Lipidomic Typing of Colorectal Cancer Tissue Containing Tumour-Infiltrating Lymphocytes by MALDI Mass Spectrometry Imaging

Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma

MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases

BACKGROUND

Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts.

AIM OF THE REVIEW

The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.

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.

Recent Analytical Methodologies in Lipid Analysis

Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.

Detection, mechanisms, and therapeutic implications of oncometabolites

Metabolic abnormalities are a hallmark of cancer cells and are essential to tumor progression. Oncometabolites have pleiotropic effects on cancer biology and affect a plethora of processes, from oncogenesis and metabolism to therapeutic resistance. Targeting oncometabolites, therefore, could offer promising therapeutic avenues against tumor growth and resistance to treatments. Recent advances in characterizing the metabolic profiles of cancer cells are shedding light on the underlying mechanisms and associated metabolic networks. This review summarizes the diverse detection methods, molecular mechanisms, and therapeutic targets of oncometabolites, which may lead to targeting oncometabolism for cancer therapy.

Spatial metabolomics principles and application to cancer research

Mass spectrometry imaging (MSI) is an emerging technology in cancer metabolomics. Desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI) MSI are complementary techniques to identify hundreds of metabolites in space with close to single-cell resolution. This technology leap enables research focusing on tumor heterogeneity, cancer cell plasticity, and the communication signals between cancer and stromal cells in the tumor microenvironment (TME). Currently, unprecedented knowledge is generated using spatial metabolomics in fundamental cancer research. Yet, also translational applications are emerging, including the assessment of spatial drug distribution in organs and tumors. Moreover, clinical research investigates the use of spatial metabolomics as a rapid pathology tool during cancer surgeries. Here, we summarize MSI applications, the knowledge gained by this technology in space, future directions, and developments needed.

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.

Self-supervised clustering analysis of colorectal cancer biomarkers based on multi-scale whole slides image and mass spectrometry imaging fused images

Mass spectrometry imaging (MSI) is widely used for unlabeled molecular co-localization in biological samples and is also commonly used for screening cancer biomarkers. The main issues affecting the screening of cancer biomarkers are: 1) low-resolution MSI and pathological slices cannot be accurately matched; 2) a large amount of MSI data cannot be directly analyzed without manual annotation. This paper proposes a self-supervised cluster analysis method for colorectal cancer biomarkers based on multi-scale whole slide images (WSI) and MSI fusion images without manual annotation, which can accurately determine the correlation between molecules and lesion areas. This paper uses the combination of WSI multi-scale high-resolution and MSI high-dimensional data to obtain high-resolution fusion images. This method can observe the spatial distribution of molecules in pathological slices and use this method as an evaluation index for self-supervised screening of cancer biomarkers. The experimental results show that the method proposed in this chapter can train the image fusion model with a small amount of MSI and WSI data, and the mean Pixel Accuracy (mPA) and mean Intersection over Union (mIoU) evaluation metrics of the fused images can reach 0.9587 and 0.8745. And self-supervised clustering using MSI features and fused image features can obtain good classification results, and the precision, recall, and F1-score values of the self-supervised model reach 0.9074, 0.9065, and 0.9069, respectively. This method effectively combines the advantages of WSI and MSI, which will significantly expand the application scenarios of MSI and facilitate the screening of disease markers.

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.

Lipid Metabolism Interplay in CRC—An Update

Colorectal cancer (CRC) to date still ranks as one of the deadliest cancer entities globally, and despite recent advances, the incidence in young adolescents is dramatically increasing. Lipid metabolism has recently received increased attention as a crucial element for multiple aspects of carcinogenesis and our knowledge of the underlying mechanisms is steadily growing. However, the mechanism how fatty acid metabolism contributes to CRC is still not understood in detail. In this review, we aim to summarize our vastly growing comprehension and the accompanied complexity of cellular fatty acid metabolism in CRC by describing inputs and outputs of intracellular free fatty acid pools and how these contribute to cancer initiation, disease progression and metastasis. We highlight how different lipid pathways can contribute to the aggressiveness of tumors and affect the prognosis of patients. Furthermore, we focus on the role of lipid metabolism in cell communication and interplay within the tumor microenvironment (TME) and beyond. Understanding these interactions in depth might lead to the discovery of novel markers and new therapeutic interventions for CRC. Finally, we discuss the crucial role of fatty acid metabolism as new targetable gatekeeper in colorectal cancer.

Human Group IIA Phospholipase A2-Three Decades on from Its Discovery

Phospholipase A₂ (PLA₂) enzymes were first recognized as an enzyme activity class in 1961. The secreted (sPLA₂) enzymes were the first of the five major classes of human PLA₂s to be identified and now number nine catalytically-active structurally homologous proteins. The best-studied of these, group IIA sPLA₂, has a clear role in the physiological response to infection and minor injury and acts as an amplifier of pathological inflammation. The enzyme has been a target for anti-inflammatory drug development in multiple disorders where chronic inflammation is a driver of pathology since its cloning in 1989. Despite intensive effort, no clinically approved medicines targeting the enzyme activity have yet been developed. This review catalogues the major discoveries in the human group IIA sPLA₂ field, focusing on features of enzyme function that may explain this lack of success and discusses future research that may assist in realizing the potential benefit of targeting this enzyme. Functionally-selective inhibitors together with isoform-selective inhibitors are necessary to limit the apparent toxicity of previous drugs. There is also a need to define the relevance of the catalytic function of hGIIA to human inflammatory pathology relative to its recently-discovered catalysis-independent function.

Directing the Future Breakthroughs in Immunotherapy: The Importance of a Holistic Approach to the Tumour Microenvironment

Simple Summary

Immunotherapies have changed the way we treat cancer and, while some patients have benefitted greatly, there are still those that do not respond to therapy. Understanding why some patients respond to therapy and others do not is critical in developing new immunotherapeutic strategies. The increasing awareness of the importance of investigating the tumour in its entirety, including the surrounding tissue and role of various immune cells is helping to differentiate responders and non-responders. In addition, the resolution gained by the development of sophisticated bioinformatic technologies allows for a deeper understanding of the complex roles of individual cells in the tumour. This advancement will be critical for the development of novel therapies to treat cancer.

Abstract

Immunotherapy has revolutionised the treatment of cancers by exploiting the immune system to eliminate tumour cells. Despite the impressive response in a proportion of patients, clinical benefit has been limited thus far. A significant focus to date has been the identification of specific markers associated with response to immunotherapy. Unfortunately, the heterogeneity between patients and cancer types means identifying markers of response to therapy is inherently complex. There is a growing appreciation for the role of the tumour microenvironment (TME) in directing response to immunotherapy. The TME is highly heterogeneous and contains immune, stromal, vascular and tumour cells that all communicate and interact with one another to form solid tumours. This review analyses major cell populations present within the TME with a focus on their diverse and often contradictory roles in cancer and how this informs our understanding of immunotherapy. Furthermore, we discuss the role of integrated omics in providing a comprehensive view of the TME and demonstrate the potential of leveraging multi-omics to decipher the underlying mechanisms of anti-tumour immunity for the development of novel immunotherapeutic strategies.