Spatial differentiation of metabolism in prostate cancer tissue by MALDI-TOF MSI

Emerging roles for fatty acid oxidation in cancer

Fatty acid oxidation (FAO) denotes the mitochondrial aerobic process responsible for breaking down fatty acids (FAs) into acetyl-CoA units. This process holds a central position in the cancer metabolic landscape, with certain tumor cells relying primarily on FAO for energy production. Over the past decade, mounting evidence has underscored the critical role of FAO in various cellular processes such as cell growth, epigenetic modifications, tissue-immune homeostasis, cell signal transduction, and more. FAO is tightly regulated by multiple evolutionarily conserved mechanisms, and any dysregulation can predispose to cancer development. In this view, we summarize recent findings to provide an updated understanding of the multifaceted roles of FAO in tumor development, metastasis, and the response to cancer therapy. Additionally, we explore the regulatory mechanisms of FAO, laying the groundwork for potential therapeutic interventions targeting FAO in cancers within the metabolic landscape.

Large-Scale Metabolite Imaging Gallery of Mouse Organ Tissues to Study Spatial Metabolism

Mass spectrometry imaging (MSI) has revolutionized the study of tissue metabolism by enabling the visualization of small molecule metabolites (SMMs) with high spatial resolution. However, comprehensive SMM imaging databases for different organ tissues are lacking, hindering our understanding of spatial organ metabolism. To address this resource gap, we present a large-scale SMM imaging gallery for mouse brain, kidney, and liver, capturing SMMs spanning eight chemical super classes and encompassing over 40 metabolic pathways. Manual curation and display of these imaging data sets unveil spatial patterns of metabolites that are less documented in the reported organs. Specifically, we identify 65 SMMs in brain coronal sections and 71 in sagittal tissue sections, including spatial patterns for neurotransmitters. Furthermore, we map 98 SMMs in kidneys and 66 SMMs in liver, providing insights into their amino acid and glutathione metabolism. Our insightful SMM imaging gallery serves as a critical resource for the spatial metabolism research community, filling a significant resource gap. This resource is freely available for download and can be accessed through the BioImage Archive and METASPACE repositories, providing high-quality annotated images for potential future computational models and advancing our understanding of tissue metabolism at the spatial level.

Lipid-metabolism-focused CRISPR screens identify enzymes of the mevalonate pathway as essential for prostate cancer growth

Dysregulated lipid metabolism plays an important role in prostate cancer, although the understanding of the essential regulatory processes in tumorigenesis is incomplete. We employ a CRISPR-Cas9 screen using a custom human lipid metabolism knockout library to identify essential genes for prostate cancer survival. Screening in three prostate cancer cell lines reveals 63 shared dependencies, with enrichment in terpenoid backbone synthesis and N-glycan biosynthesis. Independent knockout of key genes of the mevalonate pathway reduces cell proliferation. Further investigation focuses on NUS1, a subunit of cis-prenyltransferase required for dolichol synthesis. NUS1 knockout decreases tumor growth in vivo and viability in patient-derived xenograft (PDX)-derived organoids. Mechanistic studies reveal that loss of NUS1 promotes oxidative stress, lipid peroxidation and ferroptosis sensitivity, endoplasmic reticulum (ER) stress, and G1 cell-cycle arrest, and it dampens androgen receptor (AR) signaling, collectively leading to growth arrest. This study highlights the critical role of the mevalonate-dolichol-N-glycan biosynthesis pathway, particularly NUS1, in prostate cancer survival and growth.

Integrating Ambient Ionization Mass Spectrometry Imaging and Spatial Transcriptomics on the Same Cancer Tissues to Identify RNA-Metabolite Correlations

Innovations in spatial omics technologies applied to human tissues have led to breakthrough discoveries in various diseases, including cancer. Two of these approaches-spatial transcriptomics and spatial metabolomics-have blossomed independently, fueled by technologies such as spatial transcriptomics (ST) and mass spectrometry imaging (MSI). Although powerful, these technologies only offer insights into the spatial distributions of restricted classes of molecules and have not yet been integrated to provide more holistic insights into biological questions. These techniques can be performed on adjacent serial sections from the same sample, but section-to-section variability can convolute data integration. We present a novel method combining desorption electrospray ionization mass spectrometry imaging (DESI-MSI) spatial metabolomics and Visium spatial transcriptomics on the same tissue sections. We show that RNA quality is maintained after performing DESI-MSI on a tissue under ambient conditions and that ST data is unperturbed following DESI-MSI. We demonstrate this workflow on human breast and lung cancer tissues and identify novel correlations between metabolites and mRNA transcripts in cancer-specific tissue regions.

Biobanking after Radical Prostatectomy: Comparison of Slice and Punch Protocols

Background: Biobanking of prostate cancer tissue is crucial for advancing biomarker-guided precision medicine. However, there is no standardized optimal protocol for biobanking prostatectomy specimens. This study aims to compare the representativeness and sustainability of two biobanking protocols-"Punch" and "Slice"-currently used in Norway. Methods: Fresh frozen tissue from 40 radical prostatectomy specimens was biobanked using both the Punch and Slice protocols. Following macroscopic evaluation, a 2 mm thick transverse slice of the prostate (Slice protocol) was collected and stored in an ultra-freezer for future drill biopsy subsampling, guided by histopathological assessment of adjacent formalin-fixed, paraffin-embedded tissue sections. After the slice was collected, five cylindrical tissue samples were punched from the cut surfaces (Punch protocol). Statistical analyses were conducted to compare the sampling precision and time consumption of both protocols. Results: Cancerous tissue was successfully sampled in 87.5% of cases using the Punch protocol and 75% of cases using the Slice protocol. Both methods yielded comparable results in terms of the number of cancerous cores and the ability to sample tissue representing the highest Gleason grade. The mean biobanking time of tissue slices was 4.9 minutes compared to 15.1 minutes for the ready-to-use tissue punches. Both methods have previously been shown to provide high-quality RNA extracts. Conclusion: Both biobanking protocols are effective for sampling prostate cancer tissue, with no significant difference in precision or quality. The choice between protocols should consider factors such as resource availability, tissue quantity, and specific research needs. The Punch protocol is less resource-intensive overall, while the Slice protocol collects vastly more tissue, has a shorter period of ischemia, and provides detailed mapping of biobanked components, allowing for further subsampling at multiple time points.

