TOF-SIMS imaging reveals tumor heterogeneity and inflammatory response markers in the microenvironment of basal cell carcinoma

ToF-SIMS imaging reveals changes in tumor cell lipids during metastatic progression of melanoma

Most melanomas progress from radial to vertical growth phase before spreading locoregionally and distally. Much is still unknown about the metabolic changes in the tumor cells and their microenvironment during this metastatic progression. We aimed to gain new insight into the molecular characteristics of melanoma in regard to spatial lipidomics to deliver new knowledge regarding tumor metastatic progression. We included 10 fresh tumor samples from 10 patients including two in situ melanomas, two invasive primary melanomas, and six metastatic melanomas (four in-transit metastases and two distant metastases). In addition, we analyzed four healthy skin controls from the same patients. Time-of-flight imaging secondary ion mass spectrometry (ToF-SIMS) enabled detailed spatial-lipidomics that could be directly correlated with conventional histopathological analysis of consecutive H&E-stained tissue sections. Significant differences in the lipid profiles were found in primary compared to metastatic melanomas, notably an increase in phosphatidylethanolamine lipids relative to phosphatidylinositol lipids and an increase in GM3 gangliosides in the metastatic samples. Furthermore, analysis of the data from in transit versus distant metastases samples highlighted that specific phospholipids, and a difference in the long versus shorter chain GM3 gangliosides, discriminated the metastatic routes. Further studies are warranted to verify these preliminary findings. Lipidomic changes could serve as a novel biomarker for tumor progression and even serve as a target for novel treatments. Furthermore, analyzing the lipid profiles could help to differentiate between primary and metastatic melanomas in challenging cases.

Lipid organization and turnover in the plasma membrane of human differentiating neural progenitor cells revealed by time-of-flight secondary ion mass spectrometry imaging

Membrane lipids have been known to influence multiple signalling and cellular processes. Dysregulation of lipids at the neuronal membrane is connected to a significant alteration of the brain function and morphology, leading to brain diseases and neurodegeneration. Understanding the lipid composition and turnover of neuronal membrane will provide a significant insight into the molecular events underlying the regulatory effects of these biomolecules in a neuronal system. In this study, we aimed to characterize the composition and turnover of the plasma membrane lipids in human neural progenitor cells (NPCs) at an early differentiation stage into midbrain neurons using ToF-SIMS imaging. Lipid composition of the native plasma membrane was explored, followed by an examination of the lipid turnover using different isotopically labelled lipid precursors, including 13C-choline, 13C-lauric acid, 15N-linoleic, and 13C-stearic. Our results showed that differentiating NPCs contain a high abundance of ceramides, glycerophosphoserines, neutral glycosphingolipids, diradylglycerols, and glycerophosphocholines at the plasma membrane. In addition, different precursors were found to incorporate into different membrane lipids which are specific for the short- or long-carbon chains, and the unsaturation or saturation stage of the precursors. The lipid structure of neuronal membrane reflects the differentiation status of NPCs, and it can be altered significantly using a particular lipid precursor. Our study illustrates a potential of ToF-SIMS imaging to study native plasma membrane lipids and elucidate complex cellular processes by providing molecular -rich information at a single cell level.

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.

Exploring multisite heterogeneity of human basal cell carcinoma proteome and transcriptome

Basal cell carcinoma (BCC) is the most common type of skin cancer. Due to multiple, potential underlying molecular tumor aberrations, clinical treatment protocols are not well-defined. This study presents multisite molecular heterogeneity profiles of human BCC based on RNA and proteome profiling. Three areas from lesions excised from 9 patients were analyzed. The focus was gene expression profiles based on proteome and RNA measurements of intra-tumor heterogeneity from the same patient and inter-tumor heterogeneity in nodular, infiltrative, and superficial BCC tumor subtypes from different patients. We observed significant overlap in intra- and inter-tumor variability of proteome and RNA expression profiles, showing significant multisite heterogeneity of protein expression in the BCC tumors. Inter-subtype analysis has also identified unique proteins for each BCC subtype. This profiling leads to a deeper understanding of BCC molecular heterogeneity and potentially contributes to developing new sampling tools for personalized diagnostics therapeutic approaches to BCC.

