Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer

Proteomic profiling of FFPE specimens: Discovery of HNRNPA2/B1 and STT3B as biomarkers for determining formalin fixation durations

Recent advancements in proteomics technologies using formalin-fixed paraffin-embedded (FFPE) samples have significantly advanced biomarker discovery. Yet, the effects of varying sample preparation protocols on proteomic analyses remain poorly understood. We analyzed mouse liver FFPE samples that varied in fixatives, fixation duration, and storage temperature using LC/MS. We found that variations in fixation duration significantly affected the abundance of specific proteins, showing that HNRNPA2/B1 demonstrated a significant decrease in abundance in samples fixed for long periods, whereas STT3B exhibited a significant increase in abundance in samples fixed for long durations. These findings were supported by immunohistochemical analysis across liver, spleen, and lung tissues, demonstrating a significant decrease in the nuclear staining of HNRNPA2/B1 in long-duration acid formalin(AF)-fixed FFPE samples, and an increase in cytoplasmic staining of STT3B in long-duration neutral buffered formalin-fixed liver and lung tissues and granular staining in all long-duration AF-fixed FFPE tissue types. Similar trends were observed in the long-duration fixed HeLa cells. These results demonstrate that fixation duration critically affects the proteomic integrity of FFPE samples, emphasizing the urgent need for standardized fixation protocols to ensure consistent and reliable proteomic data. SIGNIFICANCE: The quality of FFPE samples is primarily influenced by the fixation and storage conditions. However, previous studies have mainly focused on their impact on nucleic acids and the extent to which different fixation conditions affect changes in proteins has not been evaluated. In addition, to our knowledge, proteomic research focusing on differences in formalin fixation conditions has not yet been conducted. Here, we analyzed FFPE samples with different formalin fixation and storage conditions using LC/MS and evaluated the impact of different fixation conditions on protein variations. Our study unequivocally established formalin fixation duration as a critical determinant of protein variation in FFPE specimens and successfully identified HNRNPA2/B1 and STT3B as potential biomarkers for predicting formalin fixation duration for the first time. The study findings open new avenues for quality assessment in biomedical research and diagnostics.

In situ profiling reveals spatially metabolic injury in the initiation of polystyrene nanoplastic-derived intestinal epithelial injury in mice

Despite increasing concerns regarding the harmful effects of plastic-induced gut injury, mechanisms underlying the initiation of plastic-derived intestinal toxicity remain unelucidated. Here, mice were subjected to long-term exposure to polystyrene nanoplastics (PS-NPs) of varying sizes (80, 200, and 1000 nm) at doses relevant to human dietary exposure. PS-NPs exposure did not induce a significant inflammatory response, histopathological damage, or intestinal epithelial dysfunction in mice at a dosage of 0.5 mg/kg/day for 28 days. However, PS-NPs were detected in the mouse intestine, coupled with observed microstructural changes in enterocytes, including mild villous lodging, mitochondrial membrane rupture, and endoplasmic reticulum (ER) dysfunction, suggesting that intestinal-accumulating PS-NPs resulted in the onset of intestinal epithelial injury in mice. Mechanistically, intragastric PS-NPs induced gut microbiota dysbiosis and specific bacteria alterations, accompanied by abnormal metabolic fingerprinting in the plasma. Furthermore, integrated data from mass spectrometry imaging-based spatial metabolomics and metallomics revealed that PS-NPs exposure led to gut dysbiosis-associated host metabolic reprogramming and initiated intestinal injury. These findings provide novel insights into the critical gut microbial-host metabolic remodeling events vital to nanoplastic-derived-initiated intestinal injury.

ECHDC2 inhibits the proliferation of gastric cancer cells by binding with NEDD4 to degrade MCCC2 and reduce aerobic glycolysis

BACKGROUND

The Enoyl-CoA hydratase/isomerase family plays a crucial role in the metabolism of tumors, being crucial for maintaining the energy balance and biosynthetic needs of cancer cells. However, the enzymes within this family that are pivotal in gastric cancer (GC) remain unclear.