Metabolic readouts of tumor instructed normal tissues (TINT) identify aggressive prostate cancer subgroups for tailored therapy

Introduction

Prostate cancer (PC) diagnosis relies on histopathological examination of prostate biopsies, which is restricted by insufficient sampling of all tumors present. Including samples from non-PC but tumor instructed normal tissues (TINT) may increase the diagnostic power by displaying the adaptive responses in benign tissues near tumors.

Methods

Here, we applied high-resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) to identify metabolomic biomarkers of possible diagnostic value in benign prostate tissues near low/high-grade tumors.

Results

Benign samples near high-grade tumors (B ISUP 3 + 4) exhibited altered metabolic profiles compared to those close to low-grade tumors (B ISUP 1 + 2). The levels of six metabolites differentiated between the two groups; myo-inositol, lysine, serine and combined signal of lysine/leucine/arginine were increased in benign samples near high-grade tumors (B ISUP 3 + 4) compared to near low-grade tumors (B ISUP 1 + 2), while levels of ethanolamine and lactate were decreased. Additionally, we revealed metabolic differences in non-cancer tissues as a function of their distance to the nearest tumor. Eight metabolites (glutathione, glutamate, combined signal of glutamate/glutamine - glx, glycerol, inosine, ethanolamine, serine and arginine) differentiated between benign tissue located close to the tumor (d ≤ 5 mm) compared to those far away (d ≥ 1 cm).

Conclusion

Our HR MAS NMR-based approach identified metabolic signatures in prostate biopsies that reflect the response of benign tissues to the presence of nearby located tumors in the same prostate and confirmed the power of the TINT concept for improved PC diagnostics and understanding of tumor-tissue interactions.

Metabolic interplays between the tumour and the host shape the tumour macroenvironment

Metabolic reprogramming of cancer cells and the tumour microenvironment are pivotal characteristics of cancers, and studying these processes offer insights and avenues for cancer diagnostics and therapeutics. Recent advancements have underscored the impact of host systemic features, termed macroenvironment, on facilitating cancer progression. During tumorigenesis, these inherent features of the host, such as germline genetics, immune profile and the metabolic status, influence how the body responds to cancer. In parallel, as cancer grows, it induces systemic effects beyond the primary tumour site and affects the macroenvironment, for example, through inflammation, the metabolic end-stage syndrome of cachexia, and metabolic dysregulation. Therefore, understanding the intricate metabolic interplay between the tumour and the host is a growing frontier in advancing cancer diagnosis and therapy. In this Review, we explore the specific contribution of the metabolic fitness of the host to cancer initiation, progression and response to therapy. We then delineate the complex metabolic crosstalk between the tumour, the microenvironment and the host, which promotes disease progression to metastasis and cachexia. The metabolic relationships among the host, cancer pathogenesis and the consequent responsive systemic manifestations during cancer progression provide new perspectives for mechanistic cancer therapy and improved management of patients with cancer.

The tumor microenvironment is an ecosystem sustained by metabolic interactions

Cancer-associated fibroblasts (CAFs) and immune cells make up two major components of the tumor microenvironment (TME), contributing to an ecosystem that can either support or restrain cancer progression. Metabolism is a key regulator of the TME, providing a means for cells to communicate with and influence each other, modulating tumor progression and anti-tumor immunity. Cells of the TME can metabolically interact directly through metabolite secretion and consumption or by influencing other aspects of the TME that, in turn, stimulate metabolic rewiring in target cells. Recent advances in understanding the subtypes and plasticity of cells in the TME both open up new avenues and create challenges for metabolically targeting the TME to hamper tumor growth and improve response to therapy. This perspective explores ways in which the CAF and immune components of the TME could metabolically influence each other, based on current knowledge of their metabolic states, interactions, and subpopulations.

Metabolic characteristics of prostate cancer cells with high metastatic potential revealed by (S)-ethyl 1-(3-(4-chlorophenoxy)-2-hydroxypropyl)-3-(4-methoxyphenyl)-1H-pyrazole-5-carboxylate inhibition

A small molecule, (S)-ethyl 1-(3-(4-chlorophenoxy)-2-hydroxypropyl)-3-(4-methoxyphenyl)-1H-pyrazole-5-carboxylate (SEC), has been reported to be capable of suppressing metastasis of prostate cancer (PCa) cells. In this study, SEC was used to study the metabolic responses of PCa cell lines (LNCaP, PC3, and DU145) with different metastatic potential and the alterations of mTOR, p-mTOR, AMPK, and p-AMPK levels, when the PCa cells were inhibited. The ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based analysis showed that SEC induced the decreases of intracellular metabolites including glutamic acid, glutamine, and histidine (LNCaP); creatinine, citric acid/isocitric acid, and aspartic acid (PC3); and spermidine, S-hydroxymethylglutathione, LPE (20:3), and palmitic amide (DU145), and the increases of intracellular LPC (18:0) (LNCaP); tyrosine, pyroglutamic acid/pyrroline hydroxycarboxylic acid (PC3); and tyrosine, phenylalanine, phenylacetylglycine, spermine, histidine, and choline (DU145). SEC also caused the decrease of extracellular N1-acetylspermidine (LNCaP), erythronic acid/threonic acid (PC3 and DU145), and nicotinic acid/picolinic acid (DU145), and the increase of extracellular 5'-methylthioadenosine (DU145). High metastatic PC3 and DU145 cells exhibited changes in aromatic amino acid metabolism including tyrosine metabolism, phenylalanine, tyrosine, and tryptophan metabolism, and phenylalanine metabolism (PC3 and DU145), TCA cycle (PC3), arginine and proline metabolism, and glycerophospholipid metabolism (DU145), different from the low metastatic LNCaP cells, which had changes in alanine, aspartate, and glutamate metabolism, and arginine biosynthesis. In addition, the levels of p-mTOR and p-AMPK were shown to be obviously downregulated and upregulated, respectively, in high metastatic PC3 and DU145 cells upon SEC inhibition, while this behavior was not detected in LNCaP cells.

