A lipidomics roadmap: from basic research to societal challenges.

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Learn more: PMC Disclaimer | PMC Copyright Notice Nat Commun . 2026 May 28;17:4778. doi: 10.1038/s41467-026-73797-4 A lipidomics roadmap: from basic research to societal challenges Maria Fedorova 1, ✉ , Anne K Bendt 2 , Justine Bertrand-Michel 3 , Oliver Fiehn 4 , Joanna Godzien 5 , Laura Goracci 6 , Florian Gruber 7 , Xue Li Guan 8, 9 , Xianlin Han 10 , Michal Holčapek 11 , Julia Kuligowski 12 , Peter J Meikle 13, 14 , Palina Nepachalovich 1 , Valerie B O’Donnell 15 , Matej Orešič 16, 17, 18 , Sider Penkov 1 , Snježana Petrović 19 , Anton Potapov 20 , Patricia Prabutzki 21, 22 , Laura Righetti 23 , Tatjana Ruskovska 24 , Andrej Shevchenko 25 , Kai Simons 25, 26 , Corinne M Spickett 27 , Olga Vvedenskaya 26 , Jennifer Watts 28 , Markus R Wenk 29 , Michael Witting 30, 31 , Yu Xia 32 , Baoru Yang 17 , Maria Rosário Domingues 33, ✉ Author information Article notes Copyright and License information 1 Center of Membrane Biochemistry and Lipid Research, University Hospital Carl Gustav Carus and Faculty of Medicine of TU Dresden, Dresden, Germany 2 Singapore Lipidomics Incubator, Singapore, Singapore 3 MetaboHUB-MetaTou, Toulouse, France 4 West Coast Metabolomics Center, University of California-Davis, Davis, CA USA 5 Medical University of Bialystok, Bialystok, Poland 6 University of Perugia, Perugia, Italy 7 Department of Dermatology, CDL SKINMAGINE, Medical University of Vienna, Vienna, Austria 8 Nanyang Technological University, Singapore, Singapore 9 Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 10 The University of Texas Health Science Center at San Antonio, San Antonio, TX USA 11 University of Pardubice, Pardubice, Czech Republic 12 Health Research Institute La Fe (IIS La Fe), Valencia, Spain 13 Baker Heart and Diabetes Institute, Melbourne, VIC Australia 14 Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, VIC Australia 15 Cardiff University, Cardiff, UK 16 Örebro University, Örebro, Sweden 17 University of Turku, Turku, Finland 18 Åbo Akademi University, Turku, Finland 19 University of Belgrade, Belgrade, Serbia 20 Senckenberg Museum for Natural History Görlitz, Görlitz, Germany 21 Leipzig University, Leipzig, Germany 22 Maastricht University, Maastricht, Netherlands 23 Wageningen University & Research, Wageningen, Netherlands 24 Faculty of Medical Sciences, Goce Delcev University, Stip, North Macedonia 25 Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany 26 Lipotype GmbH, Dresden, Germany 27 Aston University & Aston Institute for Membrane Excellence, Birmingham, UK 28 Washington State University, Pullman, WA USA 29 Hamad Bin Khalifa University, Doha, Qatar 30 Metabolomics and Proteomics Core, Helmholtz Zentrum München GmbH, Neuherberg, Germany 31 Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, München, Germany 32 Tsinghua University, Beijing, China 33 CESAM- Centre for Environmental and Marine Studies, Department of Chemistry, University of Aveiro, Aveiro, Portugal ✉ Corresponding author. Received 2025 Aug 18; Accepted 2026 May 21; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13219694  PMID: 42209482 Abstract Lipidomics, a rapidly evolving discipline at the interface of biology and analytical chemistry, seeks to comprehensively characterize the lipid composition of biological systems. Driven by advances in mass spectrometry, chromatography and computational analysis, lipidomics has enabled the high-resolution mapping of lipid networks and their functional dynamics across molecular, cellular and organismal scales. In biomedical research, lipidomics is emerging as a powerful platform for biomarker discovery, enabling early diagnosis, prognosis, and therapeutic monitoring of cancer, metabolic, and neurodegenerative diseases. The field is also reshaping drug discovery by uncovering lipid-mediated pathways, identifying novel therapeutic targets, and refining assessments of drug efficacy and safety. Beyond medicine, lipidomic analyses are redefining food and nutrition science by elucidating how dietary lipids influence metabolic health and disease risk. In parallel, environmental and ecological lipidomics are emerging as powerful frameworks for assessing ecosystem health, tracking the impact of pollutants and exploring the biological consequences of climate change. Such approaches are also informing the discovery of sustainable lipid resources and the development of novel biotechnological and agricultural innovations. With its rapidly expanding analytical repertoire and cross-disciplinary relevance, lipidomics is poised to make substantial contributions to both fundamental biology and applied science. This Perspective aims to synthesise the current state of the field, delineate major analytical and conceptual challenges, and outline future directions for translating lipidomic knowledge into tangible societal and environmental benefits. Subject terms: Mass spectrometry, Bioinformatics, Lipidomics Lipidomics, a rapidly evolving field at the interface of biology and analytical chemistry, enables comprehensive analysis of lipids in biological systems. This Perspective outlines advances, challenges, and future directions, highlighting applications of lipidomics in basic and translational science. Introduction Lipids are a diverse group of small, amphiphilic or hydrophobic molecules with a wide range of chemical structures that underlie their many biological functions and activities. Once considered merely structural components of biological membranes and reservoirs for energy storage, lipids are now at the forefront of modern research. They are recognized for their critical roles in signal transduction, regulation of electrochemical gradients, central involvement in metabolic networks, and in mediating communication between organelles and cells 1 , 2 . Because of their direct involvement in essential biological processes, disruptions in lipid levels or failures in lipid quality control, at the cellular, tissue, or whole-organism level, are increasingly linked to numerous human diseases and the aging process 3 – 5 . Lipid metabolism has been a focus of scientific inquiry since the early 20th century. In recent decades, however, lipid research has undergone a transformative evolution as advances in analytical technologies now enable the study of intact lipids at an “omics” scale 6 . Modern lipidome research is opening new dimensions in both basic and applied biomedical research, enabled by advances in lipidomics technology (Box 1 ). In this Perspective, we aim to provide a comprehensive overview of recent advances in lipidomics, address current challenges, and outline future directions for the field (Fig. 1 ). Our primary goal is not only to summarize the current state of the art, but more importantly, to highlight emerging technologies, potential applications, and key challenges across a wide range of disciplines, from basic biological research to translational science in healthcare, nutrition, as well as ecology and environmental sciences. Fig. 1. The lipidomics roadmap. Open in a new tab An overview of key technological milestones that have driven the emergence and evolution of the lipidomics field. The figure highlights parallel advancements in analytical platforms and computational data processing, as well as anticipated future directions poised to shape the next phase of lipid research. DB – database; DESI – desorption electrospray ionization; IMS – ion mobility spectrometry; LC – liquid chromatography; MALDI – matrix assisted laser desorption ionization; MS – mass spectrometry; MSI – mass spectrometry imaging; SIMS – secondary ion mass spectrometry. Box 1 Lipidome and lipidomics. The terms “lipidome” 179 and “lipidomics” 180 were first introduced between 2001 and 2003. The lipidome represents the complete set of lipids in a given biological system, while lipidomics refers to the comprehensive, large-scale analysis of cellular lipids, encompassing their structural diversity, spatial distribution, functional roles, involvement in metabolic networks, and interactions with other biomolecules. The emergence of lipidomics marked a pivotal shift toward high-throughput, systems-level lipid analysis, enabled by advanced analytical technologies such as mass spectrometry (MS) often coupled with separation techniques. Since 2003, lipidomics has undergone transformative development, both in terms of the analytical and computational tools now available 7 , 181 , and in the breadth of its applications, ranging from clinical translation to ecosystem health 124 , 147 , 182 – 184 . Technological developments in lipidomics: advances and challenges Technological developments in bioanalytical chemistry and MS instrumentation have led to significant advances in lipidomics 7 (Fig. 1 , Box 2 ). Improved sensitivity (4–5 orders of magnitude since the 1980s 8 , 9 ) and mass accuracy (sub-ppm) of modern high-resolution analytical platforms have markedly expanded the detectable breadth of lipid subclasses. Compared to the dozens of lipids typically reported in the early 2000s, current lipidomics workflows now routinely allow the annotation of several hundred lipid molecular species 10 , 11 and have facilitated the characterization of previously unrecognized or poorly characterized classes (e.g., maradolipids 12 , ecdysteroids 13 , or endocannabinoid-related N-acylamidated amino acids 14 ). In particular, the hyphenation of complementary separation modalities has increased the overall peak capacity of analytical platforms. For example, the integration of liquid chromatography (LC), which separates analytes based on polarity and/or hydrophobicity, with ion mobility spectrometry (IMS), which resolves ions according to mass-to-charge ratio and gas-phase conformation, with mass spectrometry (MS) has substantially expanded the detectable coverage of complex lipidomes 15 . In parallel, the development of alternative ion activation modalities has enabled deeper structural annotation and laid the foundation for structural lipidomics. Current methods for resolving double-bond positional isomers rely either on chemical derivatization strategies (for example, the Paternò–Büchi reaction) or on alternative gas-phase fragmentation techniques, including ozone-induced dissociation (OzID), electron-induced dissociation (EID), oxygen attachment dissociation (OAD) and ultraviolet photodissociation (UVPD). These approaches enable the differentiation of isomeric lipids with respect to sn -position and double-bond location 16 – 18 . Resolving spatial organization of tissue lipidomes using MS imaging (MSI) represents another major technological breakthrough. Spatial proteomics was named the Method of the Year in 2024 19 , and lipidomics applications of MSI are steadily catching up 20 . The combination of MSI with high-resolution MS, ion mobility, and various ionization methods 21 – 23 has enabled the construction of the first spatially resolved maps of different tissues and organs 24 and revealed the molecular heterogeneity of cell populations 25 . Although quantitative MSI remains challenging, progress has been made through the development and application of suitable mixtures of internal standards 26 . Moreover, integrating MSI with complementary label-based and label-free techniques, including immunofluorescence mapping of cellular identities 27 , nanoscale spectroscopic methods such as Raman spectroscopy 28 and multi-omics approaches 29 , is opening new opportunities for systems-level analyses of lipid biology 30 . Next, significant progress has been made in extending lipidomics toward the analysis of lipidome dynamics, driven by advances in chemical biology 31 and bioorthogonal tracing methods 32 – 34 , together with the broader adoption of metabolic flux analysis 35 . When integrated with classical biochemical approaches, these strategies provide a more detailed view of lipid metabolism and function. The combination of complementary dimensions, such as temporal dynamics from metabolic tracing and spatial localization from imaging lipidomics, will enable lipidomics to move beyond static inventories of lipid species toward a systems-level understanding of lipid biology within living tissues and their microenvironments. Although advanced lipidomics workflows described above remain areas of specialization, conventional solutions based on the application of LC-MS/MS or shotgun lipidomics have become widely accessible to researchers through platforms built by individual laboratories or core institutional facilities. The increasing “democratization” of lipidomics, driven by broader access to instrumentation, a growing pool of trained operators and decreasing analytical costs, has made the technology accessible to a wide range of researchers, supporting applications from biomarker discovery to detailed investigations of lipid metabolism and biology. The widespread adoption of lipidomics has also generated large datasets, facilitating deposition in public repositories, enabling cross-study reuse and integration, and supporting the development of lipidomics atlases that define organ-specific lipid signatures 36 – 41 . Despite the increasing accessibility and adoption of lipidomics, major challenges remain in the accuracy of lipidome annotation and quantification 42 , 43 . Although advances in structural lipidomics enable deeper identification, overall annotation confidence remains limited and structural over-reporting still occurs 44 , 45 . Quantitative lipidomics is further constrained by the lack of harmonized workflows and standardized guidelines addressing factors such as ionization adducts, in-source fragmentation and matrix effects 46 , 47 . In addition, the limited availability of stable isotope-labelled internal standards and certified reference materials remains a major barrier to accurate absolute quantification and inter-laboratory comparability. Community efforts have begun to address these issues. Early initiatives by LIPID MAPS brought together researchers and standard manufacturers to expand the availability of lipid standards. More recently, the adoption of unified shorthand notation following LIPID MAPS classification, the Lipidomics Minimal Reporting Checklist introduced by the Lipidomics Standards Initiative, and reporting standards from the Metabolomics Standards Initiative and mQACC aim to establish community-wide guidelines and improve data quality and reproducibility 48 – 52 . Box 2 Major technological advances in modern lipidomics. ● Marked improvements in the sensitivity, resolution and mass accuracy of modern mass spectrometry platforms. ● Quantitative lipidomics enabled by the systematic use of isotopically labelled internal standards. ● Hyphenation of separation strategies that combine liquid chromatography (LC), ion mobility spectrometry (IMS) and MS (including MS/MS), substantially increasing molecular coverage and confidence in lipid annotation. ● Development of new ion activation methods for tandem MS (e.g., electron-induced