Open research questions in Metabolomics and Mass Spectrometry Studies

What the literature leaves open

• While transcriptome-wide association studies (TWAS) have identified disease-associated genes, strategies that integrate the metabolome remain underexplored.

  MetaboXcan: A multiomic framework linking genetically predicted metabolites, gene expression, and complex traits · 2026 · DOI

• Genetically regulated molecular phenotypes, such as gene expression and metabolites, are widely thought to mediate the effect of disease-relevant genetic loci identified by genome-wide association studies (GWAS); however, molecular mechanisms connecting genetic variation to disease remain poorly understood.

  MetaboXcan: A multiomic framework linking genetically predicted metabolites, gene expression, and complex traits · 2026 · DOI

• The recent advances in genome-scale metabolic modelling outlined above have resulted in an attractive portfolio of computational modelling tools amenable towards the creation of digital metabolic twins (DMTs), i.e., digital replicas of an individual’s metabolism, for predictive decision-making along the nutritionhost-microbiome-health axis (Fig. 1). The constituting resource are continuously updated to capture the growing knowledge content. The human reconstructions have grown from ~3,000 reactions and metabolites (Recon1(78)) to now ~80,000, including sex-, organ- and cell type-specific content in the WBMs(53). The microbiome efforts originated from reconstructing individual microbes(79) to entire resources now encompassing almost a quarter million microbes(64). However, the current reconstructions primarily capture the metabolism of endogenous metabolites, i.e., further efforts are required to incorporate information on xenobiotics, including phytochemicals(80). Currently, in silico diets are created based on information derived from publicly available databases, such as the USDA FoodData Central(81) or 4 B. Nap et al. FRIDA(82), which capture individual metabolite-level information for different food items. However, the metabolite coverage remains limited, at least in part due to current challenges in the required analytical measurement challenges in the field of foodomics(83). Further, efforts are ongoing to specify the molecular composition of complex carbohydrates, such as starch or fibre, which are usually too broadly captured in current databases(84). Increasing the content of plant-specific metabolism both in current nutrition databases and in the genome-scale reconstructions will allow for more precise and accurate simulations with the WBMs, enabling better prediction of dietary supplementation from foods in the same food groups. Future developments will expand the scope of modelling from diet alone to a more holistic capture of health modulators, such as medication use, physical activity, lifestyle factors or environmental exposures, all of which influence metabolic function. Incorporating such information will allow models to capture diet–host–microbiome interactions in the broader context of modulators of human metabolism (e.g., environmental chemicals or lifestyle factors) and support nutrition strategies tailored to specific clinical conditions or health goals. Current workflows have emerged primarily from a pre-clinical fundamental research context, with a focus on computational biology. Therefore, the specific tools and skills to generate COBRAderived mechanism-informed hypotheses remain largely confined to a specialist computational modelling community. To enable in silico modelling-enabled decision support systems amenable to providing dietary advice, there is a need to collaborate with the intended end users, such as nutritionists, to create tools which align with their needs and expectations.

  Whole-body metabolic modelling and its prospects in precision nutrition · 2026 · DOI

• Major depressive disorder (MDD) is a severe psychiatric disorder that affects more than 350 million people worldwide, yet its biomolecular mechanisms are incompletely understood, and clinically applicable markers remain elusive.

  Biochemical fingerprinting of human scalp hair reveals endocannabinoid related compounds as potential biomarker indicators of altered mitochondrial bioenergetics in immune cells from female patients with major depressive disorder · 2026 · DOI

• Finally, we applied the model to data from the NICHD Fetal Growth Studies for illustrative purposes; this demonstration is limited to assessing internal discrimination within the sampled set.