Spatial integration of multi-omics data from serial sections using the novel Multi-Omics Imaging Integration Toolset

BACKGROUND

Truly understanding the cancer biology of heterogeneous tumors in precision medicine requires capturing the complexities of multiple omics levels and the spatial heterogeneity of cancer tissue. Techniques like mass spectrometry imaging (MSI) and spatial transcriptomics (ST) achieve this by spatially detecting metabolites and RNA but are often applied to serial sections. To fully leverage the advantage of such multi-omics data, the individual measurements need to be integrated into 1 dataset.

RESULTS

We present the Multi-Omics Imaging Integration Toolset (MIIT), a Python framework for integrating spatially resolved multi-omics data. A key component of MIIT's integration is the registration of serial sections for which we developed a nonrigid registration algorithm, GreedyFHist. We validated GreedyFHist on 244 images from fresh-frozen serial sections, achieving state-of-the-art performance. As a proof of concept, we used MIIT to integrate ST and MSI data from prostate tissue samples and assessed the correlation of a gene signature for citrate-spermine secretion derived from ST with metabolic measurements from MSI.

CONCLUSION

MIIT is a highly accurate, customizable, open-source framework for integrating spatial omics technologies performed on different serial sections.

Spatial-Omics Methods and Applications

Traditional tissue profiling approaches have evolved from bulk studies to single-cell analysis over the last decade; however, the spatial context in tissues and microenvironments has always been lost. Over the last 5 years, spatial technologies have emerged that enabled researchers to investigate tissues in situ for proteins and transcripts without losing anatomy and histology. The field of spatial-omics enables highly multiplexed analysis of biomolecules like RNAs and proteins in their native spatial context-and has matured from initial proof-of-concept studies to a thriving field with widespread applications from basic research to translational and clinical studies. While there has been wide adoption of spatial technologies, there remain challenges with the standardization of methodologies, sample compatibility, throughput, resolution, and ease of use. In this chapter, we discuss the current state of the field and highlight technological advances and limitations.

Prostate cancer risk biomarkers from large cohort and prospective metabolomics studies: A systematic review

Prostate cancer (PCa) is one of the leading causes of cancer-related deaths among men. The heterogeneous nature of this disease presents challenges in its diagnosis, prognosis, and treatment. Numerous potential predictive, diagnostic, prognostic, and risk assessment biomarkers have been proposed through various population studies. However, to date, no metabolite biomarker has been approved or validated for the diagnosis, prognosis, or risk assessment of PCa. Recognizing that systematic reviews of case reports or heterogenous studies cannot reliably establish causality, this review analyzed 29 large prospective metabolomics studies that utilized harmonized criteria for patient selection, consistent methodologies for blood sample collection and storage, data analysis, and that are available in public repositories. By focusing on these large prospective studies, we identified 42 metabolites that were consistently replicated by different authors and across cohort studies. These metabolites have the potential to serve as PCa risk-assessment or predictive biomarkers. A discussion on their associations with dietary sources or dietary patterns is also provided. Further detailed exploration of the relationship with diet, supplement intake, nutrition patterns, contaminants, lifestyle factors, and pre-existing comorbidities that may predispose individuals to PCa is warranted for future research and validation.

PSA, an outdated biomarker for prostate cancer: In search of a more specific biomarker, citrate takes the spotlight

The prevailing biomarker employed for prostate cancer (PCa) screening and diagnosis is the prostate-specific antigen (PSA). Despite excellent sensitivity, PSA lacks specificity, leading to false positives, unnecessary biopsies and overdiagnosis. Consequently, PSA is increasingly less used by clinicians, thus underscoring the imperative for the identification of new biomarkers. An emerging biomarker in this context is citrate, a molecule secreted by the normal prostate, which has been shown to be inversely correlated with PCa. Here, we discuss about PSA and its usage for PCa diagnosis, its lack of specificity, and the various conditions that can affect its levels. We then provide our vision about what we think would be a valuable addition to our PCa diagnosis toolkit, citrate. We describe the unique citrate metabolic program in the prostate and how this profile is reprogrammed during carcinogenesis. Finally, we summarize the evidence that supports the usage of citrate as a biomarker for PCa diagnosis, as it can be measured in various patient samples and be analyzed by several methods. The unique relationship between citrate and PCa, combined with the stability of citrate levels in other prostate-related conditions and the simplicity of its detection, further accentuates its potential as a biomarker.

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Integration of proteomic and metabolomic analysis reveal distinct metabolic alterations of prostate cancer-associated fibroblasts compared to normal fibroblasts from patient's stroma samples

The prostate gland is a complex and heterogeneous organ composed of epithelium and stroma. Whilst many studies into prostate cancer focus on epithelium, the stroma is known to play a key role in disease with the emergence of a cancer-associated fibroblasts (CAF) phenotype associated upon disease progression. In this work, we studied the metabolic rewiring of stromal fibroblasts following differentiation to a cancer-associated, myofibroblast-like, phenotype. We determined that CAFs were metabolically more active compared to normal fibroblasts. This corresponded with a heightened lipogenic metabolism, as both reservoir species and building block compounds. Interestingly, lipid metabolism affects mitochondria functioning yet the mechanisms of lipid-mediated functions are unclear. Data showing oxidised fatty acids and glutathione system are elevated in CAFs, compared to normal fibroblasts, strengthens the hypothesis that increased metabolic activity is related to mitochondrial activity. This manuscript describes mechanisms responsible for the altered metabolic flux and shows that prostate cancer-derived extracellular vesicles can increase basal respiration in normal fibroblasts, mirroring that of the disease-like phenotype. This indicates that extracellular vesicles derived from prostate cancer cells may drive an altered oxygen-dependent metabolism associated to mitochondria in CAFs.