dissociation (EID) and ultraviolet photodissociation (UVPD)), facilitating detailed structural characterization, including assignment of sn -positional isomers and double-bond locations. ● Expansion of mass spectrometry imaging (MSI) approaches, enabling spatially resolved lipidomics and integration with complementary omics and imaging modalities. Computational solutions for lipidomics: navigating the complexities of lipid data The evolution of high-throughput instrumentation has driven the parallel development of specialized bioinformatics tools, ranging from lipid annotation to the integration of lipidomics with other omics datasets (Fig. 1 , Box 3 ). To support users in navigating the growing ecosystem of open-source tools, the lipidomics community has also developed a centralized Lipidomics Tools Guide 53 , hosted on LIPID MAPS, offering a curated overview of available resources and their applications across the lipidomics workflows. Thus, for untargeted lipidomics data, a range of approaches, including spectral library matching, rule-based annotation and hybrid methods, are now widely available 54 , 55 . However, metabolomics tools based on in silico fragmentation using AI and machine learning remain rarely adopted in lipidomics, as they require fully resolved structures to predict fragment ions 56 , 57 . In parallel with advances in experimental technologies, new tools are emerging for the annotation of double-bond positions within lipid acyl chains 54 , 58 . The growing application of MSI further highlights the need for automated, scalable solutions tailored to spatial lipidomics 59 – 63 . A similar gap exists in metabolic tracing and flux analysis, where experimental progress has outpaced the development of bioinformatics tools capable of handling complex, multidimensional datasets 34 , 64 . Yet, the large number of independently developed software tools has introduced challenges in data interoperability, as lipid identifications are often reported using different annotation criteria and formats. This issue has been partly addressed through lipid identifier conversion tools 65 , 66 , as well as community-driven efforts led by LIPID MAPS to standardize lipid nomenclature using harmonized shorthand notation 50 . The rapid expansion of measured lipid diversity has been supported by dedicated structural databases such as the LIPID MAPS Structure Database (LMSD), SwissLipids and LipidBank 67 – 69 . These resources are increasingly being curated to include metadata and contextual information, transforming them from static repositories into integrative knowledge platforms. Complementing these efforts are curated MS data repositories such as Metabolomics Workbench 70 and MetaboLights 71 , which facilitate data reuse and cross-study comparisons by providing lipidomics datasets in standardized formats (e.g., mzML) along with associated metadata. Despite substantial progress in analytical and computational tools, comprehensive characterization of the full diversity of lipid species in natural lipidomes remains elusive. New lipid structures continue to be discovered, underscoring the dynamic and incomplete nature of current lipidome coverage. The concept of “dark matter” in LC–MS/MS-based lipidomics reflects this gap, as only a fraction of features in untargeted datasets can currently be annotated 72 . This is partly due to ionization adducts, in-source fragments and multiple aggregation states of the same analyte 73 . However, a substantial proportion of features remains unannotated due to missing reference spectra or undefined fragmentation rules. Efforts to resolve this “dark lipidome” are accelerating, with emerging computational approaches designed to interrogate under-annotated spectral data 74 , 75 . Reverse metabolomics 76 , 77 , molecular networking 78 and Mass Spectrometry Query Language (MassQL) 79 are increasingly being adapted for lipidomics to support large-scale annotation. In parallel, resources such as PartialDB, developed by LIPID MAPS and EpiLipidNET, provide curated MS/MS spectra of partially characterized lipids, enabling recognition of recurrent fragmentation patterns and supporting their prospective identification. Box 3 Major advances in lipidomics-oriented software and computational tools. ● Development and iterative refinement of lipid annotation tools enabling efficient processing of high-throughput LC-MS/MS datasets. ● Emergence of new software solutions to support advances in structural and spatial lipidomics, as well as metabolic tracing of lipid pathways. ● Establishment and expansion of comprehensive lipid structural databases. ● Broad adoption of MS data repositories for lipidomics dataset deposition, enhancing data accessibility, transparency and reuse. Clinical lipidomics: transforming metabolic health assessment One of the most promising yet formidable frontiers for lipidomics lies in its clinical translation (Fig. 2 ). Given the central role of dyslipidaemia, a condition marked by abnormal lipid abundance in circulation and perturbed lipid metabolism, and altered lipid signalling in the pathogenesis and progression of a wide spectrum of metabolic 80 and degenerative diseases 81 , including cancer 82 , lipids hold significant potential as biomarkers for susceptibility, diagnosis, prognosis, and pharmacodynamic monitoring, as well as therapeutic targets 83 , 84 . Despite this promise, only a limited number of lipidomic assays have been incorporated into routine clinical practice, with FDA- and EMA-approved commercial tests or laboratory-developed tests (LDTs) remaining scarce. Notable exceptions include nuclear magnetic resonance (NMR)-based lipoprotein subclass analysis 85 and targeted MS quantification of acylcarnitines, a critical component of newborn screening programmes for inherited metabolic disorders 86 . Another LC-MS/MS-based screening application is the quantification of LPC (26:0) in dried blood spots, now included in newborn screening for X-linked adrenoleukodystrophy (X-ALD). X-ALD is a common leukodystrophy, with an estimated incidence of ~1 in 15000 births, caused by pathogenic variants in ABCD1 gene, which encodes a peroxisomal transporter required for import of very long-chain fatty acids for β-oxidation. Although affected infants are asymptomatic at birth, ~80% develop adrenocortical insufficiency before adulthood, making early detection critical for timely intervention against adrenal and neurological disease 87 . First implemented in New York State in 2013 88 , X-ALD screening has since been adopted across most U.S. states, as well as in the Netherlands and Taiwan, China, with pilot programmes underway in Japan, Italy and China 89 . Fig. 2. Dawn of clinical and healthcare-directed lipidomics. Open in a new tab Key pillars supporting the translation of lipidomic workflows into clinical and public health applications. The figure illustrates the potential of lipidomics to inform diagnostic panel development, monitor exposure to environmental pollutants, guide nutritional recommendations, and uncover therapeutic targets, considering variability across sex, age, and demographic groups. GWAS - genome-wide association studies; ISTDs – internal standards. The advent of high-throughput targeted lipidomics has enabled large-scale profiling in population cohorts, underpinning two clinically significant avenues: genome-wide association studies (GWAS) of lipid traits and lipidomics-based metrics for metabolic health assessment. Recent GWAS efforts in over 1.6 million individuals of diverse