Spatially resolved metabolomics combined with multicellular tumor spheroids to discover cancer tissue relevant metabolic signatures

Metabolic adaptation in epithelial ovarian cancer metastasis

Epithelial ovarian cancer (EOC) is highly lethal due to its unique metastatic characteristics. EOC spheroids enter a non-proliferative state, with hypoxic cores and reduced oncogenic signaling, all of which contribute to tumour dormancy during metastasis. We investigated the metabolomic states of EOC cells progressing through the three steps to metastasis. Metabolomes of adherent, spheroid, and re-adherent cells were validated by isotopic metabolic flux analysis and mitochondrial functional assays to identify metabolic pathways that were previously unknown to promote EOC metastasis. Although spheroids were thought to exist in a dormant state, metabolomic analysis revealed an unexpected upregulation of energy production pathways in spheroids, accompanied by increased abundance of tricarboxylic acid (TCA) cycle and electron transport chain proteins. Tracing of 13C-labelled glucose and glutamine showed increased pyruvate carboxylation and decreased glutamine anaplerosis in spheroids. Increased reductive carboxylation suggests spheroids adjust redox homeostasis by shuttling cytosolic NADPH into mitochondria via isocitrate dehydrogenase. Indeed, we observed spheroids have increased respiratory capacity and mitochondrial ATP production. Relative to adherent cells, spheroids reduced serine consumption and metabolism, processes which were reversed upon spheroid re-adherence. The data reveal a distinct metabolism in EOC spheroids that enhances energy production by the mitochondria while maintaining a dormant state with respect to growth and proliferation. The findings advance our understanding of EOC metastasis and identify the TCA cycle and mitochondrional activity as novel targets to disrupt EOC metastasis, providing new approaches to treat advanced disease.

Bridging the gap: advancing cancer cell culture to reveal key metabolic targets

Metabolic rewiring is a defining characteristic of cancer cells, driving their ability to proliferate. Leveraging these metabolic vulnerabilities for therapeutic purposes has a long and impactful history, with the advent of antimetabolites marking a significant breakthrough in cancer treatment. Despite this, only a few in vitro metabolic discoveries have been successfully translated into effective clinical therapies. This limited translatability is partially due to the use of simplistic in vitro models that do not accurately reflect the tumor microenvironment. This Review examines the effects of current cell culture practices on cancer cell metabolism and highlights recent advancements in establishing more physiologically relevant in vitro culture conditions and technologies, such as organoids. Applying these improvements may bridge the gap between in vitro and in vivo findings, facilitating the development of innovative metabolic therapies for cancer.

Spatial Metabolomics Profiling Reveals Curcumin Induces Metabolic Reprogramming in Three-Dimensional Tumor Spheroids

Curcumin is widely recognized for its diverse antitumor properties, ranging from breast cancer to many other types of cancers. However, its role in the tumor microenvironment remains to be elucidated. In this study, we established a 3D tumor spheroids model that can simulate the growth environment of tumor cells and visualized the antitumor metabolic alteration caused by curcumin using mass spectrometry imaging technology. Our results showed that curcumin not only exerts a profound impact on the growth and proliferation of breast cancer cells but in situ multivariate statistical analysis also reveals the significant effect on the overall metabolic profile of tumor spheroids. Meanwhile, our visualization map characterized curcumin metabolic processes of reduction and glucuronidation in tumor spheroids. More importantly, abnormal metabolic pathways related to lipid metabolism and polyamine metabolism were also remodeled at the metabolite and gene levels after curcumin intervention. These insights deepen our comprehension of the regulatory mechanism of curcumin on the tumor metabolic network, furnishing powerful references for antitumor treatment.

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.

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.