  Sparse time-varying log-ratios for longitudinal high-throughput sequencing data · 2026 · DOI

• While our findings offer valuable insights, they are based on cross-sectional data and require validation in larger, longi- tudinal cohorts to capture the dynamics of metabolic changes over time. Future studies should include more patients to strengthen generalizability. Nonetheless, a strength of our study is that, even with only five patients per group, we were able to detect consistent and biologically meaningful differences across disease stages. Integration with transcriptomic and lipidomic data could further provide a more comprehensive systems-level understanding of SARS-CoV-2–induced perturbations. In addition, the potential influence of pharmacological treatment and oxygen therapy on the observed metabolomic profiles cannot be completely excluded. All patients were managed under a uniform institutional protocol; however, subtle effects related to corticosteroid use, anticoagulant therapy, or differences in oxygen supplementation may have contrib- uted to intergroup variability. Future studies involving larger cohorts and controlled treatment regimens will be required to disentangle these confounding factors. Furthermore, blood samples were collected at a single, early disease timepoint, within the first 2 hours of hospitalization, immediately after molecular confirmation of SARS-CoV-2 infection. Although this standardized approach minimized confounding due to treatment effects, it does not capture temporal metabolic shifts occurring throughout the disease course. Future longitudinal metabolomic studies, spanning multiple time points from early infection to recovery, will be essential to elucidate the dynamic trajectory of host metabolic reprogramming in COVID-19. Therefore, the observed metabolomic signatures should be interpreted with caution, as they may reflect a combination of disease-related and treatment-related effects. Another limitation of this study is the unequal sex distribution within the study cohort (13 males and 7 females), which reflects the demographic pattern of hospitalized COVID-19 patients during the study period. Although sex-specific met- abolic differences cannot be excluded, the limited cohort size did not allow for reliable subgroup analysis by sex. Future studies with larger, sex-balanced cohorts are needed to validate and extend our observations. Further, targeted validation of key metabolites using mass spectrometry and functional assays is warranted to confirm their role as prognostic or diag- nostic biomarkers. Furthermore, because metabolite identification was based on putative annotations without validation with authentic standards, the results should be interpreted primarily at the pathway and systems levels.

  Severity-dependent metabolic rewiring in COVID-19 based on untargeted metabolomic profiling of patient plasma · 2026 · DOI

• Notably, FSV was associated with reduced hepatic M3G levels, and molecular docking raised the hypothesis that M3G might interact with components of the PI3K-Akt signaling pathway; however, this possibility has not been directly tested and requires experimental validation.

  Integrated metabolomics and computational analysis suggest that a Sanghuangporus vaninii-based formulation alleviates T2DM in mice and modulates hepatic morphine-3-glucuronide axis · 2026 · DOI

• In untargeted LC–MS/MS datasets, this combination of structural diversity and spectral similarity complicates annotation, particularly when reference spectra are sparse or unavailable.

  A MassQL‐Based Framework for Rule‐Guided MS/MS Class‐Level Retrieval and Analog Discovery of Cannabinoids · 2026 · DOI