ancestries have identified more than 900 genetic loci linked to classical blood lipid parameters, such as total, LDL, and HDL cholesterol, and triacylglycerols 90 . Integration of these genetic studies with MS-based lipidomics, which quantifies hundreds of circulating lipid species, has expanded understanding of the genetic architecture underlying lipid metabolism and its connections to complex diseases 91 , 92 . High-throughput plasma and serum lipidomics has also uncovered novel lipid biomarkers beyond the traditional clinical panel. Thus, serum ceramides have emerged as robust risk stratifiers in coronary artery disease 93 , 94 , leading to clinical implementation of ceramide assays at institutions such as the Mayo Clinic 95 . The ceramide-based risk score CERT2, incorporating phosphatidylcholine (PC) species alongside ceramides 96 , has been licensed to Quest Diagnostics for clinical assay development. More sophisticated lipidomic risk models have been proposed for primary prevention screening 97 . Recently, a plasmalogen-based score reflecting circulating 1-alkenyl-2-acyl-glycerophosphoethanolamines has been associated with reduced cardiometabolic risk and all-cause mortality, positioning it as an independent and potentially modifiable marker of metabolic health 98 . Notably, high-throughput lipidomic profiling of large cohorts has revealed pronounced sex differences in plasma lipidomes of both healthy individuals 99 and those with metabolic disorders 100 , 101 , underscoring biological sex as a critical determinant in the pathophysiology of metabolic disorders. The identification of novel lipid biomarkers linked to human metabolic health, together with large-scale quantitative screening, has increased interest in the clinical and commercial potential of lipidomics. A key step forward has been the shift from relative measurements to absolute lipid concentrations, providing an essential foundation for clinical translation. However, this also highlights persistent challenges in method validation, standardization and harmonization, particularly in quality assurance. Accurate absolute quantification remains difficult, especially for large lipid panels, due to the limited availability of isotopically labelled standards 102 , 103 . Variability in reported concentrations further underscores the need for well-characterized reference materials to ensure inter-study and inter-laboratory comparability 104 . Community-driven adoption of shared reference samples has demonstrated potential to harmonize data generated across diverse MS platforms and laboratories 105 . Recognizing this imperative, concerted efforts such as ring trials organized by the Singapore Lipidomics Incubator (SLING) and the Reference Materials and Biological Reference Ranges Interest Group of the International Lipidomics Society (ILS) have been launched. A recent interlaboratory study focused on quantifying four ceramides relevant to cardiovascular risk highlighted the importance of authentic labelled standards and shared reference materials 106 . Additional ring trials targeting bile acids and blood plasma lipidomes are ongoing. As quantitative reproducibility improves, lipidomics is moving towards establishing reference intervals for individual lipids and composite scores in healthy populations, a key step for clinical implementation that will require careful stratification across demographic and physiological variables. Achieving this goal will require comprehensive evaluation of reference intervals across diverse populations, including stratification by age, sex, comorbidities, diet and hormonal status. The clinical implementation of both simple lipid panels, comprising a few species, and complex profiles encompassing hundreds of lipid molecules offers transformative potential for the diagnosis and management of inflammatory, cardiometabolic, neurodegenerative disorders, and cancer. Integrating quantitative lipidomics with advanced statistical and epidemiological modelling will enable the development of predictive frameworks expressed as risk or health scores, positioning lipids as independent and modifiable markers of metabolic health influenced by diet and lifestyle. Beyond their utility as biomarkers, lipids themselves represent promising therapeutic targets, given their central role in shaping metabolic and phenotypic responses (Box 4 ). Over the coming decade, clinical lipidomics is poised to expand rapidly within established healthcare pathways and the burgeoning field of precision medicine. Large-scale lipidomic data derived from population biobanks, such as the UK Biobank, FINNGEN, and NAKO, will provide unprecedented insights into the interplay of lifestyle, environment, and genetics, underpinning advances in disease prevention and personalized healthcare 107 , 108 . Box 4 Discovery and clinical significance of an alternative fatty-acid desaturation pathway that supports tumour metabolic plasticity. Advanced structural lipidomics has enabled new insights into cancer metabolism by revealing an alternative fatty-acid desaturation pathway that supports tumour plasticity. This pathway permits certain tumours to circumvent stearoyl-CoA desaturase (SCD) by generating monounsaturated fatty acids (MUFAs), notably sapienate (16:1 ω−10), in an SCD-independent manner via fatty acid desaturase 2 (FADS2) 185 . Importantly, sapienate is both structurally and functionally distinct from its isomer palmitoleate (16:1 ω−7). In prostate cancer cell lines, LC-OzID-MS demonstrated that SCD inhibition triggers accumulation of ω−10 and ω−12 MUFAs, providing direct structural evidence for FADS2-mediated metabolic compensation 177 . Given that SCD inhibitors are being pursued as promising therapeutic agents, with some compounds, including MTI-301 already in clinical trials (e.g., NCT06911008 ), the ability to identify tumours capable of activating FADS2-driven MUFA synthesis will be crucial for predicting non-responders to SCD-targeted therapies. Beyond MUFAs, multiple lipid classes, including cholesterol, ceramides and plasmalogens hold potential for modulation, offering new avenues for therapeutic strategies 186 . Lipidomics in human toxicology: unveiling the impact of environmental pollutants Lipidomics has become an essential tool for investigating how environmental pollutants affect human health by interacting with the lipidome (Fig. 2 ). Environmental factors and toxins, including UV radiation, organic pollutants, plastics, nanoparticles, and heavy metals, contribute to the “exposome”, defined as the cumulative environmental chemical exposures an individual experiences throughout life 109 . Growing evidence suggests lipids are reliable biomarkers for assessing the toxic effects of environmental exposures. Thus, oxidized lipids (epilipids) have emerged as valuable molecular markers of senescence for monitoring both chronological skin aging and UV-induced damage 110 – 112 . Lipidomics has provided crucial insights into the effects of per- and polyfluoroalkyl substances (PFAS), a group of persistent pollutants of anthropogenic origin that gained popularity with the advent of Teflon but are now used in a wide range of products. PFAS exposure has been linked to inflammation and liver damage, with prenatal and early-life exposure leading to lipid profile changes associated with diseases such as type 1 diabetes 113 , 114 . Similarly, lipidomic analysis has proven valuable in monitoring the toxicity of microplastics and heavy metals 115 , 116 . Despite these advancements, studying the