Liquid chromatography-mass spectrometry-based metabolomics and fluxomics reveals the metabolic alterations in glioma U87MG multicellular tumor spheroids versus two-dimensional cell cultures

RATIONALE

Multicellular tumor spheroids (MCTSs) that reconstitute the metabolic characteristics of in vivo tumor tissue may facilitate the discovery of molecular biomarkers and effective anticancer therapies. However, little is known about how cancer cells adapt their metabolic changes in complex three-dimensional (3D) microenvironments. Here, using the two-dimensional (2D) cell model as control, the metabolic phenotypes of glioma U87MG multicellular tumor spheroids were systematically investigated based on static metabolomics and dynamic fluxomics analysis.

METHODS

A liquid chromatography-mass spectrometry-based global metabolomics and lipidomics approach was adopted to survey the cellular samples from 2D and 3D culture systems, revealing marked molecular differences between them. Then, by means of metabolomic pathway analysis, the metabolic pathways altered in glioma MCTSs were found using 13 C6 -glucose as a tracer to map the metabolic flux of glycolysis, the tricarboxylic acid (TCA) cycle, de novo nucleotide synthesis, and de novo lipid biosynthesis in the MCTS model.

RESULTS

We found nine metabolic pathways as well as glycerolipid, glycerophospholipid and sphingolipid metabolism to be predominantly altered in glioma MCTSs. The reduced nucleotide metabolism, amino acid metabolism and glutathione metabolism indicated an overall lower cellular activity in MCTSs. Through dynamic fluxomics analysis in the MCTS model, we found that cells cultured in MCTSs exhibited increased glycolysis activity and de novo lipid biosynthesis activity, and decreased the TCA cycle and de novo purine nucleotide biosynthesis activity.

CONCLUSIONS

Our study highlights specific, altered biochemical pathways in MCTSs, emphasizing dysregulation of energy metabolism and lipid metabolism, and offering novel insight into metabolic events in glioma MCTSs.

Lipid metabolism analysis in esophageal cancer and associated drug discovery

Esophageal cancer is an upper gastrointestinal malignancy with a bleak prognosis. It is still being explored in depth due to its complex molecular mechanisms of occurrence and development. Lipids play a crucial role in cells by participating in energy supply, biofilm formation, and signal transduction processes, and lipid metabolic reprogramming also constitutes a significant characteristic of malignant tumors. More and more studies have found esophageal cancer has obvious lipid metabolism abnormalities throughout its beginning, progress, and treatment resistance. The inhibition of tumor growth and the enhancement of antitumor therapy efficacy can be achieved through the regulation of lipid metabolism. Therefore, we reviewed and analyzed the research results and latest findings for lipid metabolism and associated analysis techniques in esophageal cancer, and comprehensively proved the value of lipid metabolic reprogramming in the evolution and treatment resistance of esophageal cancer, as well as its significance in exploring potential therapeutic targets and biomarkers.

Spatially Resolved Metabolomics Combined with the 3D Tumor-Immune Cell Coculture Spheroid Highlights Metabolic Alterations during Antitumor Immune Response

The metabolic cross-talk between tumor and immune cells plays key roles in immune cell function and immune checkpoint blockade therapy. However, the characterization of tumor immunometabolism and its spatiotemporal alterations during immune response in a complex tumor microenvironment is challenging. Here, a 3D tumor-immune cell coculture spheroid model was developed to mimic tumor-immune interactions, combined with mass spectrometry imaging-based spatially resolved metabolomics to visualize tumor immunometabolic alterations during immune response. The inhibition of T cells was simulated by coculturing breast tumor spheroids with Jurkat T cells, and the reactivation of T cells can be monitored through diminishing cancer PD-L1 expressions by berberine. This system enables simultaneously screening and imaging discriminatory metabolites that are altered during T cell-mediated antitumor immune response and characterizing the distributions of berberine and its metabolites in tumor spheroids. We discovered that the transport and catabolism of glutamine were significantly reprogrammed during the antitumor immune response at both metabolite and enzyme levels, corresponding to its indispensable roles in energy metabolism and building new biomass. The combination of spatially resolved metabolomics with the 3D tumor-immune cell coculture spheroid visually reveals metabolic interactions between tumor and immune cells and possibly helps decipher the role of immunometabolic alterations in tumor immunotherapy.

