The high sodium content in traditional soy sauce presents significant public health concerns, particularly related to hypertension and cardiovascular diseases. However, reducing salt content often disrupts microbial ecology and impairs flavor formation during fermentation. To overcome this challenge, we developed synthetic microbial communities (SynMCs) for reduced-salt (13% NaCl) moromi fermentation under traditional sun-brewing conditions. Using integrated multi-omics analyses, we identified an optimal consortium (SMC-L1) incorporating Tetragenococcus halophilus T10 as a key lactic acid bacterium alongside functional yeast strains. This defined community maintained fermentation stability while significantly enhancing flavor-relevant biochemical profiles. SMC-L1 inoculation markedly improved key quality parameters, increasing total nitrogen by 40.8% and amino acid nitrogen by 56.7%. Furthermore, it elevated critical metabolites including organic acids, particularly succinate, free amino acids, and short-chain esters. Network analysis revealed robust ecology-metabolite relationships: Tetragenococcus abundance correlated with succinate production and ester synthesis, while Aspergillus dynamics corresponded with free amino acid accumulation. These findings highlight how targeted microbial consortia can reprogram metabolic networks under salt-reduced conditions. From a food microbiology perspective, this study demonstrates that rational design of microbial communities can effectively decouple salt reduction from flavor deterioration in fermented foods. The metabolic pathways observed, particularly the anaerobic TCA cycle activity connecting Tetragenococcus to succinate accumulation, provides mechanistic insights into microbial adaptation to reduced-salt environments. This approach offers a viable strategy for developing healthier fermented products without compromising their sensory characteristics, advancing both fundamental knowledge and practical applications in food biotechnology.

BackgroundFragaria nilgerrensis is a wild diploid strawberry species that represents a rich source of genetic variations with potential for enhancing fruit quality traits. However, the transcriptional regulation of changes in fruit quality relevant metabolites during F. nilgerrensis fruit development and ripening has not been investigated. Thus, this study analyzed the changes and accumulation of sugars, organic acids, phenolic acids and flavonoids at the four developmental stages of F. nilgerrensis fruit.ResultsD-sucrose, raffinose, D-trehalose, melibiose and isocitric acid increased as fruit developed. In terms of phenolic acids and flavonoids, cinnamic acid, hydroxycinnamic acid, coumarin, coniferin, anthocyanidins, rutin, and nicotiflorin accumulated as fruit developed. Conversely, contents of sinapoyl malate, coniferaldehyde, sinapinaldehyde, coniferyl alcohol, quercetin, gallocatechin, eriodictyol, luteolin, phloretin, and naringenin were decreased. The expression levels of key structural genes that corresponded with metabolite changes were identified. These genes included RFS (LOC101297814) and IDH(LOC101296705) in saccharide and organic acid metabolism, PAL (LOC101315259), BG (LOC101313585), F5H (LOC101307828), CCR (LOC101315149), CAD (LOC101306416 and LOC101309917), GT5 (LOC101296671), CHS(LOC101298162 and LOC101298456), LAR(LOC101306809), FLS(LOC101303260, LOC101309876, and LOC101302485), and ANR (LOC101292386) in phenylpropanoid and flavonoid biosynthesis pathways. Correlation analysis revealed that multiple transcription factor families were involved in the saccharide, phenylpropanoid and flavonoid biosynthesis, among which, AUX/IAA (LOC101298379), WRKY (LOC101302596), and AP2/ERF(LOC101295372)TFs were significantly correlated with saccharide synthesis. The effects of AP2/ERF (LOC101291560), AUX/IAA (LOC101298379), MYB (LOC105352442), and WRKY (LOC101302596 and LOC101295677) TFs were significantly correlated with cinnamic acid accumulation.ConclusionsThis study identified key metabolites, structural genes, and transcription factors influencing fruit quality-related metabolic changes during fruit development in F. nilgerrensis. These findings may facilitate the utilization of wild strawberry resources for breeding novel cultivars.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12870-025-07590-8.