exposome remains challenging due to the complex and unpredictable nature of toxic mixtures, which can interact synergistically or antagonistically 117 . Human exposome research is critical, as individual responses to environmental exposures vary widely, making it difficult to isolate specific toxic effects. A more comprehensive map of the human lipidome in response to environmental toxins could lead to the development of biomarkers for early disease detection and improved understanding of exposure-related health risks. In the future, lipidomics may play a key role in personalizing health strategies by identifying lipid signatures linked to environmental pollutants, ultimately improving public health and disease prevention 118 . Remarkably, lipidomics is also being extended into space biology: combined microbiome and metabolomics analyses from the International Space Station have revealed environment-specific lipid and metabolite signatures, establishing a molecular framework for monitoring health during long-duration space missions 119 . Food and nutritional lipidomics: deciphering the complex role of lipids in diet and health Lipidomics has become a widely adopted technology for studying the composition, nutritional value and health benefits of dietary lipids 120 (Fig. 2 ). Lipids play a crucial role in modulating metabolic health, and numerous studies have highlighted the potential of dietary interventions to regulate disease risk. For instance, the EPIC-Potsdam population-based cohort study utilized lipidomics to identify biomarkers for cardiovascular disease (CVD) and type 2 diabetes (T2D) risk, as well as to assess how these biomarkers respond to dietary interventions based on the content of saturated (SFA) and polyunsaturated fatty acids (PUFA) 121 . Dietary lipids can also influence the composition and metabolic activity of the gut microbiome. A Western-style, high-fat diet rich in SFA has been shown to induce shifts in gut microbial communities 122 . Importantly, it is not only the fatty acyl composition of foods that affects health, but also the structural features and complex interactions between lipids and other nutrients. As research on the role of lipids in human diets grows, there is increasing recognition of the need to understand food compositions beyond the traditional focus on total fat, fatty acid composition, and cholesterol 123 . This highlights the importance of more precise food profiling using advanced lipidomics technologies 124 . Additionally, food lipidomes vary depending on factors such as food type, genetic background, geographical origin, and climate. For example, lipidomic analysis of 155 agricultural products from 58 species across 23 plant families revealed differences not only at the level of species origin but also in the specific plant tissues, such as leaf, stem, seed, and caryopsis 125 . Consequently, lipidomics has emerged as a powerful tool for assessing food quality but also authenticity and traceability 126 . Furthermore, food processing and storage can alter the lipid composition by promoting lipid degradation and oxidation 127 . Lipidomics allows the quantification of lipid peroxidation products and the evaluation of food matrix stability across different processing stages, thus aiding in the optimization of industrial production processes 128 , 129 . Recent advancements in MS technologies provide significant benefits. For instance, rapid evaporation-ionization MS (REIMS) enables the direct analysis of both raw and processed food products, offering a reliable point-control method 130 . Additionally, the growth and broader use of structural lipidomics will enhance our understanding of lipid isomeric structures and their role in influencing sensory quality, stability, bioavailability, and the health effects of food products 131 , 132 . Lipidomics breakthroughs have also led to the discovery of bioactive compounds in food, such as plant-derived oxylipins, which can regulate inflammation and immune responses in consumers 133 , and TG-estolides, which serve as precursors to a novel class of fatty acid esters, FAHFAs, with antidiabetic potential 134 . Moving forward, a concerted effort to standardize and harmonize food lipidomics, through both analytical techniques and data integration strategies, is essential. Integrating lipid profile data across various food matrices into specialized databases (e.g., FoodDB and FoodData Central 135 ) would greatly support comprehensive, omics-based food research 136 . Mapping chemical exposures from diet will provide valuable insights to enhance health and reduce disease risk. Moreover, combining lipidomics data on dietary responses with GWAS focused on health outcomes will facilitate the development of nutrigenetic strategies, leading to personalized dietary recommendations 137 , 138 . The current deficit of dietary lipids to be produced and consumed according to WHO recommendations is estimated as 45 megaton (Mt) per year, which is projected to grow to 113 Mt by 2050 139 . However, compared to proteins, the composition and nutritional quality of lipids have received less focus when assessing food system sustainability. Lipidomics offers a powerful tool for developing sustainable strategies for lipid production, including the exploration of alternative food sources such as microalgae, new crops, insects, and even microbes 140 , 141 . For food producers and stakeholders, understanding how environmental factors, agricultural practices, processing, and preservation influence lipid quality is crucial to ensuring food safety, nutrition, and overall quality. Exploring lipidomes across the tree of life: mapping the molecular basis of diversity and evolution Recent discoveries underscore the minimal lipid requirements for life, with evidence that as few as two lipid species can sustain a minimal cell 142 . In striking contrast, lipid structural diversity across the Tree of Life is extensive. Despite conserved cellular architecture and core membrane functions, lipid composition varies widely among taxa, reflecting genetic, morphological and physiological adaptations to diverse ecological niches. Understanding the evolutionary and functional basis of this diversification requires comprehensive qualitative and quantitative lipidome profiling across biological diversity (Fig. 3 ). Fig. 3. Lipidomics in Earth and environmental sciences. Open in a new tab The expanding role of lipidomics in uncovering lipid biodiversity and tracing the evolution of lipidomes and metabolic pathways across the kingdoms of life. The figure highlights the ecological relevance of lipids in structuring food webs and regulating ecosystem dynamics, their role in organismal adaptation to environmental conditions, and the potential of lipidomic profiles as indicators for monitoring ecosystem health and assessing the impact of climate change. Recent advances in structural lipidomics, combined with phylogenetic approaches, are providing new insight into molecular evolution and species divergence. A key concept is the “lipid divide”, the deep biochemical separation between archaeal membranes, composed of isoprenoid ether-linked glycerol-1-phosphate (G1P) forming monolayers, and bacterial/eukaryotic membranes, composed of fatty acid ester-linked glycerol-3-phosphate (G3P) bilayers. This dichotomy is thought to have emerged after the Last Universal Common Ancestor (LUCA), leaving its membrane composition unresolved. One hypothesis proposes that LUCA had a mixed, heterochiral membrane containing both