4 T Cells

The development of an effective humoral response to pathogens and immunogens is a multiphase biological process, which is mediated by the coordinated function of specialized immune cell types in secondary lymphoid organs and particularly in T cell and follicular areas. More specifically, within the follicular/germinal center area, the orchestrated interplay between B cells, follicular helper CD4 T cells (Tfh), and stromal cells triggers a cascade of immune reactions leading to the development of memory B cells and plasma cells able to generate effective, antigen-specific antibodies. The role of Tfh cells in this process is critical. Given the need for vaccines capable to induce antibodies of high affinity, neutralizing activity, and durability, understanding the cellular and molecular mechanisms regulating Tfh cell development is of great importance. Here, we describe novel approaches for the comprehensive understanding of these cells and possible implications for future studies in vaccine development and the understanding of the pathogenesis of relevant diseases.

Multilevel human secondary lymphoid immune system compartmentalization revealed by complementary imaging approaches

Secondary human lymphoid tissue immune reactions take place in a highly coordinated environment with compartmentalization representing a fundamental feature of this organization. In situ profiling methodologies are indispensable for the understanding of this compartmentalization. Here, we propose a complementary experimental approach aiming to reveal different aspects of this process. The analysis of human tonsils, using a combination of single cell phenotypic analysis based on flow cytometry and multiplex imaging and mass spectrometry-based methodologies, revealed a compartmentalized organization at the cellular and molecular levels. More specifically, the skewed distribution of highly specialized immune cell subsets and relevant soluble mediators was accompanied by a compartmentalized localization of several lipids across different anatomical areas of the tonsillar tissue. The performance of such combinatorial experimental approaches could lead to the identification of novel in situ interactions and molecular targets for the in vivo manipulation of lymphoid organ, particularly the germinal center, immune reactions.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

Simultaneous metabolite MALDI-MSI, whole exome and transcriptome analysis from formalin-fixed paraffin-embedded tissue sections

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) allows spatial analysis of proteins, metabolites, or small molecules from tissue sections. Here, we present the simultaneous generation and analysis of MALDI-MSI, whole-exome sequencing (WES), and RNA-sequencing data from the same formalin-fixed paraffin-embedded (FFPE) tissue sections. Genomic DNA and total RNA were extracted from (i) untreated, (ii) hematoxylin-eosin (HE) stained, and (iii) MALDI-MSI-analyzed FFPE tissue sections from three head and neck squamous cell carcinomas. MALDI-MSI data were generated by a time-of-flight analyzer prior to preprocessing and visualization. WES data were generated using a low-input protocol followed by detection of single-nucleotide variants (SNVs), tumor mutational burden, and mutational signatures. The transcriptome was determined using 3'-RNA sequencing and was examined for similarities and differences between processing stages. All data met the commonly accepted quality criteria. Besides SNVs commonly identified between differently processed tissues, FFPE-typical artifactual variants were detected. Tumor mutational burden was in the same range for tissues from the same patient and mutational signatures were highly overlapping. Transcriptome profiles showed high levels of correlation. Our data demonstrate that simultaneous molecular profiling of MALDI-MSI-processed FFPE tissue sections at the transcriptome and exome levels is feasible and reliable.

Protein Alterations in Cardiac Ischemia/Reperfusion Revealed by Spatial-Omics

Myocardial infarction is the most common cause of death worldwide. An understanding of the alterations in protein pathways is needed in order to develop strategies that minimize myocardial damage. To identify the protein signature of cardiac ischemia/reperfusion (I/R) injury in rats, we combined, for the first time, protein matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and label-free proteomics on the same tissue section placed on a conductive slide. Wistar rats were subjected to I/R surgery and sacrificed after 24 h. Protein MALDI-MSI data revealed ischemia specific regions, and distinct profiles for the infarct core and border. Firstly, the infarct core, compared to histologically unaffected tissue, showed a significant downregulation of cardiac biomarkers, while an upregulation was seen for coagulation and immune response proteins. Interestingly, within the infarct tissue, alterations in the cytoskeleton reorganization and inflammation were found. This work demonstrates that a single tissue section can be used for protein-based spatial-omics, combining MALDI-MSI and label-free proteomics. Our workflow offers a new methodology to investigate the mechanisms of cardiac I/R injury at the protein level for new strategies to minimize damage after MI.

Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section

Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized different histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.