METHODS

We employed bioinformatics techniques to identify key Enoyl-CoA hydratase/isomerase in GC. The expression of ECHDC2 and its clinical significance were validated through tissue microarray analysis. The role of ECHDC2 in GC was further assessed using colony formation assays, CCK8 assay, EDU assay, Glucose and lactic acid assay, and subcutaneous tumor experiments in nude mice. The mechanism of action of ECHDC2 was validated through Western blotting, Co-immunoprecipitation, and immunofluorescence experiments.

RESULTS

Our analysis of multiple datasets indicates that low expression of ECHDC2 in GC is significantly associated with poor prognosis. Overexpression of ECHDC2 notably inhibits aerobic glycolysis and proliferation of GC cells both in vivo and in vitro. Further experiments revealed that overexpression of ECHDC2 suppresses the P38 MAPK pathway by inhibiting the protein level of MCCC2, thereby restraining glycolysis and proliferation in GC cells. Ultimately, it was discovered that ECHDC2 promotes the ubiquitination and subsequent degradation of MCCC2 protein by binding with NEDD4.

CONCLUSIONS

These findings underscore the pivotal role of the ECHDC2 in regulating aerobic glycolysis and proliferation in GC cells, suggesting ECHDC2 as a potential therapeutic target in GC.

Desorption Electrospray Ionization Mass Spectrometry Imaging Techniques Depict a Reprogramming of Energy and Purine Metabolism in the Core Brain Regions of Chronic Social Defeat Stress Mice

Depression is associated with pathological changes and metabolic abnormalities in multiple brain regions. The simultaneous comprehensive and in situ detection of endogenous molecules in all brain regions is essential for a comprehensive understanding of depression pathology, which is described in this paper. A method based on desorption electrospray ionization mass spectrometry imaging (DESI-MSI) technology was developed to classify mouse brain regions using characteristic lipid molecules and to detect the metabolites in mouse brain tissue samples simultaneously. The results showed that characteristic lipid molecules can be used to clearly distinguish each subdivision of the mouse brain, and the accuracy of this method is higher than that of the conventional staining method. The cerebellar cortex, medial prefrontal cortex, hippocampus, striatum, nucleus accumbens-core, and nucleus accumbens-shell exhibited the most significant differences in the chronic social defeat stress model. An analysis of metabolic pathways revealed that 13 kinds of molecules related to energy metabolism and purine metabolism exhibited significant changes. A DESI-MSI method was developed for the detection of pathological brain sections. We found, for the first time, that there are characteristic changes in the energy metabolism in the cortex and purine metabolism in the striatum, which is highly important for obtaining a deeper and more comprehensive understanding of the pathology of depression and discovering regulatory targets.

Whole-transcriptome sequencing in advanced gastric or gastroesophageal cancer: A deep dive into its clinical potential

Advanced gastric and gastroesophageal junction cancers (GC/GEJCs) harbor diverse molecular signatures, highlighting the need for intricate evaluations to identify potential therapeutic targets. Although whole-transcriptome sequencing (WTS) has emerged as a useful tool for understanding these molecular intricacies, its clinical implications have yet to be fully elucidated. This study evaluated the correlation between immunohistochemistry (IHC) and WTS, compared their clinical significance, and identified potential therapeutic targets undetectable through IHC alone. We enrolled 140 patients with advanced GC/GEJC and assessed them using IHC for six pivotal biomarkers: claudin-18 (CLDN18), human epidermal growth factor receptor 2 (HER2), multiple receptor tyrosine kinases (RTKs), and programmed death ligand 1 (PD-L1). Concurrently, WTS was employed as part of the analyses in MONSTAR-SCREEN-2, a multicenter multiomics study. IHC analysis revealed 16.4% HER2, 39.3% CLDN18 (2+/3 + ≥75%), and 15.8% PD-L1 (combined positive score ≥ 10) positivity, among other molecular markers. Significant correlations were observed between IHC and WTS for all six pivotal biomarkers. Among nineteen HER2 IHC-positive patients treated with anti-HER2 therapeutics, ERBB2 status in WTS was significantly associated with progression-free survival (ERBB2-high vs. -low: median 9.0 vs. 5.6 months, log-rank p = 0.046). IHC-based molecular profiling revealed significantly high expression of CLDN18 in RTK-negative patients, with 78.4% positive for either CLDN18 or PD-L1. Additionally, WTS revealed elevated expression of pivotal biomarkers in patients displaying negative targetable biomarkers via IHC. Our findings highlighted the significant correlation between IHC and WTS, reinforcing the clinical utility of WTS. A subset with IHC-negative but WTS-positive status may benefit from specific biomarker-targeted therapies.