lipid types, which, due to its chemical instability, diverged into archaeal and bacterial architectures, giving rise to the “lipid divide”. This long-standing model has recently regained attention. An engineered E. coli strain producing both archaeal and bacterial phospholipids was shown to form stable hybrid membranes, challenging the assumption that mixed systems are inherently unstable 143 . In contrast, studies in Mycoplasma mycoides suggest that mixed lipid populations can impair membrane function, but likely through disrupted lipid-protein interactions rather than altered bilayer physics 142 . Recent discoveries of microorganisms with naturally occurring mixed membranes add a new dimension to this debate. Combined lipidomic and comparative genomic analyses indicate that members of the bacterial Fibrobacteres-Chlorobi-Bacteroidetes superphylum harbour a complete archaeal-like lipid-synthesis pathway 144 , whereas certain members of the archaeal Asgard superphylum encode components of the bacterial lipid-biosynthetic route 145 . The co-occurrence of both biosynthetic machineries may indeed support the existence of a mixed-membrane state in LUCA, offering a plausible bridge across the “lipid divide”. Systematic efforts to map lipid diversity across phylogenetic lineages are still in their early stages. While many lipid classes are evolutionarily conserved, the abundance and composition of their molecular species often vary markedly between taxa 146 . In addition, some lipid subclasses appear to be clade-specific: for example, glycoglycerolipids are typical of photosynthetic organisms, including algae and plants, while betaine lipids are found in algae, bryophytes, and some fungi, but are absent in seed plants 147 . Structural lipidomics continues to uncover novel lipid subclasses, such as maradolipids 12 and phosphorylated glycosphingolipids 148 in nematodes or phosphatidylhomoserine species in E. coli 149 , suggesting that a substantial fraction of lipid diversity remains unexplored. For newly identified lipids, functional assignment can be challenging. However, because structurally related lipids often share functional roles, comparative lipidomics provides a powerful framework for inferring lipid class biology. Maradolipids, glycolipids with a trehalose headgroup first identified in bacteria such as Mycobacterium and Corynebacterium and later found in C. elegans 12 , illustrate this principle. Although their functions remain incompletely defined, their enrichment in bacterial cell walls suggests roles in membrane permeability and barrier properties that are likely conserved in nematodes. Furthermore, for model organisms such as C. elegans , D. melanogaster , and mice the integration of genetic tools with lipidomic profiling, supports detailed studies of lipid function and regulation 150 , 151 . Emerging multi-omics approaches that combine lipidomics with transcriptomic and proteomic data, alongside refined comparative algorithms such as the LUX score 152 , 153 , are poised to generate mechanistic insights into lipid function and evolution across broad taxonomic groups (Box 5 ). Uncovering novel lipid subclasses, particularly in underexplored clades, requires combining high-resolution structural lipidomics with validation approaches such as labelling and metabolic incorporation 149 . Often next-generation de novo annotation strategies that go beyond conventional spectral library matching are required. The structural complexity of uncharacterized lipids often necessitates manual interpretation or the development of specialized computational pipelines 154 . To accelerate discovery, new algorithms are being developed, including molecular networking and reverse lipidomics, which allow annotation based on shared structural motifs rather than exact spectral matches. These efforts are increasingly supported by large-scale data repositories and taxonomically informed tools such as microbeMASST and plantMASST, which link LC-MS/MS spectra to over 1,300 microbial and 2,800 plant species, respectively 155 , 156 . Platforms like GNPS (Global Natural Products Social Molecular Networking) 78 further enable molecular networking to reveal structural relationships among unknown lipids. Together, these integrative advances are transforming lipidomics into a powerful chemotaxonomic tool, capable of revealing both deep evolutionary relationships and environmentally driven adaptations across the Tree of Life. Box 5 The comparative lipidomics approach reveals conserved biochemical motifs and traces the evolutionary lipid distribution. Lipids are more resistant to diagenetic degradation than proteins or nucleic acids, thus structural lipid features can persist in the geological record. Sterol lipids provide key examples. Saturated steranes and hopanes, derived from sterols and hopanoids, are widely used in sedimentary rocks as proxies for eukaryotic and bacterial biomass in ancient ecosystems 187 . Distinct sterol side-chain structures enable taxonomic inference: C27 sterols (e.g., cholesterol) are characteristic of eumetazoans and red algae, C28 sterols of fungi, and C29 sterols of plants and green algae. Comparative lipidomics of fossil steranes, combined with genomics, can identify organisms capable of producing specific sterol classes and constrain when these pathways evolved 188 , 189 . Thus, sterols and hopanoids act as molecular fossils preserved in sediments and are increasingly used as biomarkers and molecular clocks to track the co-evolution of metabolic pathways and lineages 187 . Lipidomics in eco-evo-devo research: uncovering the molecular dynamics of ecosystem evolution Lipids are increasingly appreciated as critical molecular markers for probing structure, connectivity, and function of ecosystems across the biosphere (Fig. 3 ). Lipidomics has emerged as a powerful approach to explore the ecological, evolutionary, and developmental (eco-evo-devo) roles of essential dietary lipids, offering fresh insights into the molecular foundations of biodiversity and ecosystem dynamics. Fatty acid (FA) profiling is widely used in both aquatic and terrestrial environments to trace trophic pathways and delineate food web architecture. The heterogeneous spatiotemporal distribution of dietary lipids mirrors complex biotic interactions and abiotic factors 157 . Despite their ubiquity, FAs display notable qualitative or quantitative specificity and diversity across taxa: bacterial branched-chain and cyclopropane FAs, ω−3 PUFAs from microalgae, enrichment of oleic acid in plants or linoleic acid in fungi serve as distinctive trophic biomarkers. This specificity makes FA profiling a valuable tool for ecological network analysis because of the “dietary routing” (i.e., incorporation of the food FA biomarkers into consumer’s lipidome). Advanced statistical frameworks such as Quantitative Fatty Acid Signature Analysis (QFASA) and Bayesian source-tracing models have been developed to reconstruct consumer diets from FA data in marine and terrestrial systems 158 – 161 . More broadly, the emerging field of ecological lipidology situates lipidomics as a lens to address fundamental questions in biodiversity, adaptation, and macroevolution, including the fitness consequences of lipid-specific foraging and the evolutionary impact of sterol or PUFA auxotrophy 162 . Lipidomics has advanced our understanding of environmental adaptation by revealing how membrane lipid composition responds to changing conditions. For instance, the ratio of saturated to unsaturated acyl