Cell and tissue imaging by secondary ion mass spectrometry

This Tutorial focuses on the use of secondary ion mass spectrometry for the analysis of cellular and tissue samples. The Tutorial aims to cover the considerations in sample preparation analytical set up and some specific aspects of data interpretation associated with such analysis.

Molecular Classifiers in Skin Cancers: Challenges and Promises

Skin cancers are common and heterogenous malignancies affecting up to two in three Australians before age 70. Despite recent developments in diagnosis and therapeutic strategies, the mortality rate and costs associated with managing patients with skin cancers remain high. The lack of well-defined clinical and histopathological features makes their diagnosis and classification difficult in some cases and the prognostication difficult in most skin cancers. Recent advancements in large-scale "omics" studies, including genomics, transcriptomics, proteomics, metabolomics and imaging-omics, have provided invaluable information about the molecular and visual landscape of skin cancers. On many occasions, it has refined tumor classification and has improved prognostication and therapeutic stratification, leading to improved patient outcomes. Therefore, this paper reviews the recent advancements in omics approaches and appraises their limitations and potential for better classification and stratification of skin cancers.

Advancements in ToF-SIMS imaging for life sciences

In the last 2 decades, Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) has gained significant prominence as a powerful imaging technique in the field of life sciences. This comprehensive review provides an in-depth overview of recent advancements in ToF-SIMS instrument technology and its applications in metabolomics, lipidomics, and single-cell analysis. We highlight the use of ToF-SIMS imaging for studying lipid distribution, composition, and interactions in cells and tissues, and discuss its application in metabolomics, including the analysis of metabolic pathways. Furthermore, we review recent progress in single-cell analysis using ToF-SIMS, focusing on sample preparation techniques, in situ investigation for subcellular distribution of drugs, and interactions between drug molecules and biological targets. The high spatial resolution and potential for multimodal analysis of ToF-SIMS make it a promising tool for unraveling the complex molecular landscape of biological systems. We also discuss future prospects and potential advancements of ToF-SIMS in the research of life sciences, with the expectation of a significant impact in the field.

Discriminant Principal Component Analysis of ToF-SIMS Spectra for Deciphering Compositional Differences of MSC-Secreted Extracellular Matrices

Identifying characteristic extracellular matrix (ECM) variants is a key challenge in mechanistic biology, bioengineering, and medical diagnostics. The reported study demonstrates the potential of time-of-flight secondary ion mass spectrometry (ToF-SIMS) to detect subtle differences between human mesenchymal stromal cell (MSC)-secreted ECM types as induced by exogenous stimulation or emerging pathology. ToF-SIMS spectra of decellularized ECM samples are evaluated by discriminant principal component analysis (DPCA), an advanced multivariate analysis technique, to decipher characteristic compositional features. To establish the approach, signatures of major ECM proteins are determined from samples of pre-defined mixtures. Based on that, sets of ECM variants produced by MSCs in vitro are analyzed. Differences in the content of collagen, fibronectin, and laminin in the ECM resulting from the combined supplementation of MSC cultures with polymers that induce macromolecular crowding and with ascorbic acid are detected from the DPCA of ToF-SIMS spectra. The results are verified by immunostaining. Finally, the comparative ToF-SIMS analysis of ECM produced by MSCs of healthy donors and patients suffering from myelodysplastic syndrome display the potential of the novel methodology to reveal disease-associated alterations of the ECM composition.

Review of the Tumor Microenvironment in Basal and Squamous Cell Carcinoma

It is widely known that tumor cells of basal and squamous cell carcinoma interact with the cellular and acellular components of the tumor microenvironment to promote tumor growth and progression. While this environment differs for basal and squamous cell carcinoma, the cellular players within both create an immunosuppressed environment by downregulating effector CD4+ and CD8+ T cells and promoting the release of pro-oncogenic Th2 cytokines. Understanding the crosstalk that occurs within the tumor microenvironment has led to the development of immunotherapeutic agents, including vismodegib and cemiplimab to treat BCC and SCC, respectively. However, further investigation of the TME will provide the opportunity to discover novel treatment options.