Metabolic imaging across scales reveals distinct prostate cancer phenotypes

Hyperpolarised magnetic resonance imaging (HP-13C-MRI) has shown promise as a clinical tool for detecting and characterising prostate cancer. Here we use a range of spatially resolved histological techniques to identify the biological mechanisms underpinning differential [1-13C]lactate labelling between benign and malignant prostate, as well as in tumours containing cribriform and non-cribriform Gleason pattern 4 disease. Here we show that elevated hyperpolarised [1-13C]lactate signal in prostate cancer compared to the benign prostate is primarily driven by increased tumour epithelial cell density and vascularity, rather than differences in epithelial lactate concentration between tumour and normal. We also demonstrate that some tumours of the cribriform subtype may lack [1-13C]lactate labelling, which is explained by lower epithelial lactate dehydrogenase expression, higher mitochondrial pyruvate carrier density, and increased lipid abundance compared to lactate-rich non-cribriform lesions. These findings highlight the potential of combining spatial metabolic imaging tools across scales to identify clinically significant metabolic phenotypes in prostate cancer.

Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning

Spatially resolved transcriptomics (SRT) has enabled precise dissection of tumor-microenvironment (TME) by analyzing its intracellular molecular networks and intercellular cell-cell communication (CCC). However, lacking computational exploration of complicated relations between cells, genes, and histological regions, severely limits the ability to interpret the complex structure of TME. Here, we introduce stKeep, a heterogeneous graph (HG) learning method that integrates multimodality and gene-gene interactions, in unraveling TME from SRT data. stKeep leverages HG to learn both cell-modules and gene-modules by incorporating features of diverse nodes including genes, cells, and histological regions, allows for identifying finer cell-states within TME and cell-state-specific gene-gene relations, respectively. Furthermore, stKeep employs HG to infer CCC for each cell, while ensuring that learned CCC patterns are comparable across different cell-states through contrastive learning. In various cancer samples, stKeep outperforms other tools in dissecting TME such as detecting bi-potent basal populations, neoplastic myoepithelial cells, and metastatic cells distributed within the tumor or leading-edge regions. Notably, stKeep identifies key transcription factors, ligands, and receptors relevant to disease progression, which are further validated by the functional and survival analysis of independent clinical data, thereby highlighting its clinical prognostic and immunotherapy applications.

Improved on-tissue detection of the anti-cancer agent doxorubicin by quantitative matrix-assisted laser desorption/ionization mass spectrometry imaging

Doxorubicin (dox) is an affordable, and highly effective chemotherapeutic agent used in cancer treatment, yet its application is known to cause cumulative cardiac and renal toxicity. In this study, we employed matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) to evaluate the distribution of dox in mouse heart and kidney after in vivo treatment. To this end, we performed absolute quantification using an isotopically labeled form (13C d3-dox) as an internal standard. Unfortunately, ion suppression often leads to loss of sensitivity in compound detection and can result in hampered drug quantification. To overcome this issue, we developed an on-tissue chemical derivatization (OTCD) method using Girard's reagent T (GirT). With the developed method, dox signal was increased by two orders of magnitude. This optimized sample preparation enabled a sensible gain in dox detection, making it possible to study its distribution and abundance (up to 0.11 pmol/mm2 in the heart and 0.33 pmol/mm2 in the kidney medulla). The optimized approach for on-tissue derivatization and subsequent quantification creates a powerful tool to better understand the relationship between dox exposure (at clinically relevant concentrations) and its biological detrimental effects in various tissues. Overall, this work is a showcase of the added value of MALDI-MSI for pharmaceutical studies to better understand heterogeneity in drug exposure between and within organs.

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.

Multimodal analytical tools for the molecular and elemental characterisation of lesions in brain tissue of multiple sclerosis patients

Multiple sclerosis (MS) is a prevalent immune-mediated inflammatory disease of the central nervous system inducing a widespread degradation of myelin and resulting in neurological deficits. Recent advances in molecular and atomic imaging provide the means to probe the microenvironment in affected brain tissues at an unprecedented level of detail and may provide new insights. This study showcases state-of-the-art spectroscopic and mass spectrometric techniques to compare distributions of molecular and atomic entities in MS lesions and surrounding brain tissues. MS brains underwent post-mortem magnetic resonance imaging (MRI) to locate and subsequently dissect MS lesions and surrounding white matter. Digests of lesions and unaffected white matter were analysed via ICP-MS/MS revealing significant differences in concentrations of Li, Mg, P, K, Mn, V, Rb, Ag, Gd and Bi. Micro x-ray fluorescence spectroscopy (μXRF) and laser ablation - inductively coupled plasma - time of flight - mass spectrometry (LA-ICP-ToF-MS) were used as micro-analytical imaging techniques to study distributions of both endogenous and xenobiotic elements. The essential trace elements Fe, Cu and Zn were subsequently calibrated using in-house manufactured gelatine standards. Lipid distributions were studied using IR-micro spectroscopy and matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI). MALDI-MSI was complemented with high-resolution tandem mass spectrometry and trapped ion mobility spectroscopy for the annotation of specified phospho- and sphingolipids, revealing specific lipid species decreased in MS lesions compared to surrounding white matter. This explorative study demonstrated that modern molecular and atomic mapping techniques provide high-resolution imaging for relevant bio-indicative entities which may complement our current understanding of the underlying pathophysiological processes.

Direct Performance of Triple-Stage Tandem Mass Spectrometry Analysis Using Dual-Direction Dipolar Excitation in a Digital Linear Ion Trap

The ion trap mass spectrometer offers a unique advantage over other mass spectrometers by enabling multistage tandem mass spectrometry analysis with a single mass analyzer. It is employed to generate fragment ions through collision-induced dissociation (CID) usually by applying alternating current (AC) signals to a pair of electrodes for dipole excitation. The process of achieving double-stage tandem mass spectrometry analysis (MS/MS) in the mass spectrometer involves successive stages of injection, cooling, isolation, excitation, and scanning. For triple-stage tandem mass spectrometry analysis (MS/MS/MS), additional stages of isolation, cooling, and excitation need to be added based on the MS/MS analysis, resulting in a complex and time-consuming mass spectrometry workflow. In this study, a digital ion trap technology with the method of simultaneously applying dipole excitation signals to two pairs of electrodes in the ion trap was developed. This allows fragmentation of the precursor ion in one direction while exciting the first-generation product ions in the other direction, enabling direct acquisition of MS/MS/MS spectra. This approach simplifies the process of tandem mass spectrometry, as demonstrated by experimental studies on methamphetamine, which show that dual-direction excitation effectively reduces workflow and enhances the intensity of product ions. Additionally, the method of direct MS/MS/MS spectra achieved through dual-direction excitation in a digital ion trap mass spectrometer allows for a lower q value of the precursor ion owing to a pseudopotential well depth that is 1.648 times greater than that of a traditional sinusoidal ion trap. The experiments of analyzing high concentration n-butyl acetate and isobutyl acetate have shown that the implementation of MS/MS/MS analysis using dual-direction excitation can provide more mass spectral information and effectively distinguish between the two isomeric samples. The results of direct triple-stage spectra obtained by this technique for several typical volatile hazardous chemicals demonstrate the method's capability for rapid analysis and detection of such substances. In summary, the developed method of dual-directional excitation coupled with digital ion trap technology enables direct performance of triple-stage tandem mass spectrometry analysis, improving fragment ion intensities and providing more valuable mass spectral information. It offers advantages such as simplified workflows, faster analysis, and enhanced accuracy for analyzing compounds with low mass fragment ions.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

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.