chains in membrane lipids provides a sensitive indicator of homeoviscous adaptation in poikilothermic organisms such as bacteria, algae, and plants, enabling the maintenance of membrane fluidity across temperature gradients 163 . Recent studies of deep-sea fauna have implicated plasmalogens as critical to homeocurvature adaptation, supporting membrane integrity under extreme hydrostatic pressure 164 . While lipids have long served as biomarkers in oceanographic research 117 , their application at the lipidomics scale is more recent. A global survey of marine plankton lipidomes across 146 sites uncovered a strong correlation between lipid unsaturation indices and ocean temperature 165 . Crucially, temperature-induced variations in eicosapentaenoic acid levels enabled projections of its availability under future climate scenarios, highlighting potential risks to the nutritional quality of marine food webs. Despite significant advances, wide application of lipidomics in ecosystem research faces several challenges. The vast functional diversity of lipidomes across species, especially those adapted to extreme or specialized niches, remains largely uncharted, with many lipid species still unidentified. Accessing such organisms often necessitates costly, logistically complex expeditions 166 . Although lipidomics in eco-evo-devo research is nascent, expanding lipidome coverage beyond traditional model organisms has already demonstrated substantial promise. Comprehensive profiling of FA concentrations and distributions across diverse taxa is essential to discern the specificity and universality of lipid-based biomarkers, which could be expressed in either absolute or relative terms. To date, marine food webs have received more extensive lipidomic characterization than terrestrial systems. To address this disparity, a lipid library covering 50 basal resource species in soil food webs, including algae, bacteria, fungi, and plants, has recently been developed to enhance FA signature resolution and resource classification in terrestrial ecosystems 167 . However, integration of FA profiles across ecosystem boundaries, linking aquatic, terrestrial, above- and below-ground networks, remains limited, hindering efforts to resolve primary FA sources and trophic pathways at ecosystem and biosphere scales. Understanding how climate change, land use, and other environmental pressures reshape FA trafficking and distribution is vital for predicting ecosystem resilience, supporting bioremediation, guiding conservation, and safeguarding biodiversity and human nutrition. Future perspectives in lipidomics A remarkable expansion in lipidomics technologies, supported by the parallel development of computational tools, has positioned lipidomics and its applications at the forefront of modern biology and biomedicine. However, several open questions remain and define future directions in technological development (Box 6 ). Among these, a key challenge is the functional interpretation of lipid compositional changes. Current views on lipid functions are largely centred around biochemical pathways 168 , 169 , biophysical ontologies 170 , and fragmented knowledge of lipid-protein interactions in signalling. However, lipids rarely act at the level of individual species; instead, they function as supramolecular assemblies in the form of organelle-specific membranes and specialized membrane domains. Such lipid collectives most likely represent the true functional units responsible for biological activity; however, they remain challenging to define both qualitatively and quantitatively 171 . As knowledge in lipid biochemistry continues to expand, bridging the gap between lipidomics and function will require close collaboration between classical lipid biologists and analytical experts. Recent efforts to generate lipid atlases at increasing levels of spatial resolution (including subcellular structures 172 , specific cell types 36 ) and distinct tissues and organs 41 , 173 , represent important steps toward contextualizing lipid function. Leveraging the expanding knowledge base, tools such as LIPID MAPS Reaction Explorer can support the association of lipid species with corresponding proteins and genes, thereby facilitating cross-omics data integration. Moreover, the growing accessibility of omics technologies is enabling the simultaneous generation of multi-omics data from the same experimental systems, and new computational frameworks are being developed to support integrative, data- and knowledge-driven analyses 174 – 176 . This will be a critical step toward achieving functional lipidomics. Given the centrality of lipids to physiology across scales, the integration of lipidomics into biomedical research has strong potential for diagnostics and clinical translation. Major hurdles here will be the clinical performance validation, regulatory challenges, acceptance and finally implementation of such assays in a clinical setting, preferably with metrologically traceable, molar concentrations. Lipidomics enables the discovery of biomarkers across a broad spectrum of human diseases with advances in structural lipidomics further illuminating metabolic plasticity in diverse cancers 177 and revealing previously unrecognized lipid diversity in bacteria 149 . These insights are informing the development of personalized therapies and uncovering novel mechanisms that may be exploited to combat antimicrobial resistance. Lipidomics also becomes powerful tool for understanding the biology of ageing. Lipid alterations have emerged as robust correlates of biological age 4 , culminating in the development of “Lipid Ageing Clocks” 178 . Notably, lipidomic studies have revealed pronounced sexual dimorphism in lipidome remodelling during healthy ageing and disease development, exposing persistent gaps in biomedical knowledge and therapeutic designs. These findings directly support gender equality by underscoring the need for sex-inclusive biomedical research. Although the majority of lipidomics research has focused on human biology, its applications increasingly extend to environmental monitoring. Lipid biomarkers are emerging as sensitive indicators of ecosystem health 117 . The integration of lipidomic data in ecological monitoring and food web analysis positions lipidomics at the forefront of efforts to track and predict environmental change, making it an essential tool for exploring dynamic biosphere processes on a planetary scale. Advances in food lipidomics are becoming instrumental in shaping sustainable food systems and responsible production practices. These efforts illuminate the deep interdependence between ecosystem integrity and human health. In the broader context of human health and global food security, modern food and biotechnology industries are increasingly recognizing the value of data generated by lipidomics for assessing nutritional value, product safety, and quality control, and advancing personalized nutrition strategies. Lipidomics research now transcends human health and stretches to global benefit: it is being applied to study the molecular organization of organisms across all domains of life, offering insights into evolutionary biology and ecosystem function. Box 6 Open questions and future directions in lipidomics. ● Implementation of rigorous quality assurance frameworks and standardized operating procedures (for example, community-driven Lipidomics Minimal Reporting Checklist) to overcome current limitations in robustness and reproducibility ● Elucidation of