Abstract Treating glioblastoma remains a therapeutic challenge in oncology. Viroimmunotherapy is emerging as a promising new treatment approach. Our laboratory developed an oncolytic adenovirus, termed Delta-24-RGD, which has been tested in clinical trials for recurrent glioblastoma patients with encouraging results (NCT00805376, NCT02798406). These clinical studies, together with preclinical data, showed that the efficacy of Delta-24-RGD was due to direct tumor cell oncolysis and indirect activation of anti-tumor immunity. To further leverage this immune component, our group generated Delta-24-RGDOX, an armed version of Delta-24-RGD that expresses the T-cell activator OX40 ligand (OX40-L). Here, we aim to further improve the efficacy of Delta-24-RGDOX by targeting factors that maintain the immunosuppressive characteristic of gliomas. RNA sequencing of glioma-bearing mice treated with Delta-24-RGDOX revealed interferon gamma (IFNγ) as the top upstream regulator. In addition, we observed upregulation of the tryptophan metabolism and immunoregulator IDO1-AhR network in these treated tumors. We performed gene expression analysis on 38 tumor biopsies from the Delta-24-RGD and Pembrolizumab clinical trial (NCT02798406), identifying a significant positive correlation between IFNγ and IDO1 (a main regulator of AhR activation), identifying this axis as clinically relevant. The IFNγ signaling converges through the JAK family, which induces the activation STATs and the subsequent engagement of inflammatory programs. In vitro studies demonstrated that Delta-24-RGDOX treatment of glioma cells increased STATs and IDO1 expression, resulting in AhR nuclear translocation and transcriptional activation. Of clinical interest, the IFNγ-induced IDO1 expression was completely abrogated by concomitant treatment with Ruxolitinib, a clinically approved JAK inhibitor. Using syngeneic glioma models, we showed that combination therapy with Delta-24-RGDOX and Ruxolitinib yielded better survival outcomes compared to monotherapy controls. In summary, this study illustrates the feasibility of combining virotherapy with drugs targeting immunosuppressive pathways in gliomas and provides a strong rationale for translating this strategy into the clinical setting.

Addressing the global health challenge of type 2 diabetes requires safer therapeutic options beyond conventional treatments with known limitations. This systematic evaluation explores the potential of β-glucans from diverse sources in diabetes management. By integrating existing research on gut microbiota modulation and short-chain fatty acid metabolism, we elucidate the mechanisms through which β-glucans alleviate diabetes and its complications, including neurodegeneration, cardiovascular impairment, and renal fibrosis. The analysis reveals how β-glucans remodel microbial metabolic networks through short-chain fatty acid production and bile acid cycle regulation, modulate energy metabolism pathways, and disrupt oxidative stress-inflammatory crosstalk. Based on comprehensive safety assessments, we establish scientific intake guidelines while investigating β-glucans' structural stability and functional retention during food processing as well as their targeting efficiency and controlled-release properties as nanocarriers for drug delivery. This work provides a theoretical foundation for the dual application of β-glucans in precision nutrition and intelligent drug development for diabetes management.

Sphingolipids govern diverse cellular processes; their dysregulation underlies numerous diseases. Despite extensive characterizations, understanding the orchestration of the sphingolipid network within living organisms remains challenging. We established a versatile genetic platform of CRISPR-engineered reporters of 52 sphingolipid regulators, recapitulating endogenous gene activity and protein distribution. This platform further allows conditional protein degradation for functional characterization. In addition, we developed the biosensor OlyAw to detect ceramide phosphoethanolamine and visualize membrane raft dynamics in vivo. Using this platform, we established comprehensive profiles of the sphingolipid metabolic network in the brain at the transcriptional and translational levels. The highly heterogeneous patterns indicate extensive coordination between distinct cell types and regions, suggesting the brain functions as a coherent unit to execute specific steps of sphingolipid metabolism. As a proof-of-concept application, we showed cell type-specific requirements of sphingomyelinases, including CG6962/dSMPD4 and CG3376/aSMase, degrading distinct subcellular pools of ceramide phosphoethanolamine to maintain brain function. These findings establish a foundation for future studies on brain sphingolipid metabolism and showcase the utilization of this genetic platform in elucidating in vivo mechanisms of sphingolipid metabolism.

MotivationVarious methods have been proposed to construct metabolic networks from metabolomic data; however, small sample sizes, multiple confounding factors, the presence of indirect interactions as well as randomness in metabolic processes are of major concern.ResultsIn this study, we benchmark existing algorithms for creating correlation- and regression-based networks of changes in metabolite abundance and evaluate their performance across different sample sizes of a generative model. Using standard interaction-level tests and network-scale analyses based on centrality scores, we assess how well these methods recover represented metabolomic networks. Our findings reveal significant challenges in network inference and result interpretation, even when sample sizes are significant and data are the result of computer modeling of metabolic pathways. Despite these limitations, we demonstrate that correlation-based network inference can, to some extent, discriminate between two different metabolic states in the computational model. This suggests potential utility in distinguishing overarching changes in metabolic processes but not direct pathways in different conditions.Availability and implementationAll relevant data is provided at https://github.com/TheCOBRALab/metabolicRelationships