Quantitative mass spectrometry imaging: therapeutics & biomolecules

Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology.

Monitoring lipid alterations in Drosophila heads in an amyotrophic lateral sclerosis model with time-of-flight secondary ion mass spectrometry

Lipid alterations in the brain are well-documented in disease and aging, but our understanding of their pathogenic implications remains incomplete. Recent technological advances in assessing lipid profiles have enabled us to intricately examine the spatiotemporal variations in lipid compositions within the complex brain characterized by diverse cell types and intricate neural networks. In this study, we coupled time-of-flight secondary ion mass spectrometry (ToF-SIMS) to an amyotrophic lateral sclerosis (ALS) Drosophila model, for the first time, to elucidate changes in the lipid landscape and investigate their potential role in the disease process, serving as a methodological and analytical complement to our prior approach that utilized matrix-assisted laser desorption/ionization mass spectrometry. The expansion of G4C2 repeats in the C9orf72 gene is the most prevalent genetic factor in ALS. Our findings indicate that expressing these repeats in fly brains elevates the levels of fatty acids, diacylglycerols, and ceramides during the early stages (day 5) of disease progression, preceding motor dysfunction. Using RNAi-based genetic screening targeting lipid regulators, we found that reducing fatty acid transport protein 1 (FATP1) and Acyl-CoA-binding protein (ACBP) alleviates the retinal degeneration caused by G4C2 repeat expression and also markedly restores the G4C2-dependent alterations in lipid profiles. Significantly, the expression of FATP1 and ACBP is upregulated in G4C2-expressing flies, suggesting their contribution to lipid dysregulation. Collectively, our novel use of ToF-SIMS with the ALS Drosophila model, alongside methodological and analytical improvements, successfully identifies crucial lipids and related genetic factors in ALS pathogenesis.

PaSTe. Blockade of the Lipid Phenotype of Prostate Cancer as Metabolic Therapy: A Theoretical Proposal

BACKGROUND

Prostate cancer is the most frequently diagnosed malignancy in 112 countries and is the leading cause of death in eighteen. In addition to continuing research on prevention and early diagnosis, improving treatments and making them more affordable is imperative. In this sense, the therapeutic repurposing of low-cost and widely available drugs could reduce global mortality from this disease. The malignant metabolic phenotype is becoming increasingly important due to its therapeutic implications. Cancer generally is characterized by hyperactivation of glycolysis, glutaminolysis, and fatty acid synthesis. However, prostate cancer is particularly lipidic; it exhibits increased activity in the pathways for synthesizing fatty acids, cholesterol, and fatty acid oxidation (FAO).

OBJECTIVE

Based on a literature review, we propose the PaSTe regimen (Pantoprazole, Simvastatin, Trimetazidine) as a metabolic therapy for prostate cancer. Pantoprazole and simvastatin inhibit the enzymes fatty acid synthase (FASN) and 3-hydroxy-3-methylglutaryl- coenzyme A reductase (HMGCR), therefore, blocking the synthesis of fatty acids and cholesterol, respectively. In contrast, trimetazidine inhibits the enzyme 3-β-Ketoacyl- CoA thiolase (3-KAT), an enzyme that catalyzes the oxidation of fatty acids (FAO). It is known that the pharmacological or genetic depletion of any of these enzymes has antitumor effects in prostatic cancer.

RESULTS

Based on this information, we hypothesize that the PaSTe regimen will have increased antitumor effects and may impede the metabolic reprogramming shift. Existing knowledge shows that enzyme inhibition occurs at molar concentrations achieved in plasma at standard doses of these drugs.

CONCLUSION

We conclude that this regimen deserves to be preclinically evaluated because of its clinical potential for the treatment of prostate cancer.

Molecular heterogeneity in prostate cancer and the role of targeted therapy

Data collected from large-scale studies has shown that the incidence of prostate cancer globally is on the rise, which could be attributed to an overall increase in lifespan. So, the question is how has modern science with all its new technologies and clinical breakthroughs mitigated or managed this disease? The answer is not a simple one as prostate cancer exhibits various subtypes, each with its unique characteristics or signatures which creates challenges in treatment. To understand the complexity of prostate cancer these signatures must be deciphered. Molecular studies of prostate cancer samples have identified certain genetic and epigenetic alterations, which are instrumental in tumorigenesis. Some of these candidates include the androgen receptor (AR), various oncogenes, tumor suppressor genes, and the tumor microenvironment, which serve as major drivers that lead to cancer progression. These aberrant genes and their products can give an insight into prostate cancer development and progression by acting as potent markers to guide future therapeutic approaches. Thus, understanding the complexity of prostate cancer is crucial for targeting specific markers and tailoring treatments accordingly.

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

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.

Loss of carnitine palmitoyltransferase 1a reduces docosahexaenoic acid-containing phospholipids and drives sexually dimorphic liver disease in mice

BACKGROUND AND AIMS

Genome and epigenome wide association studies identified variants in carnitine palmitoyltransferase 1a (CPT1a) that associate with lipid traits. The goal of this study was to determine the role of liver-specific CPT1a on hepatic lipid metabolism.