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.

Recent Advances in Mass Spectrometry-Based Spatially Resolved Molecular Imaging of Drug Disposition and Metabolomics

Mass spectrometric imaging is a nontargeted, tag-free, high-throughput, and highly responsive analytical approach. The highly accurate molecular visualization detection technology enables qualitative and quantitative analyses of biologic tissues or cells scanned by mass spectrometry in situ, extracting known and unknown multiple compounds, and simultaneously assessing relative contents of targeting molecules by monitoring their molecular ions and pinpointing the spatial locations of those molecules distributed. Five mass spectrometric imaging techniques and their characteristics are introduced in the review, including matrix-assisted laser desorption ionization mass spectrometry, secondary ion mass spectrometry, desorption electrospray ionization mass spectrometry, laser ablation electrospray ionization mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry. The mass spectrometry-based techniques provide the possibility for spatial metabolomics with the capability of high throughput and precision detection. The approaches have been widely employed to spatially image not only metabolome of endogenous amino acids, peptides, proteins, neurotransmitters, and lipids but also the disposition of exogenous chemicals, such as pharmaceutical agents, environmental pollutants, toxicants, natural products, and heavy metals. The techniques also provide us with spatial distribution imaging of analytes in single cells, tissue microregions, organs, and whole animals. SIGNIFICANCE STATEMENT: The review article includes an overview of five commonly used mass spectrometers for spatial imaging and describes the advantages and disadvantages of each. Examples of the technology applications cover drug disposition, diseases, and omics. Technical aspects of relative and absolute quantification by mass spectrometric imaging and challenges for future new applications are discussed as well. The reviewed knowledge may benefit the development of new drugs and provide a better understanding of biochemical processes related to physiology and diseases.

Serum Metabolite Biomarkers for Pancreatic Tumors: Neuroendocrine and Pancreatic Ductal Adenocarcinomas-A Preliminary Study

BACKGROUND

Pancreatic cancer is the most common pancreatic solid malignancy with an aggressive clinical course and low survival rate. There are a limited number of reliable prognostic biomarkers and a need to understand the pathogenesis of pancreatic tumors; neuroendocrine (PNET) and pancreatic ductal adenocarcinomas (PDAC) encouraged us to analyze the serum metabolome of pancreatic tumors and disturbances in the metabolism of PDAC and PNET.

METHODS

Using the AbsoluteIDQ^® p180 kit (Biocrates Life Sciences AG, Innsbruck, Austria) with liquid chromatography-mass spectrometry (LC-MS), we identified changes in metabolite profiles and disrupted metabolic pathways serum of NET and PDAC patients.

RESULTS

The concentration of six metabolites showed statistically significant differences between the control group and PDAC patients (p.adj < 0.05). Glutamine (Gln), acetylcarnitine (C2), and citrulline (Cit) presented a lower concentration in the serum of PDAC patients, while phosphatidylcholine aa C32:0 (PC aa C32:0), sphingomyelin C26:1 (SM C26:1), and glutamic acid (Glu) achieved higher concentrations compared to serum samples from healthy individuals. Five of the tested metabolites: C2 (FC = 8.67), and serotonin (FC = 2.68) reached higher concentration values in the PNET serum samples compared to PDAC, while phosphatidylcholine aa C34:1 (PC aa C34:1) (FC = -1.46 (0.68)) had a higher concentration in the PDAC samples. The area under the curves (AUC) of the receiver operating characteristic (ROC) curves presented diagnostic power to discriminate pancreatic tumor patients, which were highest for acylcarnitines: C2 with AUC = 0.93, serotonin with AUC = 0.85, and PC aa C34:1 with AUC = 0.86.

CONCLUSIONS

The observations presented provide better insight into the metabolism of pancreatic tumors, and improve the diagnosis and classification of tumors. Serum-circulating metabolites can be easily monitored without invasive procedures and show the present clinical patients' condition, helping with pharmacological treatment or dietary strategies.