Nanomedomics

Nanomaterials have attractive physicochemical properties. A variety of nanomaterials such as inorganic, lipid, polymers, and protein nanoparticles have been widely developed for nanomedicine via chemical conjugation or physical encapsulation of bioactive molecules. Superior to traditional drugs, nanomedicines offer high biocompatibility, good water solubility, long blood circulation times, and tumor-targeting properties. Capitalizing on this, several nanoformulations have already been clinically approved and many others are currently being studied in clinical trials. Despite their undoubtful success, the molecular mechanism of action of the vast majority of nanomedicines remains poorly understood. To tackle this limitation, herein, this review critically discusses the strategy of applying multiomics analysis to study the mechanism of action of nanomedicines, named nanomedomics, including advantages, applications, and future directions. A comprehensive understanding of the molecular mechanism could provide valuable insight and therefore foster the development and clinical translation of nanomedicines.

Gastric cancer immunosuppressive microenvironment heterogeneity: implications for therapy development

Although immunotherapy has revolutionized solid tumor treatment, durable responses in gastric cancer (GC) remain limited. The heterogeneous tumor microenvironment (TME) facilitates immune evasion, contributing to resistance to conventional and immune therapies. Recent studies have highlighted how specific TME components in GC acquire immune escape capabilities through cancer-specific factors. Understanding the underlying molecular mechanisms and targeting the immunosuppressive TME will enhance immunotherapy efficacy and patient outcomes. This review summarizes recent advances in GC TME research and explores the role of the immune-suppressive system as a context-specific determinant. We also provide insights into potential treatments beyond checkpoint inhibition.

Development of Transmission Ambient Pressure Laser Desorption Ionization/Postphotoionization Mass Spectrometry Imaging

Laser-based high-resolution mass spectrometry imaging at ambient conditions has promising applications in life science. However, the ion yield during laser desorption/ablation is poor. Here, transmission atmospheric pressure laser desorption ionization combined with a compact postphotoionization (t-AP-LDI/PI) assembly with a krypton discharge lamp was developed for the untargeted imaging of various biomolecules. The spatial distributions of numerous lipid classes, fatty acids, neurotransmitters, and amino acids in the subregions of mouse cerebellum tissue were obtained. Compared with single laser ablation, the sensitivities for most analytes were increased by 1 to 3 orders of magnitude by dopant-assisted postphotoionization. After careful optimization, a spatial resolution of 4 μm could be achieved for the metabolites in mouse hippocampus tissue. Finally, the melanoma tissue slices were analyzed using t-AP-LDI/PI MSI, which revealed the metabolic heterogeneity of the melanoma microenvironment and exhibited the phenomenon of abnormal proliferation and invasion trends in tumor cells.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

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

BACKGROUND

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

AIM OF THE REVIEW

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

KEY SCIENTIFIC CONCEPTS OF REVIEW

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

Mapping Spatiotemporal Heterogeneity in Multifocal Breast Tumor Progression by Noninvasive Ultrasound Elastography-Guided Mass Spectrometry Imaging Strategy