APPROACH AND RESULTS

Male and female liver-specific knockout (LKO) and littermate controls were placed on a low-fat or high-fat diet (60% kcal fat) for 15 weeks. Mice were necropsied after a 16 h fast, and tissues were collected for lipidomics, matrix-assisted laser desorption ionization mass spectrometry imaging, kinome analysis, RNA-sequencing, and protein expression by immunoblotting. Female LKO mice had increased serum alanine aminotransferase levels which were associated with greater deposition of hepatic lipids, while male mice were not affected by CPT1a deletion relative to male control mice. Mice with CPT1a deletion had reductions in DHA-containing phospholipids at the expense of monounsaturated fatty acids (MUFA)-containing phospholipids in whole liver and at the level of the lipid droplet (LD). Male and female LKO mice increased RNA levels of genes involved in LD lipolysis (Plin2, Cidec, G0S2) and in polyunsaturated fatty acid metabolism (Elovl5, Fads1, Elovl2), while only female LKO mice increased genes involved in inflammation (Ly6d, Mmp12, Cxcl2). Kinase profiling showed decreased protein kinase A activity, which coincided with increased PLIN2, PLIN5, and G0S2 protein levels and decreased triglyceride hydrolysis in LKO mice.

CONCLUSIONS

Liver-specific deletion of CPT1a promotes sexually dimorphic steatotic liver disease (SLD) in mice, and here we have identified new mechanisms by which females are protected from HFD-induced liver injury.

A Guide to MALDI Imaging Mass Spectrometry for Tissues

Imaging mass spectrometry is a well-established technology that can easily and succinctly communicate the spatial localization of molecules within samples. This review communicates the recent advances in the field, with a specific focus on matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) applied on tissues. The general sample preparation strategies for different analyte classes are explored, including special considerations for sample types (fresh frozen or formalin-fixed,) strategies for various analytes (lipids, metabolites, proteins, peptides, and glycans) and how multimodal imaging strategies can leverage the strengths of each approach is mentioned. This work explores appropriate experimental design approaches and standardization of processes needed for successful studies, as well as the various data analysis platforms available to analyze data and their strengths. The review concludes with applications of imaging mass spectrometry in various fields, with a focus on medical research, and some examples from plant biology and microbe metabolism are mentioned, to illustrate the breadth and depth of MALDI IMS.

The dual role of citrate in cancer

Citrate is a key metabolite of the Krebs cycle that can also be exported in the cytosol, where it performs several functions. In normal cells, citrate sustains protein acetylation, lipid synthesis, gluconeogenesis, insulin secretion, bone tissues formation, spermatozoid mobility, and immune response. Dysregulation of citrate metabolism is implicated in several pathologies, including cancer. Here we discuss how cancer cells use citrate to sustain their proliferation, survival, and metastatic progression. Also, we propose two paradoxically opposite strategies to reduce tumour growth by targeting citrate metabolism in preclinical models. In the first strategy, we propose to administer in the tumor microenvironment a high amount of citrate, which can then act as a glycolysis inhibitor and apoptosis inducer, whereas the other strategy targets citrate transporters to starve cancer cells from citrate. These strategies, effective in several preclinical in vitro and in vivo cancer models, could be exploited in clinics, particularly to increase sensibility to current anti-cancer agents.

Comparing MS imaging of lipids by WALDI and MALDI: two technologies for evaluating a common ground truth in MS imaging

In this study, we conducted a direct comparison of water-assisted laser desorption ionization (WALDI) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging, with MALDI serving as the benchmark for label-free molecular tissue analysis in biomedical research. Specifically, we investigated the lipidomic profiles of several biological samples and calculated the similarity of detected peaks and Pearson's correlation of spectral profile intensities between the two techniques. We show that, overall, MALDI MS and WALDI MS present very close lipidomic analyses and that the highest similarity is obtained for the norharmane MALDI matrix. Indeed, for norharmane in negative ion mode, the lipidomic spectra revealed 100% similarity of detected peaks and over 0.90 intensity correlation between both technologies for five samples. The MALDI-MSI positive ion lipid spectra displayed more than 83% similarity of detected peaks compared to those of WALDI-MSI. However, we observed a lower percentage (77%) of detected peaks when comparing WALDI-MSI with MALDI-MSI due to the rich WALDI-MSI lipid spectra. Despite this difference, the global lipidomic spectra showed high consistency between the two technologies, indicating that they are governed by similar processes. Thanks to this similarity, we can increase datasets by including data from both modalities to either co-train classification models or obtain cross-interrogation.

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.

Kiss A, Pálóczi K, Sebestyén A, Pesti A, Kiss A, Baghy K, Dezső K, Füle T, Tátrai P, Kovalszky I, Reszegi A. Dynamic Interplay in Tumor Ecosystems: Communication between Hepatoma Cells and Fibroblasts

Tumors are intricate ecosystems where cancer cells and non-malignant stromal cells, including cancer-associated fibroblasts (CAFs), engage in complex communication. In this study, we investigated the interaction between poorly (HLE) and well-differentiated (HuH7) hepatoma cells and LX2 fibroblasts. We explored various communication channels, including soluble factors, metabolites, extracellular vesicles (EVs), and miRNAs. Co-culture with HLE cells induced LX2 to produce higher levels of laminin β1, type IV collagen, and CD44, with pronounced syndecan-1 shedding. Conversely, in HuH7/LX2 co-culture, fibronectin, thrombospondin-1, type IV collagen, and cell surface syndecan-1 were dominant matrix components. Integrins α6β4 and α6β1 were upregulated in HLE, while α5β1 and αVβ1 were increased in HuH7. HLE-stimulated LX2 produced excess MMP-2 and 9, whereas HuH7-stimulated LX2 produced excess MMP-1. LX2 activated MAPK and Wnt signaling in hepatoma cells, and conversely, hepatoma-derived EVs upregulated MAPK and Wnt in LX2 cells. LX2-derived EVs induced over tenfold upregulation of SPOCK1/testican-1 in hepatoma EV cargo. We also identified liver cancer-specific miRNAs in hepatoma EVs, with potential implications for early diagnosis. In summary, our study reveals tumor type-dependent communication between hepatoma cells and fibroblasts, shedding light on potential implications for tumor progression. However, the clinical relevance of liver cancer-specific miRNAs requires further investigation.