Auxenochlorella pyrenoidosa, a promising edible bioresource, can be efficiently and safely cultivated using exogenous phytohormones to enhance its productivity. This study employed multi-omics analysis to systematically investigate the effects and mechanisms of exogenous trans-Zeatin (tZ) on the growth and metabolism of A. pyrenoidosa. Results demonstrated that 10 mg/L tZ significantly promoted algal growth, increasing biomass by 166 ± 3.35% at 72 hours (h), while concurrently elevating cellular soluble protein (SP), carbohydrate (CHO), and chlorophyll a (Chla) content. tZ also strengthened the antioxidant defense system, evidenced by reduced reactive oxygen species (ROS) levels, enhanced activities of antioxidant enzymes (superoxide dismutase (SOD) and catalase (CAT)), upregulation of glutathione metabolism, and decreased lipid peroxidation product (malondialdehyde (MDA)). Furthermore, tZ activated key metabolic pathways, including nitrogen metabolism, photosynthetic carbon fixation, and porphyrin biosynthesis, leading to the accumulation of arginine and polyamines, etc. This study reveals that tZ promotes microalgal growth by coordinately regulating carbon–nitrogen metabolic networks and antioxidant systems, providing a theoretical foundation for phytohormone-augmented microalgae cultivation technologies.

Glucose homeostasis relies on coordinated interactions among multiple organs, and its disruption relates to diabetes development. This study investigated how inter-organ metabolic coordination, assessed by whole-body [¹⁸F]FDG PET/CT, is altered across the glycemic continuum at both the population and individual levels, and whether individualized dysfunction features can improve early diabetes risk stratification. We analyzed whole-body [¹⁸F]FDG PET/CT scans from 1,149 adults across two independent centers, classified into normoglycemic, pre-diabetic, and diabetic groups based on fasting glucose. Standardized uptake values normalized by lean body mass were extracted from 20 major organs. These values were further adjusted for age, sex, and BMI using general linear models to reduce demographic confounding. Group-level metabolic connectivity networks were constructed using bootstrapped correlation matrices with False Discovery Rate correction. To capture individual-level dysregulation, we generated deviation networks by quantifying how each subject's inter-organ coordination diverged from demographically matched normative reference patterns. These personalized network features were used to train and validate a machine learning model for classifying pre-diabetes versus diabetes. At the population-level, network topological analysis revealed a decline in inter-organ metabolic connectivity from normoglycemia to pre-diabetes to diabetes. Pre-diabetes was marked by widespread but modest weakening of connections across multiple organs, while diabetes showed fewer but more concentrated disruptions, indicating a shift toward localized network breakdown. Specific alterations included reduced coordination between the kidneys in pre-diabetes, and disrupted connectivity between the brain and liver in diabetes. Individualized deviation networks captured subject-level differences in metabolic connectivity, with greater heterogenity observed in pre-diabetes. A machine learning model trained on these personalized features successfully distinguished diabetes from pre-diabetes (AUC = 0.75, external validation), with brain-peripheral connections emerging as the most informative predictors. This study reveals distinct patterns of inter-organ matbolic connectivity breakdowns across glycemic states and demonstrates that individualized network features can effectively capture subject-specific dysregulation.

Background/Objectives: Semen Ziziphi Spinosae (SZS), a medicinal and edible traditional Chinese herb, has been widely used to treat insomnia. As critical regulators of the central nervous system, astrocytes play a pivotal role in maintaining sleep homeostasis. However, the mechanisms by which SZS modulates astrocytic function to improve sleep remain unclear. Methods: In this study, we employed an integrated transcriptomics and metabolomics approach to investigate the protective effects of SZS extract against lipopolysaccharide (LPS)-induced inflammatory injury and metabolic dysfunction in astrocytes. Results: Transcriptomic analysis revealed that SZS ameliorates cellular damage (including apoptosis, autophagy, and cell cycle dysregulation) through a FOXO3-centric signaling network. Concurrently, SZS restored cellular energy metabolism by increasing ATP production and reducing Ca2+ overload, thereby activating the AMPK signaling pathway to support normal astrocytic function. Metabolomic profiling further demonstrated that SZS-mediated restoration of energy homeostasis sustains ABC transporter activity, which in turn modulates neurotransmitter (serotonin, L-glutamic acid, adenosine), metabolic mediators (leukotrienes, palmitoylethanolamide, succinic acid), and nucleotide (uridine 5′-diphosphate). These coordinated changes normalized GABAergic synapse activity and neuroactive ligand receptor interactions, ultimately resolving neural metabolic network disturbances. Conclusions: Our findings elucidate a novel FOXO3-energy metabolism-ABC transporter axis through which SZS extract attenuates neuroinflammation and metabolic dysfunction in astrocytes and exerts sleep-promoting and neuroprotective effects. This study provides a scientific foundation for understanding the modern pharmacological mechanisms of traditional Chinese medicine in insomnia treatment, highlighting astrocytic regulation as a potential therapeutic target.