Integrated spatial transcriptome and metabolism study reveals metabolic heterogeneity in human injured brain

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain. With the advances of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of spatial organization and functional orientation. Here, we generate spatial transcriptomes and metabolites from six patients with brain trauma with surgical samples. The resulting spatial marker gene, which is highly replicable across analysis methods, sequencing technologies, and modalities, is a comprehensive molecular marker of the diverse metabolic changes in human injured brains. The atlas includes an area of lipid peroxidation that resembles injured neurons in the brain. We further discover imbalanced myo-inositol and myo-inositol phosphate and related spatial markers. Our results highlight the complex transcriptomic regulation and metabolic alterations in the injured brain and will directly enable the design of reagents to target specific genes in the human brain for functional analysis.

Spatiotemporal pharmacometabolomics based on ambient mass spectrometry imaging to evaluate the metabolism and hepatotoxicity of amiodarone in HepG2 spheroids

Three-dimensional (3D) cell spheroid models combined with mass spectrometry imaging (MSI) enables innovative investigation of in vivo-like biological processes under different physiological and pathological conditions. Herein, airflow-assisted desorption electrospray ionization-MSI (AFADESI-MSI) was coupled with 3D HepG2 spheroids to assess the metabolism and hepatotoxicity of amiodarone (AMI). High-coverage imaging of >1100 endogenous metabolites in hepatocyte spheroids was achieved using AFADESI-MSI. Following AMI treatment at different times, 15 metabolites of AMI involved in N-desethylation, hydroxylation, deiodination, and desaturation metabolic reactions were identified, and according to their spatiotemporal dynamics features, the metabolic pathways of AMI were proposed. Subsequently, the temporal and spatial changes in metabolic disturbance within spheroids caused by drug exposure were obtained via metabolomic analysis. The main dysregulated metabolic pathways included arachidonic acid and glycerophospholipid metabolism, providing considerable evidence for the mechanism of AMI hepatotoxicity. In addition, a biomarker group of eight fatty acids was selected that provided improved indication of cell viability and could characterize the hepatotoxicity of AMI. The combination of AFADESI-MSI and HepG2 spheroids can simultaneously obtain spatiotemporal information for drugs, drug metabolites, and endogenous metabolites after AMI treatment, providing an effective tool for in vitro drug hepatotoxicity evaluation.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.

Unravelling the enigma of siRNA and aptamer mediated therapies against pancreatic cancer

Pancreatic cancer (PC) is a fatal disease that has a poor 5-year survival rate. The poor prognosis can be attributed to both troublesome detections at the initial stage, which makes the majority of the treatment options largely unsuccessful and leads to extensive metastasis, as well as to its distinct pathophysiological characteristics, such as rich desmoplastic tumours bounded by dysplastic and hypo perfused vessels restricting the mobility of therapeutic agents. Continued attempts have been made to utilise innovative measures for battling PC to increase the therapeutic effectiveness of therapies and overcome their cytotoxicity. Combined cancer targeting and gene silencing approach has shown improved outcomes in patients' survival rates and quality of life, offering a potential solution to therapeutic complications. It particularly targets various barriers to alleviate delivery problems and diminish tumour recurrence and metastasis. While aptamers, a type of single-stranded nucleic acids with strong binding affinity and specificity to target molecules, have recently surfaced as a viable PC strategy, siRNA can interfere with the expression of certain genes. By concurrently suppressing genes and boosting targeted approach, the cocktail of siRNA/Aptamer and other therapeutic drugs can circumvent the multi-drug resistance phenomena. Additionally, combination therapy with additive or synergistic effects can considerably increase the therapeutic efficacy of anti-cancer medications. This study outlines the primary difficulties in treating PC, along with recent developments in siRNA/Aptamer mediated drug delivery to solve the major hiccup of oncology field.