Spatiotemporal heterogeneity of tumors provides an escape mechanism for breast cancer cells, which can obstruct the investigation of tumor progression. While molecular profiling obtained from mass spectrometry imaging (MSI) is rich in biochemical information, it lacks the capacity for in vivo analysis. Ultrasound diagnosis has a high diagnostic accuracy but low chemical specificity. Here, we describe a noninvasive ultrasound elastography (UE)-guided MSI strategy (UEg-MSI) that integrates physical and biochemical characteristics of tumors acquired from both in vivo and in vitro imaging. Using UEg-MSI, both elasticity histopathology metabolism "fingerprints" and reciprocal crosstalk are revealed, indicating the intact, multifocal spatiotemporal heterogeneity of spontaneous tumorigenesis of the breast from early, middle, and late stages. Our results demonstrate a gradual increase in malignant degree of primary focus in cervical and thoracic mammary glands. This progression is characterized by increased stiffness according to elasticity scores, histopathological changes from hyperplasia to increased nests of neoplastic cells and necrotic areas, and regional metabolic heterogeneity and reprogramming at the spatiotemporal level. De novo fatty acid (FA) synthesis focused on independent (such as ω-9 FAs) and dependent (such as ω-6 FAs) dietary FA intake in the core cancerous nest areas in the middle and late stages of tumor or in the peripheral microareas in the early stage of the tumor. SM-Cer signaling pathway and GPs biosynthesis and degradation, as well as glycerophosphoinositol intensity, changed in multiple characteristic microareas. The UEg-MSI strategy holds the potential to expand MSI applications and enhance ultrasound-mediated cancer diagnosis. It offers new insight into early cancer discovery and the occurrence of metastasis.

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Lipidomics reveals immune-related adverse events in NSCLC patients receiving immune checkpoint inhibitor

There is a lack of reliable biomarkers to predict and identify the risk of immune-related adverse events (irAEs) in non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) treatment. This study aims to explore potential biomarkers using lipidomics to identify and predict the risk of irAEs in NSCLC patients receiving ICI treatment. This prospective study enrolled 94 NSCLC patients with IIIB/IV stage NSCLC who underwent first-line chemotherapy in combination with ICI treatment. The prediction cohort consisted of plasma samples collected from 60 patients before ICI treatment, and the occurrence of irAE was monitored within 6 months of initiating first-line ICI therapy. The validation cohort comprised 34 patients, with plasma samples obtained from 15 patients who did not develop irAE at 6 months of ICI treatment and plasma samples collected from 19 irAE patients at the onset of irAE. Through non-targeted lipidomics and semi-targeted lipid quantification analysis, we identify 11 differentially metabolized lipids and further screened these lipids with the area under the curve (AUC) > 0.7 to predict the occurrence of irAEs in NSCLC patients following ICI treatment. The results showed that the biomarker panel consisting of 9 lipids (LPC-18:2, PC-40:6, LPC-22:6, LPC-O-18:0, PS-38:0, PC-38:6, PC-37:6, PC-36:5,LPC-17:0) exhibited a good AUC of 0.859 in the prediction and 0.940 in the validation cohort phase of the receiver operating characteristic curve; The study utilizes plasma lipidomics to develop a rapid and effective prediction model for identifying irAEs in advanced NSCLC patients who treatment with first-line chemotherapy combined with immunotherapy.

Association between spatial distribution of leukocyte subsets and clinical presentation of head and neck squamous cell carcinoma

Background

Interactions between tumor cells and cells in the microenvironment contribute to tumor development and metastasis. The spatial arrangement of individual cells in relation to each other influences the likelihood of whether and how these cells interact with each other.

Methods

This study investigated the effect of spatial distribution on the function of leukocyte subsets in the microenvironment of human head and neck squamous cell carcinoma (HNSCC) using multiplex immunohistochemistry (IHC). Leukocyte subsets were further classified based on analysis of two previously published HNSCC single-cell RNA datasets and flow cytometry (FC).

Results

IHC revealed distinct distribution patterns of leukocytes differentiated by CD68 and CD163. While CD68hiCD163lo and CD68hiCD163hi cells accumulated near tumor sites, CD68loCD163hi cells were more evenly distributed in the tumor stroma. PD-L1hi and PD-1hi cells accumulated predominantly around tumor sites. High cell density of PD-L1hi CD68hiCD163hi cells or PD-1hi T cells near the tumor site correlated with improved survival. FC and single cell RNA revealed high variability within the CD68/CD163 subsets. CD68hiCD163lo and CD68hiCD163hi cells were predominantly macrophages (MΦ), whereas CD68loCD163hi cells appeared to be predominantly dendritic cells (DCs). Differentiation based on CD64, CD80, CD163, and CD206 revealed that TAM in HNSCC occupy a broad spectrum within the classical M1/M2 polarization. Notably, the MΦ subsets expressed predominantly CD206 and little CD80. The opposite was observed in the DC subsets.