In-Depth Mass Spectrometry-Based Proteomics of Formalin-Fixed, Paraffin-Embedded Tissues with a Spatial Resolution of 50-200 μm

Formalin-fixed, paraffin-embedded (FFPE) tissues are banked in large repositories to cost-effectively preserve valuable specimens for later study. With the rapid growth of spatial proteomics, FFPE tissues can serve as a more accessible alternative to more commonly used frozen tissues. However, extracting proteins from FFPE tissues is challenging due to cross-links formed between proteins and formaldehyde. Here, we have adapted the nanoPOTS sample processing workflow, which was previously applied to single cells and fresh-frozen tissues, to profile protein expression from FFPE tissues. Following the optimization of extraction solvents, times, and temperatures, we identified an average of 1312 and 3184 high-confidence master proteins from 10 μm thick FFPE-preserved mouse liver tissue squares having lateral dimensions of 50 and 200 μm, respectively. The observed proteome coverage for FFPE tissues was on average 88% of that achieved for similar fresh-frozen tissues. We also characterized the performance of our fully automated sample preparation and analysis workflow, termed autoPOTS, for FFPE spatial proteomics. This modified nanodroplet processing in one pot for trace samples (nanoPOTS) and fully automated processing in one pot for trace sample (autoPOTS) workflows provides the greatest coverage reported to date for high-resolution spatial proteomics applied to FFPE tissues. Data are available via ProteomeXchange with identifier PXD029729.

What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

Three-dimensional quantitative mass spectrometry imaging in complex system: From subcellular to whole organism

Mass spectrometry imaging (MSI) has been applied for label-free three-dimensional (3D) imaging from position array across the whole organism, which provides high-dimensional quantitative data of inorganic or organic compounds that may play an important role in the regulation of cellular signaling, including metals, metabolites, lipids, drugs, peptides, and proteins. While MSI is suitable for investigation of the spatial distribution of molecules, it has a limitation with visualization and quantification of multiple molecules. 3D-MSI, however, can be applied toward exploring metabolic pathway as well as the interactions of lipid-protein, protein-protein, and metal-protein in complex systems from subcellular to the whole organism through an untargeted methodology. In this review, we highlight the methods and applications of MS-based 3D imaging to address the complexity of molecular interaction from nano- to micrometer lateral resolution, with particular focus on: (a) common and hybrid 3D-MSI techniques; (b) quantitative MSI methodology, including the methods using a stable isotope labeling internal standard (SILIS) and SILIS-free approaches with tissue extinction coefficient or virtual calibration; (c) reconstruction of the 3D organ; (d) application of 3D-MSI for biomarker screening and environmental toxicological research. 3D-MSI quantitative analysis provides accurate spatial information and quantitative variation of biomolecules, which may be valuable for the exploration of the molecular mechanism of the disease progresses and toxicological assessment of environmental pollutants in the whole organism. Additionally, we also discuss the challenges and perspectives on the future of 3D quantitative MSI.

ADVANCES IN HIGH-RESOLUTION MALDI MASS SPECTROMETRY FOR NEUROBIOLOGY

Research in the field of neurobiology and neurochemistry has seen a rapid expansion in the last several years due to advances in technologies and instrumentation, facilitating the detection of biomolecules critical to the complex signaling of neurons. Part of this growth has been due to the development and implementation of high-resolution Fourier transform (FT) mass spectrometry (MS), as is offered by FT ion cyclotron resonance (FTICR) and Orbitrap mass analyzers, which improves the accuracy of measurements and helps resolve the complex biological mixtures often analyzed in the nervous system. The coupling of matrix-assisted laser desorption/ionization (MALDI) with high-resolution MS has drastically expanded the information that can be obtained with these complex samples. This review discusses notable technical developments in MALDI-FTICR and MALDI-Orbitrap platforms and their applications toward molecules in the nervous system, including sequence elucidation and profiling with de novo sequencing, analysis of post-translational modifications, in situ analysis, key advances in sample preparation and handling, quantitation, and imaging. Notable novel applications are also discussed to highlight key developments critical to advancing our understanding of neurobiology and providing insight into the exciting future of this field. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities

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Mass spectrometry imaging (MSI) is a powerful molecular imaging technique.

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Integration with other imaging modalities is essential in clinical MSI.

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Image integration is performed by image registration techniques.

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Technical potential of image registration in MSI has not been fully exploited.

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Roadmap proposed to improve registration accuracy.

Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.

Proteomics in support of immunotherapy: contribution to model-based precision medicine

INTRODUCTION

Proteomics encompasses a wide and expanding range of methods to identify, characterize, and quantify thousands of proteins from a variety of biological samples, including blood samples, tumors, and tissues. Such methods are supportive of various forms of immunotherapy applied to chronic conditions such as allergies, autoimmune diseases, cancers, and infectious diseases.