Applications of spatially resolved omics in the field of endocrine tumors

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

Multimodal Mass Spectrometry Imaging of an Aggregated 3D Cell Culture Model

Multimodal mass spectrometry imaging (MSI) is a leading approach for investigating the molecular processes within biological samples. The parallel detection of compounds including metabolites, lipids, proteins, and metal isotopes allows for a more holistic understanding of tissue microenvironments. Universal sample preparation is a primary enabler for samples of the same set to be run across multiple modalities. Using the same method and materials for a cohort of samples reduces any potential variability during sample preparation and allows for comparable analysis across analytical imaging techniques. Here, the MSI workflow is describing a sample preparation protocol for the analysis of three-dimensional (3D) cell culture models. The analysis of biologically relevant cultures by multimodal MSI offers a method in which models of cancer and disease can be studied for the use in early-stage drug development.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Integrated spatially resolved metabolomics and network toxicology to investigate the hepatotoxicity mechanisms of component D of Polygonum multiflorum Thunb

ETHNOPHARMACOLOGICAL RELEVANCE

The liver toxicity of Reynoutria multiflora (Thunb.) Moldenke. (Polygonaceae) (Polygonum multiflorum Thunb, PM) has always attracted much attention, but the related toxicity materials and mechanisms have not been elucidated due to multi-component and multi-target characteristics. In previous hepatotoxicity screening, different components of PM were first evaluated and the hepatotoxicity of component D [95% ethanol (EtOH) elution] in a 70% EtOH extract of PM (PM-D) showed the highest hepatotoxicity. Furthermore, the main components of PM-D were identified and their hepatotoxicity was evaluated based on a zebrafish embryo model. However, the hepatotoxicity mechanism of PM-D is unknown.

AIM OF THE STUDY

This work is to explore the hepatotoxicity mechanisms of PM-D by integrating network toxicology and spatially resolved metabolomics strategy.

MATERIALS AND METHODS

A hepatotoxicity interaction network of PM-D was constructed based on toxicity target prediction for eight key toxic ingredients and a hepatotoxicity target collection. Then the key signaling pathways were enriched, and molecular docking verification was implemented to evaluate the ability of toxic ingredients to bind to the core targets. The pathological changes of liver tissues and serum biochemical assays of mice were used to evaluate the liver injury effect of mice with oral administration of PM-D. Furthermore, spatially resolved metabolomics was used to visualize significant differences in metabolic profiles in mice after drug administration, to screen hepatotoxicity-related biomarkers and analyze metabolic pathways.

RESULTS

The contents of four key toxic compounds in PM-D were detected. Network toxicology identified 30 potential targets of liver toxicity of PM-D. GO and KEGG enrichment analyses indicated that the hepatotoxicity of PM-D involved multiple biological activities, including cellular response to endogenous stimulus, organonitrogen compound metabolic process, regulation of the apoptotic process, regulation of kinase, regulation of reactive oxygen species metabolic process and signaling pathways including PI3K-Akt, AMPK, MAPK, mTOR, Ras and HIF-1. The molecular docking confirmed the high binding activity of 8 key toxic ingredients with 10 core targets, including mTOR, PIK3CA, AKT1, and EGFR. The high distribution of metabolites of PM-D in the liver of administrated mice was recognized by mass spectrometry imaging. Spatially resolved metabolomics results revealed significant changes in metabolic profiles after PM-D administration, and metabolites such as taurine, taurocholic acid, adenosine, and acyl-carnitines were associated with PM-D-induced liver injury. Enrichment analyses of metabolic pathways revealed tht linolenic acid and linoleic acid metabolism, carnitine synthesis, oxidation of branched-chain fatty acids, and six other metabolic pathways were significantly changed. Comprehensive analysis revealed that the hepatotoxicity caused by PM-D was closely related to cholestasis, mitochondrial damage, oxidative stress and energy metabolism, and lipid metabolism disorders.