• Although metabolomics represents a relatively new realm of science that emerged not more than two decades ago, it has already exhibited considerable potential in agricultural and food science research, especially pertaining to the elucidation of metabolome and lipidome changes in response to environmental or pathophysiological stimuli; thereby contributing to the crop production improvement. Despite the rapid advances in the techniques of NMR and MS coupled to GC or LC, and their broad applications to the field of metabolomics, thus far, no single analytical platform by itself can offer a holistic coverage of the metabolome in plants. While NMR possesses the advantage of capturing the structural information of metabolites as well as their abundances, LC-MS emerges as the probable optimal choice in terms of the acquisition of global metabolome in plants, and the rapid advances in MS instrumentation is greatly driving its diverse applications in agricultural sciences. In face of adverse environmental conditions, a series of primary metabolites (osmolytes, osmoprotectants) and secondary metabolites (defense metabolites) in plants accumulate to enhance their stress tolerance, thus it is impossible to generate plants with high levels of stress adaptation via engineering the levels of a just a few selected metabolites. Future studies will focus on increasing the resolution and coverage of the metabolome to obtain a comprehensive understanding of how plants adapt themselves to environmental stresses, providing new avenues to increase crop production. Apart from the identification of critical metabolic pathways to uplift crop production, metabolomics also confers a powerful tool to assess food crop quality. For example, global profiling of plant/crop metabolome could also facilitate in the identification of genetic manipulations that may serve to increase the nutritional value and quality of crops. For instance, previous lipidomic analysis has revealed that GmMYB73 manipulation promotes lipid accumulation in soybeans, thus providing a potential avenue for increasing oil production in legume crop plants. Another example is given by the establishment of an integrated lipidomic approach comprising multiple analytical arms specifically tailored to the analysis of the fine lipidomic fingerprints of palm oil, currently the leading edible oil consumed around the globe, which could be applied to the evaluation of oil quality and the health benefits/risks associated with different oil refinement techniques. Again, resolution of the metabolome (lipidome) is instrumental in such applications, since a highly sensitive analytical methodology is indispensable in detecting trace amounts of critical components such as oxidized lipids including oxidized triacylglycerols that could be auxiliary in determining the oil quality. In conclusion, while metabolomics holds great promise in forwarding agricultural research, a pressing need exists to expand its analytical capacity in order to derive fully integrated functional networks of metabolites that have true biological meaning. The systemic construction of metabolome libraries with sufficient resolution is therefore expected to further broaden the translational applications of metabolomics in various sub-arenas of agricultural research. Finally, the plethora of metabolomics data also calls for the development of competent information processing tools that allow data to be processed, integrated, interpreted, and verified alongside with proteomics and transcriptomics strategies in order to fully unravel the various intricate biological networks under study.

  Metabolic footprint of sepsis and septic shock: A narrative review · 2026 · DOI

• The continual evolution of lipidomics toward single-cell and subcellular resolution marks a paradigm shift in analytical chemistry and molecular biology. Miniaturized chromatog- raphy-based workflows—encompassing micro- and nano- flow LC, capillary, and chip-integrated formats—have rede- fined what is technically achievable in lipid analysis. These approaches maximize sensitivity and structural resolution while conserving scarce samples, addressing the inherent challenge of quantifying nonamplifiable biomolecules, such as lipids, at cellular and subcellular scales. When coupled with advanced ionization interfaces, ion mobility spec- trometry, and high-resolution mass analyzers, miniaturized LC-MS platforms now deliver unparalleled analytical depth and quantitative precision. Though many applications cur- rently are working with single-cell equivalents, e.g., diluted extracts, they pave the way for true single-cell applications. Using single-cell equivalents, analytical systems can be tested for their robustness and reproducibility, which would not be possible with single-cell samples. However, improvements on the analytical side are only one part. True single-cell application requires also improved sample handling. Methodological innovations in microflu- idic extraction, automated liquid handling, and solvent-min- iaturized sample preparation have further enabled reproduc- ible lipid analysis from picoliter-scale volumes. This also includes, for example, the addition of IS for accurate quan- titation. While in general approaches for quantitation shall not differ between bulk and single-cell lipidomics (e.g., addi- tion of IS as early as possible), the small volumes in true single-cell applications require some rethinking. Currently, no comprehensive workflow for accurate quantification of lipids in single cells exists. However, these will be required for broader insights into lipid biology, e.g., enable calcula- tion of, for example, PE-to-PC ratios for single cells, etc. A major focus of the field needs to be enabling advances in quantification. These advances are complemented by improved data acquisition and standardization strategies, Miniaturized chromatography-based lipidomics methods towards single-cell analysis including isotope-labeled internal standards, isotope-labeled cell extracts, and reference materials such as NIST SRM 1950, which ensure quantitative reliability across labora- tories. Compared to imaging-based approaches, miniatur- ized LC-MS provides superior separation of isomeric and isobaric lipids, minimizes adduct formation, and reduces ion suppression, hence, transforming sensitivity into true chemical specificity. Looking forward, the integration of automation, multi- plexed separations, and AI-driven data analysis will accel- erate the transition of lipidomics from specialized research to scalable, high-throughput applications. Hybrid MSI-LC platforms and subcellular extraction methods will bridge spatial and structural resolution, enabling mechanistic studies of lipid organization within tissues, organelles, and cellular microenvironments. The next frontier lies in fully integrated multi-omics pipelines, where lipidomic data are contextualized alongside genomic, transcriptomic, and prot- eomic layers to construct a holistic view of cellular function. Importantly, these advances do not eliminate the fundamen- tal trade-offs between sensitivity, robustness, throughput, and spatial resolution, but instead redefine the operational space in which these parameters can be balanced. As miniaturization and computation continue to converge, lipidomics is poised to move beyond cataloguing molecu- lar species toward predictive, dynamic modeling of lipid metabolism in health and disease. Miniaturized chromatog- raphy-based workflows thus stand not merely as an analyti- cal refinement but as a foundation for the next generation of systems lipidomics—quantitative, spatially resolved, and functionally integrative. Author contribution Conceptualization—MW. Original draft writing—KFR. Data curation and investigation—KFR. Writing (review and editing)—KFR, MW. Supervision—MW. Funding and resources—MW. Funding Open Access funding enabled and organized by Projekt DEAL. This research is funded by the European Union, through the Horizon 2020-Marie Skłodowska-Curie Actions Harmonizing and Unifying Blood Metabolomics Analysis Networks (HUMAN) Project, grant number 101073062.