Introduction Phytohormone abscisic acid (ABA) plays a pivotal regulatory role in crop responses to abiotic stress. However, the specificities of the coordinated transcriptional and metabolic regulatory network in wheat under ABA signaling remain to be fully elucidated. Methods This study systematically investigated the regulatory effects of exogenous ABA on wheat germinating seeds through integrated physiological, transcriptomic, and metabolomic analyses. Results Physiological results demonstrated that low-concentration ABA (2 mg·L -1 ) promoted primary root elongation (12% increase vs. 0 mg·L -1 (CK)), whereas high concentrations (≥4 mg·L -1 ) significantly inhibited growth (40% root length reduction under 6 mg·L -1 ABA). Concurrently, electrolyte leakage, malondialdehyde (MDA) content, and catalase (CAT) activity markedly increased with ABA concentration ( P < 0.05), indicating aggravated oxidative stress. Transcriptomic profiling (CK vs. 6 mg·L -1 ABA) identified 854 differentially expressed genes (DEGs; 470 up-regulated/384 down-regulated). Gene Ontology (GO) enrichment revealed DEGs predominantly involved in “Cellular process”, “Metabolic process”, “Catalytic activity”, and “Transporter activity”. KEGG analysis highlighted activation of “Linoleic acid metabolism”, “Alpha-Linolenic acid metabolism”, “Glycolysis/Gluconeogenesis”, and “Biosynthesis of amino acids” pathways. Metabolomics detected 665 differentially accumulated metabolites (DAMs), with “Lipids”, “Organic acids”, and “Amino acids” exhibiting significant alterations. KEGG enrichment emphasized “benzoxazinoid biosynthesis” and “Nicotinate/nicotinamide metabolism”. Integrative multi-omics analysis uncovered 10 core pathways, such as “Glycolysis/Gluconeogenesis”, “Biosynthesis of amino acids”, and “Cysteine and methionine metabolism”, that orchestrating ABA stress responses. Notably, L-serine and the genes TraesCS3A02G276100 and TraesCS5A02G398300 were recurrently implicated in multiple pathways, indicating their function as key network nodes. Discussion This study elucidates the molecular mechanisms by which wheat adapts to ABA stress through dynamic reprogramming of its metabolic and gene expression networks, thereby laying a theoretical foundation for developing future ABA-based seed treatment technologies or stress-resistant breeding strategies.

BackgroundDiffuse axonal injury (DAI), a severe subtype of traumatic brain injury (TBI), lacks reliable early diagnostic biomarkers, contributing to poor clinical outcomes. Systemic metabolic pathway dysregulation in DAI remains poorly characterized, limiting targeted therapeutic strategies.ObjectivesIdentify DAI-specific metabolic network disruptions and evaluate their diagnostic and prognostic utility.MethodsIn this prospective cohort study, serum metabolomics profiling, pathway enrichment analysis, and machine learning were integrated with clinical assessments in 64 adults with acute TBI (30 DAI, 34 non-DAI). Untargeted metabolomics via UPLC-LTQ-Orbitrap MS identified differential metabolites, which were mapped to biological pathways using MetaboAnalyst 5.0. Diagnostic and prognostic performance of pathway-based models was assessed using ROC analysis.ResultsDAI patients exhibited distinct metabolic perturbations, with significant dysregulation in mitochondrial fatty acid oxidation (FAO) and phospholipid metabolism. Key discriminative metabolites included carnitine C8:1 (VIP = 3.26) and lysophosphatidylcholine 22:3 sn-2, which correlated with Marshall CT scores (ρ = 0.62, p < 0.001) and pupillary reflex loss. A multi-parameter model integrating FAO and phospholipid degradation markers achieved superior diagnostic accuracy (AUC = 0.927, 95% CI: 0.86–0.98) compared to clinical models (AUC = 0.744). Pathway disruptions further predicted 3-month functional outcomes (GOSE AUC = 0.912).ConclusionDAI involves systemic metabolic network dysfunction centered on mitochondrial energetics and lipid metabolism. Pathway-centric biomarkers enhance diagnostic precision and prognostication, offering a novel framework for biomarker-driven management of TBI. These findings highlight mitochondrial FAO and phospholipid homeostasis as potential therapeutic targets, addressing a critical gap in DAI care.