Diet, nutrient supply, and tumor immune responses

Nutrients are essential for cell function. Immune cells operating in the complex tumor microenvironment (TME), which has a unique nutrient composition, face challenges of adapting their metabolism to support effector functions. We discuss the impact of nutrient availability on immune function in the tumor, competition between immune cells and tumor cells for nutrients, and how this is altered by diet. Understanding which diets can promote antitumor immune responses could open a new era of treatment, where dietary modifications can be used as an adjunct to boost the success of existing cancer therapies.

Loss of Carnitine Palmitoyltransferase 1a Reduces Docosahexaenoic Acid-Containing Phospholipids and Drives Sexually Dimorphic Liver Disease in Mice

Background and Aims

Genome and epigenome wide association studies identified variants in carnitine palmitoyltransferase 1a (CPT1a) that associate with lipid traits. The goal of this study was to determine the impact by which liver-specific CPT1a deletion impacts hepatic lipid metabolism.

Approach and Results

Six-to-eight-week old male and female liver-specific knockout (LKO) and littermate controls were placed on a low-fat or high-fat diet (HFD; 60% kcal fat) for 15 weeks. Mice were necropsied after a 16 hour fast, and tissues were collected for lipidomics, matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), kinome analysis, RNA-sequencing, and protein expression by immunoblotting. Female LKO mice had increased serum alanine aminotransferase (ALT) levels which were associated with greater deposition of hepatic lipids, while male mice were not affected by CPT1a deletion relative to male control mice. Mice with CPT1a deletion had reductions in DHA-containing phospholipids at the expense of monounsaturated fatty acids (MUFA)-containing phospholipids in both whole liver and at the level of the lipid droplet (LD). Male and female LKO mice increased RNA levels of genes involved in LD lipolysis ( Plin2 , Cidec , G0S2 ) and in polyunsaturated fatty acid (PUFA) metabolism ( Elovl5, Fads1, Elovl2 ), while only female LKO mice increased genes involved in inflammation ( Ly6d, Mmp12, Cxcl2 ). Kinase profiling showed decreased protein kinase A (PKA) activity, which coincided with increased PLIN2, PLIN5, and G0S2 protein levels and decreased triglyceride hydrolysis in LKO mice.

Conclusions

Liver-specific deletion of CPT1a promotes sexually dimorphic steatotic liver disease (SLD) in mice, and here we have identified new mechanisms by which females are protected from HFD-induced liver injury.

Graphical Summary

MALDI-TOF MS: A Promising Analytical Approach to Cancer Diagnostics and Monitoring

Cancer remains the second most common cause of death after cardiovascular diseases, accounting for nearly 10 million deaths in 2020. Although the incidence of cancer increases considerably with age, the cancer burden can also be reduced and have a high chance of cure through early detection, appropriate treatment, and care of patients. The development of high-throughput analytical approaches, like matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), contributes to identifying a pool of proteins/peptides as putative biomarkers for the early detection, diagnosis, and tumor progression. The purpose of the current review is to present an updated outline of recent proteome/peptidome research to establish putative cancer biomarkers using MALDI-TOF MS and highlight the applicability of statistical analysis in the oncology field. The pros and cons of MALDI-TOF MS application on cancer diagnostics and monitoring will be discussed, as well as compared with tandem mass spectrometry (MS/MS)-based proteomics (e.g., liquid chromatography–tandem mass spectrometry). In addition, pre-analytical (e.g., sample quality control) and analytical (e.g., sample pre-treatment, instrumental analytical conditions) properties that influence the robustness of MALDI-TOF MS data will be also discussed.

Spatial Transcriptomic Technologies

Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applications as well as the perspective of the techniques and new emerging technologies that are developed to address current limitations of spatial methodologies. In addition, we describe how spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization.

Spermine is a natural suppressor of AR signaling in castration-resistant prostate cancer

In castration-resistant prostate cancer (CRPC), clinical response to androgen receptor (AR) antagonists is limited mainly due to AR-variants expression and restored AR signaling. The metabolite spermine is most abundant in prostate and it decreases as prostate cancer progresses, but its functions remain poorly understood. Here, we show spermine inhibits full-length androgen receptor (AR-FL) and androgen receptor splice variant 7 (AR-V7) signaling and suppresses CRPC cell proliferation by directly binding and inhibiting protein arginine methyltransferase PRMT1. Spermine reduces H4R3me2a modification at the AR locus and suppresses AR binding as well as H3K27ac modification levels at AR target genes. Spermine supplementation restrains CRPC growth in vivo. PRMT1 inhibition also suppresses AR-FL and AR-V7 signaling and reduces CRPC growth. Collectively, we demonstrate spermine as an anticancer metabolite by inhibiting PRMT1 to transcriptionally inhibit AR-FL and AR-V7 signaling in CRPC, and we indicate spermine and PRMT1 inhibition as powerful strategies overcoming limitations of current AR-based therapies in CRPC.

Prediction of recurrence from metabolites and expression of TOP2A and EZH2 in prostate cancer patients treated with radiotherapy

BACKGROUND

The dual upregulation of TOP2A and EZH2 gene expression has been proposed as a biomarker for recurrence in prostate cancer patients to be treated with radical prostatectomy. A low tissue level of the metabolite citrate has additionally been connected to aggressive disease and recurrence in this patient group. However, for radiotherapy prostate cancer patients, few prognostic biomarkers have been suggested. The main aim of this study was to use an integrated tissue analysis to evaluate metabolites and expression of TOP2A and EZH2 as predictors for recurrence among radiotherapy patients.

METHODS

From 90 prostate cancer patients (56 received neoadjuvant hormonal treatment), 172 transrectal ultrasound-guided (TRUS) biopsies were collected prior to radiotherapy. Metabolic profiles were acquired from fresh frozen TRUS biopsies using high resolution-magic angle spinning MRS. Histopathology and immunohistochemistry staining for TOP2A and EZH2 were performed on TRUS biopsies containing cancer cells (n = 65) from 46 patients, where 24 of these patients (n = 31 samples) received hormonal treatment. Eleven radical prostatectomy cohorts of a total of 2059 patients were used for validation in a meta-analysis.