Conclusion

The distribution patterns and their distinct interactions via the PD-L1/PD-1 pathway suggest divergent roles of CD68/CD163 subsets in the HNSCC microenvironment. PD-L1/PD-1 interactions appear to occur primarily between specific cell types close to the tumor site. Whether PD-L1/PD-1 interactions have a positive or negative impact on patient survival appears to depend on both the spatial localization and the entity of the interacting cells. Co-expression of other markers, particularly CD80 and CD206, supports the hypothesis that CD68/CD163 IHC subsets have distinct functions. These results highlight the association between spatial leukocyte distribution patterns and the clinical presentation of HNSCC.

Spatial omics: An innovative frontier in aging research

Disentangling the impact of aging on health and disease has become critical as population aging progresses rapidly. Studying aging at the molecular level is complicated by the diverse aging profiles and dynamics. However, the examination of cellular states within aging tissues in situ is hampered by the lack of high-resolution spatial data. Emerging spatial omics technologies facilitate molecular and spatial analysis of tissues, providing direct access to precise information on various functional regions and serving as a favorable tool for unraveling the heterogeneity of aging. In this review, we summarize the recent advances in spatial omics application in multi-organ aging research, which has enhanced the understanding of aging mechanisms from multiple standpoints. We also discuss the main challenges in spatial omics research to date, the opportunities for further developing the technology, and the potential applications of spatial omics in aging and aging-related diseases.

Metabolic heterogeneity in cancer

Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.

Targeting lipid metabolism in cancer metastasis

This review delves into the most recent research on the metabolic adaptability of cancer cells and examines how their metabolic functions can impact their progression into metastatic forms. We emphasize the growing significance of lipid metabolism and dietary lipids within the tumor microenvironment, underscoring their influence on tumor progression. Additionally, we present an outline of the interplay between metabolic processes and the epigenome of cancer cells, underscoring the importance regarding the metastatic process. Lastly, we examine the potential of targeting metabolism as a therapeutic approach in combating cancer progression, shedding light on innovative drugs/targets currently undergoing preclinical evaluation.

Isotope-Coded On-Tissue Derivatization for Quantitative Mass Spectrometry Imaging of Short-Chain Fatty Acids in Biological Tissues

Short-chain fatty acids (SCFAs), as the main metabolites of gut microbiota, are recognized as crucial players in the host's inflammatory response and metabolic disease. Imaging the spatial distributions and calculating the accurate contents of SCFAs in the heterogeneous intestinal tissue are critical to reveal their biological functions. Here, we develop an isotope-coded on-tissue derivatization method combined with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to map the spatial expressions of SCFAs in the colon tissue based on pair-labeled N,N,N-trimethyl-2-(piperazin-1-yl)ethan-1-aminium iodide (TMPA) and D3-TMPA. A noticeable increase in the MALDI-MSI sensitivity of SCFAs was achieved after on-tissue derivatization, which enables the visualization of acetic acid, propionic acid, butyric acid, valeric acid, hexanoic acid, hydroxy acetic acid, and hydroxy propionic acid in the colon tissue. Moreover, the introduction of D3-TMPA-tagged SCFAs as internal standards can significantly reduce quantitation deviation from the matrix effects, ensuring the quantitative MALDI-MSI of SCFAs. We further used this method to characterize the spatial alterations of SCFAs in the colon tissues of mice with enterocolitis. The development of this strategy provides a reliable approach to image the spatial expressions of SCFAs in tissues and paves an insight way to study the roles of SCFAs in the gut microbiota and disease.

Spatial transcriptomics in development and disease

The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.