Polyketides represent a structurally diverse class of natural products with a wide range of biological functions, including antimicrobial activity, defense responses, developmental regulation, pigmentation, and intercellular and intracellular communication signals. The social amoeba Dictyostelium discoideum harbors 40 polyketide synthase (PKS) genes, yet the specific and collective functions remain poorly understood. PKSs require activation by the phosphopantetheinyl transferase DiSfp, which converts inactive apoenzymes into functional holo forms. Disruption of the DiSfp gene abolished the production of PKS-derived metabolites across all developmental stages. Integrated phenotypic, transcriptomic, and metabolomic analyses revealed impaired growth in liquid culture, defects in macropinocytosis, aberrant chemotaxis, and diminished spore formation, associated with altered expression of genes regulating these processes. Comparative metabolomic profiling of the mutant identified candidate polyketide metabolites across different developmental stages, providing a valuable resource for targeted identification and isolation of previously undescribed compounds. This study establishes a functional link between the PKS machinery and the metabolic and developmental networks of D. discoideum, highlighting the essential roles of polyketides in cellular physiology and offering a framework for future polyketide discovery.

Cardiovascular disease (CVD) and cancer are the leading causes of morbidity and mortality in the U.S. Diet is a significant risk factor for both CVD and cancer and has been shown to influence survival and treatment response. Importantly, dietary interventions exacerbate chemotherapy-related cardiotoxicities. To effectively integrate dietary interventions into treatment recommendations, it is critical to understand the complex interactions between nutrients and metabolic changes in the heart. We used the Periodic Table of Food Initiative (PTFI) dataset within the American Heart Association Precision Medicine 2024 Data Challenge. Mass spectrometry analysis of molecular compositions from 500 food products was integrated into flux balance analysis using the mammalian network of cardiac metabolism, CardioNet. We developed an algorithm to compare the metabolic efficacy of PTFI food products and their combinations as diets in computational simulations. We simulated physiological health conditions, oncometabolic stress, and anthracycline chemotherapy treatment by integrating proteomics datasets. In total, over 600,000 simulations mimicking diets were conducted. Our analysis revealed that the availability and composition of food sources directly impact cardiac metabolism, depending on a patient’s health status. Simulations of cancer patients showed an overall reduction in metabolic efficacy for most food products and their combinations. Our analysis revealed that cancer patients require distinct food compositions to ensure cardio-metabolic health. In anthracycline treatment simulations, we identified food combinations that impaired cardiac metabolism by (1) decreased energy provision, (2) significant increases in oxidative stress, reflecting increased beta-oxidation of saturated long-chain fatty acids, and (3) rapid reduction in biomass provision. Using network analysis, we identified food pairings that enable the optimization of cardiac metabolic efficacy. These networks are a starting point for further mechanistic studies and clinical validation. Our findings directly impact cancer patients by developing data-driven recommendations for improved food and meal plans during the different disease stages and treatment. Our results highlight the potential of exploring food interactions for improving cardiometabolic health. Computational workflow simulating food and diets to evaluate metabolic efficacy in the heart during cancer and cancer-related treatment.

Revisiting the Warburg Effect: Linking Hydrogen Sulfide to the Central Metabolic Network in Cancer

Understanding how distinct neuronal subtypes contribute to Alzheimer’s disease (AD) pathology remains a major challenge. Patient-derived induced pluripotent stem cell (iPSC) studies have shown neuronal subtype-specific molecular and pathological signatures, yet the underlying metabolic shifts driving this selective vulnerability are not completely understood. Here we present iNeuron-GEM, the first manually curated, genome-scale metabolic network of human neurons that integrates transcriptomic and metabolic knowledge to resolve subtype-specific metabolic states. By coupling iNeuron-GEM with single nucleus RNA sequencing data from post-mortem human cohort studies, ROSMAP and SEA-AD, we capture neuronal subtype-specific metabolic features and fluxes and identify perturbations in lipid and energy metabolism across excitatory and inhibitory neurons. Integrative analysis with NPS-AD data shows overlapping metabolic disruptions in AD and schizophrenia (SCZ), suggesting shared molecular vulnerabilities between neurodegenerative and neuropsychiatric disorders. We also developed a computational pipeline to infer transcriptional regulation of metabolic pathways and identify NR6A1 and NR3C1 as important regulators of lipid dysregulation in AD neurons. Our study establishes iNeuron-GEM as a framework to identify neuronal subtype-specific metabolic vulnerabilities in complex brain disorders.