RESULTS

Among radiotherapy patients with up to 11 years of follow-up, a low level of citrate was found to predict recurrence, p = 0.001 (C-index = 0.74). Citrate had a higher predictive ability compared with individual clinical variables, highlighting its strength as a potential biomarker for recurrence. The dual upregulation of TOP2A and EZH2 was suggested as a biomarker for recurrence, particularly for patients not receiving neoadjuvant hormonal treatment, p = 0.001 (C-index = 0.84). While citrate was a statistically significant biomarker independent of hormonal treatment status, the current study indicated a potential of glutamine, glutamate and choline as biomarkers for recurrence among patients receiving neoadjuvant hormonal treatment, and glucose among patients not receiving neoadjuvant hormonal treatment.

CONCLUSION

Using an integrated approach, our study shows the potential of citrate and the dual upregulation of TOP2A and EZH2 as biomarkers for recurrence among radiotherapy patients.

Engineering the MoS2 /MXene Heterostructure for Precise and Noninvasive Diagnosis of Prostate Cancer with Clinical Specimens

High-throughput metabolic fingerprinting has been deemed one of the most promising strategies for addressing the high false positive rate of prostate cancer (PCa) diagnosis in the prostate-specific antigen (PSA) gray zone. However, the current metabolic fingerprinting remains challenging in achieving high-precision metabolite detection in complex biological samples (e.g., serum and urine). Herein, a novel self-assembly MoS2 /MXene heterostructure nanocomposite with a tailored doping ratio of 10% is presented as a matrix for laser desorption ionization mass spectrometry analysis in clinical biosamples. Notably, owing to the two-dimensional architecture and doping effect, MoS2 /MXene demonstrates favorable laser desorption ionization performance with low adsorption energy, which is evidenced by efficient urinary metabolic fingerprinting with an enhanced area under curve (AUC) diagnosis capability of 0.959 relative to that of serum metabolic fingerprinting (AUC = 0.902) for the diagnosis of PCa in the PSA gray zone. Thus, this MoS2 /MXene heterostructure is anticipated to offer a novel strategy to precisely and noninvasively diagnose PCa in the PSA gray zone.

A multimodal pipeline using NMR spectroscopy and MALDI-TOF mass spectrometry imaging from the same tissue sample

NMR spectroscopy and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) are both commonly used to detect large numbers of metabolites and lipids in metabolomic and lipidomic studies. We have demonstrated a new workflow, highlighting the benefits of both techniques to obtain metabolomic and lipidomic data, which has realized for the first time the combination of these two complementary and powerful technologies. NMR spectroscopy is frequently used to obtain quantitative metabolite information from cells and tissues. Lipid detection is also possible with NMR spectroscopy, with changes being visible across entire classes of molecules. Meanwhile, MALDI MSI provides relative measures of metabolite and lipid concentrations, mapping spatial information of many specific metabolite and lipid molecules across cells or tissues. We have used these two complementary techniques in combination to obtain metabolomic and lipidomic measurements from triple-negative human breast cancer cells and tumor xenograft models. We have emphasized critical experimental procedures that ensured the success of achieving NMR spectroscopy and MALDI MSI in a combined workflow from the same sample. Our data show that several phospholipid metabolite species were differentially distributed in viable and necrotic regions of breast tumor xenografts. This study emphasizes the power of combined NMR spectroscopy-MALDI imaging to advance metabolomic and lipidomic studies.

High-multiplex tissue imaging in routine pathology-are we there yet?

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ∼80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. We show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

MALDI Imaging Mass Spectrometry of High-Grade Gliomas: A Review of Recent Progress and Future Perspective

Glioblastoma (GBM) is the most common malignancy of the brain with a relatively short median survival and high mortality. Advanced age, high socioeconomic status, exposure to ionizing radiation, and other factors have been correlated with an increased incidence of GBM, while female sex hormones, history of allergies, and frequent use of specific drugs might exert protective effects against this disease. However, none of these explain the pathogenesis of GBM. The most recent WHO classification of CNS tumors classifies neoplasms based on their histopathological and molecular characteristics. Modern laboratory techniques, such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, enable the comprehensive metabolic analysis of the tissue sample. MALDI imaging is able to characterize the spatial distribution of a wide array of biomolecules in a sample, in combination with histological features, without sacrificing the tissue integrity. In this review, we first provide an overview of GBM epidemiology, risk, and protective factors, as well as the recent WHO classification of CNS tumors. We then provide an overview of mass spectrometry workflow, with a focus on MALDI imaging, and recent advances in cancer research. Finally, we conclude the review with studies of GBM that utilized MALDI imaging and offer our perspective on future research.

Sample Preparation for Metabolite Detection in Mass Spectrometry Imaging

Metabolites reflect the biological state of cells and tissue, and metabolomics is therefore a field of high interest both to understand normal physiological functions and disease development. When studying heterogeneous tissue samples, mass spectrometry imaging (MSI) is a valuable tool as it conserves the spatial distribution of analytes on tissue sections. A large proportion of metabolites are, however, small and polar, making them vulnerable to delocalizing through diffusion during sample preparation. Here we present a sample preparation method optimized to limit diffusion and delocalization of small polar metabolites in fresh frozen tissue sections. This sample preparation protocol includes cryosectioning, vacuum frozen storage, and matrix application. The methods described were primely developed for matrix-assisted laser desorption/ionization (MALDI) MSI, but the protocol describing cryosectioning and vacuum freezing storage can also be applied before desorption electrospray ionization (DESI) MSI. Our vacuum drying and vacuum packing approach offers a particular advantage to limit delocalization and safe storage.

Emerging metabolomic tools to study cancer metastasis

Metastasis is responsible for 90% of deaths in patients with cancer. Understanding the role of metabolism during metastasis has been limited by the development of robust and sensitive technologies that capture metabolic processes in metastasizing cancer cells. We discuss the current technologies available to study (i) metabolism in primary and metastatic cancer cells and (ii) metabolic interactions between cancer cells and the tumor microenvironment (TME) at different stages of the metastatic cascade. We identify advantages and disadvantages of each method and discuss how these tools and technologies will further improve our understanding of metastasis. Studies investigating the complex metabolic rewiring of different cells using state-of-the-art metabolomic technologies have the potential to reveal novel biological processes and therapeutic interventions for human cancers.