  Miniaturized chromatography-based lipidomics methods towards single-cell analysis · 2026 · DOI

• As miniaturization, automation, and computational integra- tion continue to advance, lipidomics is transitioning from descriptive cataloguing to mechanistic, high-resolution biology. The convergence of chromatographic precision, spatial imaging, and multi-omics integration will empower researchers to dissect lipid metabolism with unprecedented depth and context. These technological and conceptual innovations position miniaturized chromatography-based lipidomics at the forefront of analytical science, driving the field toward comprehensive single-cell and subcellular lipid profiling with direct relevance to systems biology and trans- lational medicine. Lipid diversity is also a challenge in both data acquisi- tion and analysis. Single, end-to-end workflows combining extraction and detection are limited and often fail to gener- ate a comprehensive lipidomic profile for any given sample [102]. Some lipid classes are also more abundant than oth- ers in MS-based experiments, leading to the suppression of other lipids [102]. Additionally, in most samples, the natural abundance of lipid classes should be considered (e.g., mem- brane versus signaling lipids), as these factors will influence which classes are detected in MS. In traditional lipidomics analysis, the use of “bulk” samples is necessary to minimize variations and heterogeneity [103]. While this results in an averaged snapshot of the global lipidome and the general biochemical state of the sample [103], it is only a portion of the overall picture. At single-cell resolution, the lipidome can be probed in greater detail, unmasking cellular intrinsic heterogeneity and capturing the spatial distribution of rel- evant biomolecules. Cell diversity and the heterogeneity of cellular interac- tions are fundamental questions that single-cell analysis aims to address. Compared to bulk sample lipidomics (and metabolomics), measuring biomolecules at the single-cell level is more challenging and is usually done only when information from larger samples is inadequate. Moreover, when studying disease progression and cell microenviron- ments, single-cell lipidomics and metabolomics can pro- vide even greater detail than bulk samples, especially in the absence of spatial information on lipid and metabolite distribution. Another application of single-cell lipidomics and metabo- lomics would be the analysis of liquid biopsies from cir- culating tumor cells (CTC), which are potential vectors of cancer metastasis [104]. Single-cell analysis of metabolites is beneficial for CTC detection, as it can provide informa- tion about the disease state faster than other methods. Aside from identifying and quantifying metabolites of individual cells, single-cell omics methods can be used for in-depth mechanistic studies, including host-pathogen interactions in infected cells [105, 106] and host-microbiome investigations [107]. Finally, single-cell analysis in the context of spatial information will become very important. Cells do not work in isolation in tissues; they form distinct zones or parts of organs. Several workflows focused on measuring single cells from cell cultures or dissected tissues. Technologies such as laser dissection and microcaption allow the isolation of specific cells from regions of interest in a tissue section. This led to the development of deep visual proteomics, com- bining spatial information with the advantages of nanoLC- MS/MS [108]. In a similar vein, deep visual lipidomics is expected to emerge and enhance our understanding of lipids K.F Rellin, M. Witting and their complex interactions, as illustrated by this study using a laser microdissection-coupled shotgun lipidomics platform that enables quantitative, spatially resolved lipi- dome analysis from small tissue regions [109]. Similarly, deep lipidome coverage is another goal of miniaturized and single-cell lipidomics, aiming to capture the full structural and quantitative diversity of cellular lipids. In the era of machine learning, big data, and AI-integrated workflows, deep lipidome analysis will enable quantitative mapping of cellular heterogeneity, uncovering lipid-driven regulatory mechanisms and biomarker signatures across developmen- tal and disease states.