Perieccrine Coil Elastosis: An Unusual and Previously Unrecognized Aging-Related Phenomenon

ABSTRACT

Elastic fibers are present as a thin line around the normal secretory coil of eccrine and apocrine glands, although they are virtually imperceptible with hematoxylin-eosin staining. Skin aging is a consequence of intrinsic and extrinsic factors, and glycation and ultraviolet irradiation are involved in this process favoring elastosis. We report an unusual and prominent perieccrine elastosis on the left temple in the vicinity of a basal cell carcinoma in a 78-year old man with type 2 diabetes, dyslipidemia, and hypertension. Very thick multilamellar and tortuous elastic fibers surrounded the eccrine coils. This increased amount of elastic fibers was confirmed by orcein staining as well as amyloid-P and lysozyme immunostaining. Perieccrine coil elastosis is a very unusual phenomenon that to the best of our knowledge has not been reported. Similar to dermal actinic elastosis, the presence of perieccrine coil elastosis in a skin cancer microenvironment might hypothetically promote tumor growth because of the release of elastin-derived peptides.

Nodular and Micronodular Basal Cell Carcinoma Subtypes Are Different Tumors Based on Their Morphological Architecture and Their Interaction with the Surrounding Stroma

Basal cell carcinoma (BCC) is the most frequent cancer of the skin and comprises low-risk and high-risk subtypes. We selected a low-risk subtype, namely, nodular (N), and a high-risk subtype, namely, micronodular (MN), with the aim to identify differences between them using a classical morphometric approach through a gray-level co-occurrence matrix and histogram analysis, as well as an approach based on deep learning semantic segmentation. From whole-slide images, pathologists selected 216 N and 201 MN BCC images. The two groups were then manually segmented and compared based on four morphological areas: center of the BCC islands (tumor, T), peripheral palisading of the BCC islands (touching tumor, TT), peritumoral cleft (PC) and surrounding stroma (S). We found that the TT pattern varied the least, while the PC pattern varied the most between the two subtypes. The combination of two distinct analysis approaches yielded fresh insights into the characterization of BCC, and thus, we were able to describe two different morphological patterns for the T component of the two subtypes.

Evaluation of 6 MALDI-Matrices for 10 μm Lipid Imaging and On-Tissue MSn with AP-MALDI-Orbitrap

Mass spectrometry imaging is a technique uniquely suited to localize and identify lipids in a tissue sample. Using an atmospheric pressure (AP-) matrix-assisted laser desorption ionization (MALDI) source coupled to an Orbitrap Elite, numerous lipid locations and structures can be determined in high mass resolution spectra and at cellular spatial resolution, but careful sample preparation is necessary. We tested 11 protocols on serial brain sections for the commonly used MALDI matrices CHCA, norharmane, DHB, DHAP, THAP, and DAN in combination with tissue washing and matrix additives to determine the lipid coverage, signal intensity, and spatial resolution achievable with AP-MALDI. In positive-ion mode, the most lipids could be detected with CHCA and THAP, while THAP and DAN without additional treatment offered the best signal intensities. In negative-ion mode, DAN showed the best lipid coverage and DHAP performed superiorly for gangliosides. DHB produced intense cholesterol signals in the white matter. One hundred fifty-five lipids were assigned in positive-ion mode (THAP) and 137 in negative-ion mode (DAN), and 76 peaks were identified using on-tissue tandem-MS. The spatial resolution achievable with DAN was 10 μm, confirmed with on tissue line-scans. This enabled the association of lipid species to single neurons in AP-MALDI images. The results show that the performance of AP-MALDI is comparable to vacuum MALDI techniques for lipid imaging.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

Imbalances in TCA, Short Fatty Acids and One-Carbon Metabolisms as Important Features of Homeostatic Disruption Evidenced by a Multi-Omics Integrative Approach of LPS-Induced Chronic Inflammation in Male Wistar Rats

