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Hepatology (Baltimore, Md.) May 2024Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients,... (Review)
Review
Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.
Topics: Humans; Liver; Hepatocytes; Liver Diseases; Transcriptome; Metabolomics
PubMed: 36811413
DOI: 10.1097/HEP.0000000000000341 -
Cell Reports. Medicine Jul 2023Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance...
Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.
Topics: Humans; Aged; Genome-Wide Association Study; Bayes Theorem; Macular Degeneration; Metabolomics; Metabolome
PubMed: 37348500
DOI: 10.1016/j.xcrm.2023.101085 -
Nutrients Oct 2023The field of metabolomics and related "omics" techniques allows for the identification of a vast array of molecules within biospecimens [...].
The field of metabolomics and related "omics" techniques allows for the identification of a vast array of molecules within biospecimens [...].
Topics: Humans; Metabolomics; Nutritional Status
PubMed: 37836568
DOI: 10.3390/nu15194286 -
Cell Metabolism May 2024A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq)...
A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq) and spatially resolved metabolomics to profile 54 human samples from distinct kidney anatomical regions. We generated transcriptomes of 446,267 cells and chromatin accessibility profiles of 401,875 cells and developed a package to analyze 408,218 spatially resolved metabolomes. We find that the same cell type, including thin limb, thick ascending limb loop of Henle and principal cells, display distinct transcriptomic, chromatin accessibility, and metabolomic signatures, depending on anatomic location. Surveying metabolism-associated gene profiles revealed non-overlapping metabolic signatures between nephron segments and dysregulated lipid metabolism in diseased proximal tubule (PT) cells. Integrating multimodal omics with clinical data identified PLEKHA1 as a disease marker, and its in vitro knockdown increased gene expression in PT differentiation, suggesting possible pathogenic roles. This study highlights previously underrepresented cellular heterogeneity underlying the human kidney anatomy.
Topics: Humans; Kidney; Transcriptome; Metabolomics; Epigenomics; Male; Gene Expression Profiling; Female
PubMed: 38513647
DOI: 10.1016/j.cmet.2024.02.015 -
Mass Spectrometry Reviews 2024Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it... (Review)
Review
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.
Topics: Humans; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Artificial Intelligence; Metabolomics; Neoplasms; Lipidomics
PubMed: 36065601
DOI: 10.1002/mas.21804 -
Current Opinion in Chemical Biology Dec 2023Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including... (Review)
Review
Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.
Topics: Metabolomics; Diet; Nutritional Status; Gastrointestinal Microbiome; Microbiota
PubMed: 37804582
DOI: 10.1016/j.cbpa.2023.102400 -
Magnetic Resonance in Chemistry : MRC Dec 2023Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred... (Review)
Review
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
Topics: Humans; Metabolome; Reproducibility of Results; Metabolomics; Multivariate Analysis; Principal Component Analysis
PubMed: 37005774
DOI: 10.1002/mrc.5350 -
Chinese Medical Journal Aug 2023Psoriasis is a chronic inflammatory skin disease with significant physical and psychological burdens. The interplay between the innate and adaptive immune systems is... (Review)
Review
Psoriasis is a chronic inflammatory skin disease with significant physical and psychological burdens. The interplay between the innate and adaptive immune systems is thought to contribute to the pathogenesis; however, the details of the pathogenesis remain unclear. In addition, reliable biomarkers for diagnosis, assessment of disease activity, and monitoring of therapeutic response are limited. Metabolomics is an emerging science that can be used to identify and analyze low molecular weight molecules in biological systems. During the past decade, metabolomics has been widely used in psoriasis research, and substantial progress has been made. This review summarizes and discusses studies that applied metabolomics to psoriatic disease. These studies have identified dysregulation of amino acids, carnitines, fatty acids, lipids, and carbohydrates in psoriasis. The results from these studies have advanced our understanding of: (1) the molecular mechanisms of psoriasis pathogenesis; (2) diagnosis of psoriasis and assessment of disease activity; (3) the mechanism of treatment and how to monitor treatment response; and (4) the link between psoriasis and comorbid diseases. We discuss common research strategies and progress in the application of metabolomics to psoriasis, as well as emerging trends and future directions.
Topics: Humans; Psoriasis; Skin; Biomarkers; Metabolomics
PubMed: 37106557
DOI: 10.1097/CM9.0000000000002504 -
Nature Metabolism Oct 2023Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has... (Review)
Review
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
Topics: Humans; Metabolomics; Prognosis; Phenotype; Disease Progression; Precision Medicine
PubMed: 37872285
DOI: 10.1038/s42255-023-00903-x -
Nature Aging Aug 2023Technical advancements over the past two decades have enabled the measurement of the panoply of molecules of cells and tissues including transcriptomes, epigenomes,... (Review)
Review
Technical advancements over the past two decades have enabled the measurement of the panoply of molecules of cells and tissues including transcriptomes, epigenomes, metabolomes and proteomes at unprecedented resolution. Unbiased profiling of these molecular landscapes in the context of aging can reveal important details about mechanisms underlying age-related functional decline and age-related diseases. However, the high-throughput nature of these experiments creates unique analytical and design demands for robustness and reproducibility. In addition, 'omic' experiments are generally onerous, making it crucial to effectively design them to eliminate as many spurious sources of variation as possible as well as account for any biological or technical parameter that may influence such measures. In this Perspective, we provide general guidelines on best practices in the design and analysis of omic experiments in aging research from experimental design to data analysis and considerations for long-term reproducibility and validation of such studies.
Topics: Geroscience; Reproducibility of Results; Transcriptome; Metabolome; Proteome
PubMed: 37386258
DOI: 10.1038/s43587-023-00448-4