the full complexity of natural lipidomes, including discovery of new lipid subclasses and deeper structural annotation of known species, particularly double-bond positional isomers. ● Resolution of cell-type and context specific lipid heterogeneity within tissues through high-resolution spatial lipidomics. ● Moving beyond static measurements towards dynamic analyses that capture lipidome plasticity and metabolic flux. ● Advancing from compositional cataloguing to functional lipidomics, addressing collective lipid behaviour and its mechanistic roles in biological systems. ● Improving interoperability among lipidomics datasets generated using diverse software platforms, partially addressed by emerging lipid annotation conversion tools and harmonized shorthand notation. ● Systematic characterization of novel lipid species through reverse lipidomics strategies and molecular networking frameworks. ● Development of robust computational approaches for multi-omics data integration. ● A conceptual shift towards functional lipidomics, requiring integrative frameworks that combine biochemical and biological knowledge with lipidomics-derived datasets. Conclusion The remarkable growth that lipidomics has experienced over the last two decades has been driven by advances in MS-based instrumentation and the parallel evolution of sophisticated bioinformatics tools capable of handling high-throughput data processing. The maturation of lipidomics has been propelled by coordinated efforts focused on education, collaboration, and standardization (Box 7 ). As lipidomics has evolved into a mature and versatile analytical discipline, it is increasingly well positioned to contribute to solutions addressing a range of societal challenges. Looking forward, continued technological innovation and interdisciplinary collaboration will be crucial to unlocking the full potential of lipidomics: from personalized medicine to sustainable ecosystem management, and heralding a new era of lipid-centred discoveries and applications. Box 7 Community-driven and collaborative efforts in lipidomics. Since its establishment in 2003, LIPID MAPS has served as a cornerstone resource, providing curated lipid databases, analytical tools, and educational materials that continue to guide the global lipidomics community. The Pan-European Network in Lipidomics and Epilipidomics (EpiLipidNET), launched in 2021 under the EU COST programme, now connects over 450 researchers across multiple disciplines, fostering collaborative research and knowledge exchange. On a global scale, the International Lipidomics Society (ILS), founded in 2019, champions open communication, data transparency, and methodological harmonization. The LipidMet Interest Group within the Metabolomics Society further bridges lipidomics and metabolomics fields, advancing integrated systems for metabolism research. These initiatives have been instrumental in developing best practices and quality assurance frameworks. For example, the Lipidomics Minimal Reporting Checklist, introduced by ILS and the Lipid Standards Initiative (LSI) 52 , standardizes the evaluation of lipidomics data quality in publications. The Oxylipin Analysis Interest Group has published detailed guidelines addressing technical and interpretative challenges specific to oxylipin workflows 190 . Meanwhile, LIPID MAPS continues to expand by integrating community-generated oxylipin spectral libraries and, together with EpiLipidNET, hosts the Lipidomics Tools Guide to aid bioinformatics tool selection 53 . Importantly, ILS-led ring trials targeting quantitative analysis of clinically relevant lipids such as ceramides and bile acids in standard matrices like human plasma, mark crucial milestones toward clinical implementation. As these international networks grow, they not only strengthen scientific collaboration but also lay the foundation for applying lipidomics to address urgent societal challenges, ranging from public health and precision medicine to food security and environmental sustainability, thus solidifying lipidomics as a versatile platform with broad scientific and translational impact. Author contributions The manuscript was conceptualized by M.F. and M.R.D. All the authors were involved in original writing of the sections (Technological Developments in Lipidomics—A.S.; J.B-M.; J.K.; K.S.; M.F.; M.W.; M.R.W; O.F.; P.P.; P.N.; V.O’D.; X.H.; Y.X.; Computational Solutions for Lipidomics—L.G.; M.F.; O.F.; V.O’D.; X.H.; Y.X.; Clinical Lipidomics—A.K.B.; F.G.; J.G.; K.S.; M.H.; M.W.; M.R.W; O.V.; P.M.; Lipidomics in Human Toxicology – C.M.S.; M.O.; Food and Nutritional Lipidomics—B.Y.; L.R.; M.R.D.; Sn.P.; T.R.; Exploring Lipidomes Across the Tree of Life—J.W.; M.R.D.; M.W.; Si. P.; X.L.G.; Lipidomics in Eco-Evo-Devo Research—C.M.S.; A.P.; M.F.; S. Pe.), which was then compiled by M.F., P.P., P.N., V.O’D. and C.M.S.; All authors contributed to the critical revision of the manuscript and approved the final version. Peer review Peer review information Nature Communications thanks Jeremy Koelmel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Funding This publication is based on work from COST Action EpiLipidNET, CA19105, supported by COST (European Cooperation in Science and Technology). M.F. lab is supported by “Sonderzuweisung zur Unterstützung profilbestimmender Struktureinheiten” by the SMWK to TUD, Deutsche Forschungsgemeinschaft (FE 1236/5-1; FE 1236/8-1; TRR 412/1 – 535081457), and Bundesministerium für Forschung, Technologie und Raumfahrt (031L0315A, DEEP_HCC; 01EJ2205A, FERROPath). M.H. acknowledges the support of ERC Adv grant No. 101095860. M.Witting acknowledges funding by The European Union Horizon 2020, Marie Curie Actions, grant number 101073062. L.G. acknowledges the support of the Presidenza del Consiglio dei Ministri-Dipartimento per la Coesione Territoriale e per il Sud (CURI project, n. E67G23000200001). J.G. acknowledges the Ministry of Education and Science within the project “Excellence Initiative - Research University”. J.B.M acknowledges the support of MetaboHUB infrastructure funded by the Agence Nationale de la Recherche under the France 2030 programme (MetaboHUB ANR-11-INBS-0010; MetEx+ ANR-21-ESRE-0035; MetaboHUB (JVCE) ANR-24-INBS-0012). B.Y. acknowledges the grants of the Research Council of Finland (Decision No. 356891) and Business Finland (Decision No. 1675/31/2023). X.H. acknowledges the partial support of National Institute on Aging grants R01AG061872, R01AG085545, P30AG013319, and P30AG044271. M.R.D. lab is supported by FCT – Fundação para a Ciência e a Tecnologia I.P., under the project/grant UID/50006 + LA/P/0094/2020 (doi.org/10.54499/LA/P/0094/2020 (Centro de Estudos do Ambiente e Mar (CESAM)) and UID/50006 - Laboratório Associado para a Química Verde - Tecnologias e Processos Limpos. J.W. acknowledges the National Institutes of Health (R35GM152060). S. Petrović. acknowledges the support of The Ministry of Science, Technological Development and Innovation of the Republic of Serbia, Contract No. 451-03-136/2025-03/200015. P.P. acknowledges funding by the Deutsche Forschungsgemeinschaft (grant number 565977762). F.G. gratefully acknowledges the financial support of the Federal Ministry of Economy, Energy and Tourism of Austria and the National Foundation for Research, Technology, and Development of Austria to the Christian Doppler Laboratory for Skin Multimodal Imaging of Aging and Senescence. Competing interests The authors declare no competing interests. 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