Chronic inflammation is an important risk factor in a broad variety of physical and mental disorders leading to highly prevalent non-communicable diseases (NCDs). However, there is a need for a deeper understanding of this condition and its progression to the disease state. For this reason, it is important to define metabolic pathways and complementary biomarkers associated with homeostatic disruption in chronic inflammation. To achieve that, male Wistar rats were subjected to intraperitoneal and intermittent injections with saline solution or increasing lipopolysaccharide (LPS) concentrations (0.5, 5 and 7.5 mg/kg) thrice a week for 31 days. Biochemical and inflammatory parameters were measured at the end of the study. To assess the omics profile, GC-qTOF and UHPLC-qTOF were performed to evaluate plasma metabolome; 1H-NMR was used to evaluate urine metabolome; additionally, shotgun metagenomics sequencing was carried out to characterize the cecum microbiome. The chronicity of inflammation in the study was evaluated by the monitoring of monocyte chemoattractant protein-1 (MCP-1) during the different weeks of the experimental process. At the end of the study, together with the increased levels of MCP-1, levels of interleukin-6 (IL-6), tumour necrosis factor alpha (TNF-α) and prostaglandin E2 (PGE2) along with 8-isoprostanes (an indicative of oxidative stress) were significantly increased (p-value < 0.05). The leading features implicated in the current model were tricarboxylic acid (TCA) cycle intermediates (i.e., alpha-ketoglutarate, aconitic acid, malic acid, fumaric acid and succinic acid); lipids such as specific cholesterol esters (ChoEs), lysophospholipids (LPCs) and phosphatidylcholines (PCs); and glycine, as well as N, N-dimethylglycine, which are related to one-carbon (1C) metabolism. These metabolites point towards mitochondrial metabolism through TCA cycle, β-oxidation of fatty acids and 1C metabolism as interconnected pathways that could reveal the metabolic effects of chronic inflammation induced by LPS administration. These results provide deeper knowledge concerning the impact of chronic inflammation on the disruption of metabolic homeostasis.

Multiphoton Microscopy and Mass Spectrometry for Revealing Metabolic Heterogeneity of Hepatocytes in vivo

The aim of the investigation was to study the possibility of revealing the heterogeneity of normal liver hepatocytes in terms of metabolic status using the modern methods of multiphoton microscopy and mass spectrometry.

Current Perspectives on the Role of Matrix Metalloproteinases in the Pathogenesis of Basal Cell Carcinoma

Basal cell carcinoma (BCC) is the most common skin malignancy, which rarely metastasizes but has a great ability to infiltrate and invade the surrounding tissues. One of the molecular players involved in the metastatic process are matrix metalloproteinases (MMPs). MMPs are enzymes that can degrade various components of the extracellular matrix. In the skin, the expression of MMPs is increased in response to various stimuli, including ultraviolet (UV) radiation, one of the main factors involved in the development of BCC. By modulating various processes that are linked to tumor growth, such as invasion and angiogenesis, MMPs have been associated with UV-related carcinogenesis. The sources of MMPs are multiple, as they can be released by both neoplastic and tumor microenvironment cells. Inhibiting the action of MMPs could be a useful therapeutic option in BCC management. In this review that reunites the latest advances in this domain, we discuss the role of MMPs in the pathogenesis and evolution of BCC, as molecules involved in tumor aggressiveness and risk of recurrence, in order to offer a fresh and updated perspective on this field.

Mass spectrometry in the lipid study of cancer

Introduction: Cancer is a heterogeneous disease that exploits various metabolic pathways to meet the demand for increased energy and structural components. Lipids are biomolecules that play essential roles as high energy sources, mediators, and structural components of biological membranes. Accumulating evidence has established that altered lipid metabolism is a hallmark of cancer.Areas covered: Mass spectrometry (MS) is a label-free analytical tool that can simultaneously identify and quantify hundreds of analytes. To date, comprehensive lipid studies exclusively rely on this technique. Here, we reviewed the use of MS in the study of lipids in various cancers and discuss its instrumental limitations and challenges.Expert opinion: MS and MS imaging have significantly contributed to revealing altered lipid metabolism in a variety of cancers. Currently, a single MS approach cannot profile the entire lipidome because of its lack of sensitivity and specificity for all lipid classes. For the metabolic pathway investigation, lipid study requires the integration of MS with other molecular approaches. Future developments regarding the high spatial resolution, mass resolution, and sensitivity of MS instruments are warranted.