Alcoholic liver disease (ALD) remains a major global health burden, with over 283million individuals affected by alcohol use disorder (AUD). Early-stage ALD is characterized by gut microbiota dysbiosis, intestinal barrier dysfunction, and endotoxemia. Traditional therapies focusing solely hepatoprotection or lipid reduction often show limited efficacy due to their inability to restore the gut-liver axis balance. This study aimed to evaluate the adjuvant efficacy of a multi-strain probiotic formulation (including Weizmannia coagulans BC99, Lacticaseibacillus rhamnosus LRa05, Bifidobacterium animalis subsp. lactis BLa80, and Weizmannia coagulans BC179) combined with polyene phosphatidylcholine (Essentale®) in improving liver function and metabolic profiles in ALD patients.A randomized, single-blind, placebo-controlled clinical trial was conducted in 42 ALD patients. Participants received either Essentale® plus probiotics or Essentale® plus placebo for 30 days. Liver function tests, serum lipids, fecal microbiota (16S rRNA sequencing), and fecal metabolites (GC-MS) were assessed at baseline, day 15, and day 30.Compared to placebo, the probiotic group showed significant reductions in ALT, AST, GGT, and TG, along with increased HDL-C levels. Probiotics promoted the enrichment of Bifidobacterium, Faecalibacterium, and Akkermansia, and improved microbial diversity. Metabolomic profiling revealed upregulation of anti-inflammatory and antioxidant metabolites (e.g., EGCG, S-methylglutathione) and downregulation of pro-inflammatory lipotoxic intermediates. Spearman analysis confirmed correlations between key bacterial genera and liver/metabolic biomarkers.Multi-strain probiotics effectively modulate the gut-liver axis by reshaping gut microbiota and metabolic networks, thereby enhancing the therapeutic efficacy of conventional hepatoprotective drugs in ALD. These findings support their clinical potential as a safe and complementary strategy for managing ALD.

Exploring the metabolic characteristics of different plant organs and tissues at a spatial level can help us to better understand the functional mechanisms of biological tissues and cells. Mass spectrometry imaging (MSI) provides a reliable tool for this purpose. However, its application for high-resolution metabolic mapping across various plant organs remains a significant challenge due to the intrinsic biological properties of plant samples and unfavorable analysis conditions. This study aimed to develop a novel MSI platform that can expand more diverse plant samples in spatial metabolomics research and enhance the detection efficiency of plant metabolites. The platform (AMG-LDI-MSI) based on an Au nanoparticles-loaded MoS2 and doped graphene oxide (Au@MoS2/GO) flexible film substrate combined with laser desorption/ionization (LDI)-MSI was established to enhance the detection and visualization of metabolites in various plant tissues. It has a non-sectioning, matrix-free, dual-ion mode imaging strategy, enabling high-throughput detection of metabolites and high-resolution molecular imaging within a micrometer scale. The Au@MoS2/GO as a new substrate can offer high sensitivity and molecular coverage for diverse plant metabolites (10 classes) under the positive and negative ion modes. Moreover, the AMG-LDI-MSI platform overcomes the limitations of plant tissues (e.g., fragile leaf, water-rich fruit, or lignified roots) for in situ imaging. We successfully applied the platform to map the metabolite spatial dynamics in different types of fresh tissues (rhizome, main root, branch root, fruit, leaf, and root nodule) from medicinal plants, obtained the high-quality mass spectral imaging data, and demonstrated the universality and applicability of the platform to multiple plant tissues. These results demonstrate the significant advantages of enhancing the detection of multiple tissue metabolites in plants and their high-resolution imaging. It has overcome the limitations of previously reported MSI methods, suggesting that it could become a widely used tool for deciphering metabolic networks in plant biology.

Unraveling Qu-aroma variation between inner and outer layers of medium-temperature Daqu: A multi-omics and sensory approach.

Unlocking Kluyveromyces marxianus for efficient protein expression and single-cell protein production.

Genome-wide identification of cytochrome c oxidase genes in cotton and functional characterization of GhCOX11 in drought and cold stress.