  Miniaturized chromatography-based lipidomics methods towards single-cell analysis · 2026 · DOI

• Future studies will focus on more targeted sampling of specific brain regions, such as the hippocampus and prefrontal cortex, to achieve a better understanding of the localized effects of YHD. Future studies should focus on exploring the intricate relationships between YHD’s metabolic, neurochemical, and inflammatory effects to fully elucidate its therapeutic potential.

  Exploring the mechanism of Yin Huo decoction in PCPA-induced depression mice: a metabolomics and network pharmacology approach · 2026 · DOI

• Future research should focus on identifying the specific factors driving the unex- plained environmental component, testing whether targeted interventions can modify the score, and determining whether such changes correlate with improved health outcomes. Further research is needed to determine whether the MetaboHealth score can serve as a useful indicator for monitoring responses to interventions aimed at promoting healthy ageing.

  The MetaboHealth score is 40% heritable and is influenced by frailty status, BMI, and smoking in Swedish twins · 2026 · DOI

• While the residual-based LDA approach helps accommodate inter-individual metabolic variability, the biological interpretation and mechanistic understanding of why LysoPC a C17:0 serves as a biomarker in morbid obesity remains unexplored.

  Metabolomics reveals LysoPC a C17:0 (LPC 17:0) as candidate biomarker for personalized medicine in morbid obesity · 2026 · DOI

• The dataset is imbalanced with nearly 2.5:1 ratio of obese to control participants, requiring adjustment of LDA classification threshold to the proportion between groups rather than equal priors.

  Metabolomics reveals LysoPC a C17:0 (LPC 17:0) as candidate biomarker for personalized medicine in morbid obesity · 2026 · DOI

• The study used a small final sample size of 28 plasma samples (20 obese patients and 8 controls) after quality control and outlier exclusion, which limits statistical power and generalizability.

  Metabolomics reveals LysoPC a C17:0 (LPC 17:0) as candidate biomarker for personalized medicine in morbid obesity · 2026 · DOI

• While the study identified 1,362 distinct secondary metabolites and demonstrated ecological-metabolite correlations, the scalability of this approach to larger metagenomic datasets and diverse ecosystems is not addressed.

  Innovative Approaches in Drug Discovery by Leveraging Molecular Ecology for the Identification of Novel Therapeutics · 2026 · DOI

• The pathway-level analysis aggregated expression changes across 2,229 pathways, but the paper does not discuss potential limitations of this aggregation approach or validation at the individual gene level against external databases.

  KORE-Map 1.0: Korean medicine Omics Resource Extension Map on transcriptome data of tonifying herbal medicine · 2024 · DOI

• INTRODUCTION: Assessing batch correction methods remains a major challenge in metabolomics, as no consensus currently exists for a generic and reliable evaluation strategy.

  Integrated workflow for univariate and multivariate evaluation of batch correction reliability · 2026 · DOI

• Citrus flavor and nutritional quality are closely tied to metabolite composition, yet comparative metabolic dissection of fruit pulp traits across citrus subspecies remains insufficient.