AREAS COVERED

In support of immunotherapy, proteomics based on mass spectrometry has multiple specific applications related to (i) disease modeling and patient stratification, (ii) antigen/ autoantigen/neoantigen/ allergen identification, (iii) characterization of proteins and monoclonal antibodies used for immunotherapeutic or diagnostic purposes, (iv) identification of biomarkers and companion diagnostics and (v) monitoring by immunoproteomics of immune responses elicited in the course of the disease or following immunotherapy.

EXPERT OPINION

Proteomics contributes as an enabling technology to an evolution of immunotherapy toward a precision medicine approach aiming to better tailor treatments to patients' specificities in multiple disease areas. This trend is favored by a better understanding through multi-omics profiling of both the patient's characteristics, his/her immune status as well as of the features of the immunotherapeutic drug.

Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine

INTRODUCTION

Laser Capture Microdissection (LCM) uses a laser to isolate, or capture, specific cells of interest in a complex heterogeneous tissue section, under direct microscopic visualization. Recently, there has been a surge of publications using LCM for tissue spatial molecular profiling relevant to a wide range of research topics.

AREAS COVERED

We summarize the many advances in tissue Laser Capture Proteomics (LCP) using mass spectrometry for discovery, and protein arrays for signal pathway network mapping. This review emphasizes: a) transition of LCM phosphoproteomics from the lab to the clinic for individualized cancer therapy, and b) the emerging frontier of LCM single cell molecular analysis combining proteomics with genomic, and transcriptomic analysis. The search strategy was based on the combination of MeSH terms with expert refinement.

EXPERT OPINION

LCM is complemented by a rich set of instruments, methodology protocols, and analytical A.I. (artificial intelligence) software for basic and translational research. Resolution is advancing to the tissue single cell level. A vision for the future evolution of LCM is presented. Emerging LCM technology is combining digital and AI guided remote imaging with automation, and telepathology, to a achieve multi-omic profiling that was not previously possible.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

Microproteomic sample preparation

Multiple applications of proteomics in life and health science, pathology and pharmacology, require handling size-limited cell and tissue samples. During proteomic sample preparation, analyte loss in these samples arises when standard procedures are used. Thus, specific considerations have to be taken into account for processing, that are summarised under the term microproteomics (μPs). Microproteomic workflows include: sampling (e.g., flow cytometry, laser capture microdissection), sample preparation (possible disruption of cells or tissue pieces via lysis, protein extraction, digestion in bottom-up approaches, and sample clean-up) and analysis (chromatographic or electrophoretic separation, mass spectrometric measurements and statistical/bioinformatic evaluation). All these steps must be optimised to reach wide protein dynamic ranges and high numbers of identifications. Under optimal conditions, sampling is adapted to the studied sample types and nature, sample preparation isolates and enriches the whole protein content, clean-up removes salts and other interferences such as detergents or chaotropes, and analysis identifies as many analytes as the instrumental throughput and sensitivity allow. In the suggested review, we present and discuss the current state in μP applications for processing of small number of cells (cell μPs) and microscopic tissue regions (tissue μPs).

Method to Investigate the Distribution of Water-Soluble Drug-Delivery Systems in Fresh Frozen Tissues Using Imaging Mass Cytometry

Imaging mass cytometry (IMC) offers the opportunity to image metal- and heavy halogen-containing xenobiotics in a highly multiplexed experiment with other immunochemistry-based reagents to distinguish uptake into different tissue structures or cell types. However, in practice, many xenobiotics are not amenable to this analysis, as any compound which is not bound to the tissue matrix will delocalize during aqueous sample-processing steps required for IMC analysis. Here, we present a strategy to perform IMC experiments on a water-soluble polysarcosine-modified dendrimer drug-delivery system (S-Dends). This strategy involves two consecutive imaging acquisitions on the same tissue section using the same instrumental platform, an initial laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MSI) experiment followed by tissue staining and a standard IMC experiment. We demonstrated that settings can be found for the initial ablation step that leave sufficient residual tissue for subsequent antibody staining and visualization. This workflow results in lateral resolution for the S-Dends of 2 μm followed by imaging of metal-tagged antibodies at 1 μm.

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

Mass Spectrometry Spatial-Omics on a Single Conductive Slide

Mass spectrometry imaging (MSI) can analyze the spatial distribution of hundreds of different molecules directly from tissue sections usually placed on conductive glass slides to provide conductivity on the sample surface. Additional experiments are often required for molecular identification using consecutive sections on membrane slides compatible with laser capture microdissection (LMD). In this work, we demonstrate for the first time the use of a single conductive slide for both matrix-assisted laser desorption ionization (MALDI)-MSI and direct proteomics. In this workflow, regions of interest can be directly ablated with LMD while preserving protein integrity. These results offer an alternative for MSI-based multimodal spatial-omics.