CONCLUSIONS

In this study, the hepatotoxicity mechanisms of PM-D were comprehensively identified through an integrated spatially resolved metabolomics and network toxicology strategy, providing a theoretical foundation for the toxicity mechanisms of PM and its safe clinical application.

Advanced Research in Cellular Pharmacokinetics and its Cutting-edge Technologies

Pharmacokinetics (PK), as a significant part of pharmacology, runs through the overall process of the preclinical and clinical research on drugs and plays a significant role in determining the material basis of efficacy and mechanism research. However, due to the limitations of classical PK, cellular PK was put forward and developed rapidly. Many novel and original technologies have been innovatively applied to cellular PK research, thereby providing powerful technical support. As a novel field of PK research, cellular PK expands the research object and enriches the theoretical framework of PK. It provides a new perspective for elucidating the mechanism of drug action and the dynamic process of drug in the body. Furthermore, it provides a scientific basis and guiding significance for the development of new drugs and clinical rational drug use. Cellular PK can explain the dynamic process of certain drugs (e.g., antineoplastic drugs and antibiotics) and the disposition kinetics characteristics in some specific tissues (e.g., brain and tumor) in a clearer and more accurate manner. It is a beneficial supplement and the perfection of traditional PK. In the future, traditional and cellular PKs will complement each other well and improve into an all-around research system in drug developments. Briefly, this paper reviews the conceptual development of cellular PK and key associated technologies, explores its main functions and applications, and looks forward to the important pioneering significance and promising value for the development of PK.

Three-Dimensional Mass Spectrometry Imaging Reveals Distributions of Lipids and the Drug Metabolite Associated with the Enhanced Growth of Colon Cancer Cell Spheroids Treated with Triclosan

The application of mass spectrometry imaging (MSI) to explore the responses of cancer cell spheroids (CCS) after treatment of exogenous molecules has attracted growing attention. Increasing studies have utilized MSI to image the two-dimensional distributions of exogenous and endogenous molecules in planar CCS sections. However, because CCS are volumetric and heterogenous, maintaining their three-dimensional (3D) information is essential for acquiring a better understanding of the tumor microenvironment and mechanisms of action of exogenous molecules. Here, an established method of 3D MSI was applied to distinguish the distributions of triclosan sulfate and endogenous lipids in three microregions of colon CCS with an enhanced growth induced by the treatment of triclosan, a common antimicrobial agent. The results of 3D MSI showed that triclosan sulfate gradually accumulated from the periphery to the entire structure of CCS and finally localized in the core region. Spatial lipidomics analysis revealed that the upregulated phosphatidylethanolamine (fold change (FD) = 1.26, p = 0.0021), phosphatidylinositol (FD = 1.17, p = 0.0180), and phosphatidylcholine (FD = 1.22, p = 0.0178) species mainly distributed in the outer proliferative region, while the upregulated sphingomyelin (FD = 1.18, p = 0.024) species tended to distribute in the inner necrotic region. Our results suggest that a competitive mechanism between inhibiting and promoting CCS growth might be responsible for the proliferation of CCS treated with triclosan.

Advances in mass spectrometry imaging for toxicological analysis and safety evaluation of pharmaceuticals

Safety issues caused by pharmaceuticals have frequently occurred worldwide, posing a tremendous threat to human health. As an essential part of drug development, the toxicological analysis and safety evaluation is of great significance. In addition, the risk of pharmaceuticals accumulation in the environment and the monitoring of the toxicity from natural medicines have also received ongoing concerns. Due to a lack of spatial distribution information provided by common analytical methods, analyses that provide spatial dimensions could serve as complementary safety evaluation methods for better prediction and evaluation of drug toxicity. With advances in technical solutions and software algorithms, mass spectrometry imaging (MSI) has received increasing attention as a popular analytical tool that enables the simultaneous implementation of qualitative, quantitative, and localization without complex sample pretreatment and labeling steps. In recent years, MSI has become more attractive, powerful, and sensitive and has been applied in several scientific fields that can meet the safety assessment requirements. This review aims to cover a detailed summary of the various MSI technologies utilized in the biomedical and pharmaceutical area, including technical principles, advantages, current status, and future trends. Representative applications and developments in the safety-related issues of different pharmaceuticals and natural medicines are also described to provide a reference for pharmaceutical research, improve rational clinical medicine use, and ensure public safety.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with esophageal cancer