Multifaceted effects of obesity on cancer immunotherapies: Bridging preclinical models and clinical data

Obesity, defined by excessive body fat, is a highly complex condition affecting numerous physiological processes, such as metabolism, proliferation, and cellular homeostasis. These multifaceted effects impact cells and tissues throughout the host, including immune cells as well as cancer biology. Because of the multifaceted nature of obesity, common parameters used to define it (such as body mass index in humans) can be problematic, and more nuanced methods are needed to characterize the pleiotropic metabolic effects of obesity. Obesity is well-accepted as an overall negative prognostic factor for cancer incidence, progression, and outcome. This is in part due to the meta-inflammatory and immunosuppressive effects of obesity. Immunotherapy is increasingly used in cancer therapy, and there are many different types of immunotherapy approaches. The effects of obesity on immunotherapy have only recently been studied with the demonstration of an "obesity paradox", in which some immune therapies have been demonstrated to result in greater efficacy in obese subjects despite the direct adverse effects of obesity and excess body fat acting on the cancer itself. The multifactorial characteristics that influence the effects of obesity (age, sex, lean muscle mass, underlying metabolic conditions and drugs) further confound interpretation of clinical data and necessitate the use of more relevant preclinical models mirroring these variables in the human scenario. Such models will allow for more nuanced mechanistic assessment of how obesity can impact, both positively and negatively, cancer biology, host metabolism, immune regulation, and how these intersecting processes impact the delivery and outcome of cancer immunotherapy.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

Spatially resolved visualization of reprogrammed metabolism in hepatocellular carcinoma by mass spectrometry imaging

BACKGROUND

Metabolic reprogramming refers to tumor-associated metabolic alterations during tumorigenesis and has been regarded as one of the most important features of cancer. Profiling the altered metabolites and lipids in hepatocellular carcinoma with spatial signature will not only enhance our understanding of tumor metabolic reprogramming, but also offer potential metabolic liabilities that might be exploited for hepatocellular carcinoma therapy.

METHODS

We perform matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) analysis on both hepatocellular carcinoma xenograft mouse model and hepatocellular carcinoma patients. Discriminatory metabolites that altered during the development of hepatocellular carcinoma are screened and imaged in xenograft mouse model and are further validated in 21 hepatocellular carcinoma patients.

RESULTS

We discover stepwise metabolic alterations and progressively increasing metabolic heterogeneity during the growth of hepatocellular carcinoma. Arginine and its metabolites spermine and spermidine, choline and phosphatidylcholine metabolism, and fatty acids were found to be significantly reprogrammed in hepatocellular carcinoma tissues.

CONCLUSIONS

The spatially resolved profiling of the metabolites and lipids in highly heterogeneous hepatocellular carcinoma tissue will contribute to obtaining precise metabolic information for the understanding of tumor metabolic reprogramming.

Spatial Metabolome Lipidome and Glycome from a Single brain Section

Metabolites, lipids, and glycans are fundamental biomolecules involved in complex biological systems. They are metabolically channeled through a myriad of pathways and molecular processes that define the physiology and pathology of an organism. Here, we present a blueprint for the simultaneous analysis of spatial metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. Complimenting an original experimental protocol, our workflow includes a computational framework called Spatial Augmented Multiomics Interface (Sami) that offers multiomics integration, high dimensionality clustering, spatial anatomical mapping with matched multiomics features, and metabolic pathway enrichment to providing unprecedented insights into the spatial distribution and interaction of these biomolecules in mammalian tissue biology.

Spatial metabolomics in head and neck tumors: a review

The joint analysis of single-cell transcriptomics, proteomics, lipidomics, metabolomics and spatial metabolomics is continually transforming our understanding of the mechanisms of metabolic reprogramming in tumor cells. Since head and neck tumor is the sixth most common tumor in the world, the study of the metabolic mechanism of its occurrence, development and prognosis is still undeveloped. In the past decade, this field has witnessed tremendous technological revolutions and considerable development that enables major breakthroughs to be made in the study of human tumor metabolism. In this review, a comprehensive comparison of traditional metabolomics and spatial metabolomics has been concluded, and the recent progress and challenges of the application of spatial metabolomics combined multi-omics in the research of metabolic reprogramming in tumors are reviewed. Furthermore, we also highlight the advances of spatial metabolomics in the study of metabolic mechanisms of head and neck tumors, and provide an outlook of its application prospects.