  Preliminary study on comparative non-targeted metabolomics analysis sheds light on the chemical diversity of citrus fruit pulps · 2026 · DOI

• However, the molecular mechanisms underlying decreases in blood pressure induced by chronic hypoxia exposure in spontaneously hypertensive rats (SHRs) remain unclear.

  Integrated proteomics and metabolomics reveal mechanisms of blood pressure reduction in spontaneously hypertensive rats under hypoxic conditions · 2026 · DOI

• This work was limited by the quality of the submissions in public repositories, espe- cially the quality and quantity of annotations reported by the submitters.

  Network-based integration of metabolomics data from large-scale repositories · 2026 · DOI

• Despite its potential, metabolomics faces challenges such as technical complexity, biological variability, lack of standardisation, and the need for advanced statistical methods.

  Metabolomics in sepsis-associated acute kidney injury research · 2026 · DOI

• Metabolic disruptions are widely observed, yet their involvement in the molecular aetiology of AD remains underexplored.

  Hyperglycosylation is a metabolic driver of Alzheimer’s disease · 2026 · DOI

• The spontaneous adsorption of biomolecules onto nanoparticle surfaces has been extensively characterized at the protein level, but the metabolite corona remains poorly defined while being physicochemically and biologically distinctive.

  Chemically Engineered Carbon Nanotubes Map Class-Selective Metabolite Enrichment from Human Plasma · 2026 · DOI

Most-cited papers in Metabolomics and Mass Spectrometry Studies

• MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation · Nucleic Acids Research · 2024 · 1,974 citations
• Wekemo Bioincloud: A user‐friendly platform for meta‐omics data analyses · iMeta · 2024 · 259 citations
• MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics · Nature Communications · 2024 · 248 citations
• Integrative plasma and fecal metabolomics identify functional metabolites in adenoma-colorectal cancer progression and as early diagnostic biomarkers · Cancer Cell · 2024 · 136 citations
• A preliminary metabolomic analysis of older adults with and without depression · International Journal of Geriatric Psychiatry · 2006 · 123 citations
• Blood protein assessment of leading incident diseases and mortality in the UK Biobank · Nature Aging · 2024 · 122 citations
• microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data · Nature Microbiology · 2024 · 111 citations
• MS-DIAL 5 multimodal mass spectrometry data mining unveils lipidome complexities · Nature Communications · 2024 · 110 citations
• Lipidome atlas of the adult human brain · Nature Communications · 2024 · 105 citations
• xiVIEW: Visualisation of Crosslinking Mass Spectrometry Data · Journal of Molecular Biology · 2024 · 105 citations

Most recent work

• Assessing the metabolomics "dark matter" by a detectable khipu model · bioRxiv · 2026
• Structure-informed deep generation enables de novo metabolite annotation in untargeted metabolomics · Nature Communications · 2026
• Hyperglycosylation is a metabolic driver of Alzheimer’s disease · Nature Metabolism · 2026
• Comparison of Liquid Chromatography- and Nano-Electrospray Ionization-Mass Spectrometry Approaches for Single-Cell Metabolomics · Analytical Chemistry · 2026
• Comprehensive lipidomic profiling of dietary Indian millets and identification of fatty acid esters of hydroxy fatty acids by untargeted LC/MS · Food Chemistry · 2026
• A seven-year longitudinal study of the Alzheimer's disease metabolome. · medRxiv · 2026
• Longitudinal Plasma Metabolomics Guides Dynamic Risk Assessment and Dietary Modulation for Esophageal Squamous Cell Cancer Chemoimmunotherapy · Cancer Discovery · 2026
• MargheRita: streamlining MS-DIAL output analysis and metabolite identification in R · bioRxiv · 2026
• Microextraction and Microsampling Strategies in Lipidomics: Current Trends and Opportunities · Canadian Journal of Chemistry · 2026
• Anxiety Disorder Types From a Metabolomics Perspective: A Mendelian Randomization Analysis Based on 1400 Plasma Metabolites · Brain and Behavior · 2026

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