Metabolomics has been reported as an efficient tool to screen biomarkers that are related to esophageal cancer. However, the metabolic biomarkers identifying malignant degrees and therapeutic efficacy are still largely unknown in the disease. Here, GC-MS-based metabolomics was used to understand metabolic alteration in 137 serum specimens from patients with esophageal cancer, which is approximately two- to fivefold as many plasma specimens as the previous reports. The elevated amino acid metabolism is in sharp contrast to the reduced carbohydrate as a characteristic feature of esophageal cancer. Comparative metabolomics showed that most metabolic differences were determined between the early stage (0–II) and the late stage (III and IV) among the 0–IV stages of esophageal cancer and between patients who received treatment and those who did not receive treatment. Glycine, serine, and threonine metabolism and glycine were identified as the potentially overlapped metabolic pathway and metabolite, respectively, in both disease progress and treatment effect. Glycine, fructose, ornithine, and threonine can be a potential array for the evaluation of disease prognosis and therapy in esophageal cancer. These results highlight the means of identifying previously unknown biomarkers related to esophageal cancer by a metabolomics approach.

Lipidomic comparison of 2D and 3D colon cancer cell culture models

Altered lipid metabolism is one of the hallmarks of cancer. Cellular proliferation and de novo synthesis of lipids are related to cancer progression. In this study, we evaluated the lipidomic profile of two-dimensional (2D) monolayer and multicellular tumor spheroids from the HCT 116 colon carcinoma cell line. We utilized serial trypsinization on the spheroid samples to generate three cellular populations representing the proliferative, quiescent, and necrotic regions of the spheroid. This analysis enabled a comprehensive identification and quantification of lipids produced in each of the spheroid layer and 2D cultures. We show that lipid subclasses associated with lipid droplets form in oxygen-restricted and acidic regions of spheroids and are produced at higher levels than in 2D cultures. Additionally, sphingolipid production, which is implicated in cell death and survival pathways, is higher in spheroids relative to 2D cells. Finally, we show that increased numbers of lipids composed of polyunsaturated fatty acids (PUFAs) are produced in the quiescent and necrotic regions of the spheroid. The lipidomic signature for each region and cell culture type highlights the importance of understanding the spatial aspects of cancer biology. These results provide additional lipid biomarkers in colon cancer cells that can be further studied to target pivotal lipid production pathways.

Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies

Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.

Comparison of Osteosarcoma Aggregated Tumour Models with Human Tissue by Multimodal Mass Spectrometry Imaging

Osteosarcoma (OS) is the most common primary bone malignancy and largely effects adolescents and young adults, with 60% of patients under the age of 25. There are multiple cell models of OS described in vitro that express the specific genetic alterations of the sarcoma. In the work reported here, multiple mass spectrometry imaging (MSI) modalities were employed to characterise two aggregated cellular models of OS models formed using the MG63 and SAOS-2 cell lines. Phenotyping of the metabolite activity within the two OS aggregoid models was achieved and a comparison of the metabolite data with OS human tissue samples revealed relevant fatty acid and phospholipid markers. Although, annotations of these species require MS/MS analysis for confident identification of the metabolites. From the putative assignments however, it was suggested that the MG63 aggregoids are an aggressive tumour model that exhibited metastatic-like potential. Alternatively, the SAOS-2 aggregoids are more mature osteoblast-like phenotype that expressed characteristics of cellular differentiation and bone development. It was determined the two OS aggregoid models shared similarities of metabolic behaviour with different regions of OS human tissues, specifically of the higher metastatic grade.