Applications of mass spectrometry imaging in virus research

Mass spectrometry imaging (MSI) is a label-free molecular imaging technique allowing an untargeted detection of a broad range of biomolecules and xenobiotics. MSI enables imaging of the spatial distribution of proteins, peptides, lipids and metabolites from a wide range of samples. To date, this technique is commonly applied to tissue sections in cancer diagnostics and biomarker development, but also molecular histology in general. Advances in the methodology and bioinformatics improved the resolution of MS images below the single cell level and increased the flexibility of the workflow. However, MSI-based research in virology is just starting to gain momentum and its full potential has not been exploited yet. In this review, we discuss the main applications of MSI in virology. We review important aspects of matrix-assisted laser desorption/ionization (MALDI) MSI, the most widely used MSI technique in virology. In addition, we summarize relevant literature on MSI studies that aim to unravel virus-host interactions and virus pathogenesis, to elucidate antiviral drug kinetics and to improve current viral disease diagnostics. Collectively, these studies strongly improve our general understanding of virus-induced changes in the proteome, metabolome and metabolite distribution in host tissues of humans, animals and plants upon infection. Furthermore, latest MSI research provided important insights into the drug distribution and distribution kinetics, especially in antiretroviral research. Finally, MSI-based investigations of oncogenic viruses greatly increased our knowledge on tumor mass signatures and facilitated the identification of cancer biomarkers.

Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections

Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub) types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections.

In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n = 11), clear cell renal cell carcinoma (ccRCC, n = 12) and chromophobe renal cell carcinoma (ChRCC, n = 5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients.

This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.

Protocol Improvement for RNA Extraction From Compromised Frozen Specimens Generated in Austere Conditions: A Path Forward to Transcriptomics-Pathology Systems Integration

At the heart of the phenome-to-genome approach is high throughput assays, which are liable to produce false results. This risk can be mitigated by minimizing the sample bias, specifically, recycling the same tissue specimen for both phenotypic and genotypic investigations. Therefore, our aim is to suggest a methodology of obtaining robust results from frozen specimens of compromised quality, particularly if the sample is produced in conditions with limited resources. For example, generating samples at the International Space Station (ISS) is challenging because the time and laboratory footprint allotted to a project can get expensive. In an effort to be economical with available resources, snap-frozen euthanized mice are the straightforward solution; however, this method increases the risk of temperature abuse during the thawing process at the beginning of the tissue collection. We found that prolonged immersion of snap frozen mouse carcass in 10% neutral buffered formalin at 4°C yielded minimal microscopic signs of ice crystallization and delivered tissues with histomorphology that is optimal for hematoxylin and eosin (H&E) staining and fixation on glass slides. We further optimized a method to sequester the tissue specimen from the H&E slides using an incubator shaker. Using this method, we were able to recover an optimal amount of RNA that could be used for downstream transcriptomics assays. Overall, we demonstrated a protocol that enables us to maximize scientific values from tissues collected in austere condition. Furthermore, our protocol can suggest an improvement in the spatial resolution of transcriptomic assays.

Spatially resolved proteomics in osteoarthritis: State of the art and new perspectives

Osteoarthritis (OA) is one of the most common diseases worldwide caused by chronic degeneration of the joints. Its high prevalence and the involvement of several tissues define OA as a highly heterogeneous disease. New biological markers to evaluate the progression of the pathology and improve its prognosis are needed. Among all the different -omic strategies applied to OA, solution phase bottom-up proteomics has made an extensive contribution to the field of biomarker research. However, new technologies for protein analysis should be considered for a better understanding of the disease. This review focuses on complementary proteomic methodologies and new technologies for translational research of OA and other rheumatic pathologies, especially mass spectrometry imaging and protein imaging methods not applied by the OA community yet.

Imaging Isomers on a Biological Surface: A Review

Mass spectrometry imaging is an imaging technology that allows the localization and identification of molecules on (biological) sample surfaces. Obtaining the localization of a compound in tissue is of great value in biological research. Yet, the identification of compounds remains a challenge. Mass spectrometry alone, even with high-mass resolution, cannot always distinguish between the subtle structural differences of isomeric compounds. This review discusses recent advances in mass spectrometry imaging of lipids, steroid hormones, amino acids and proteins that allow imaging with isomeric resolution. These improvements in detailed identification can give new insights into the local biological activity of isomers.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.