Spatial Resolved Metabolomics via AFADESI-MSI Reveals Lipid Metabolic Alterations in Aging Lungs

Spatial Resolved Metabolomics via AFADESI-MSI Reveals Lipid Metabolic Alterations in Aging Lungs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatial Resolved Metabolomics via AFADESI-MSI Reveals Lipid Metabolic Alterations in Aging Lungs Lifeng Yan, Xiahui Ge, Huaqi Guo, Yu Xie, Weining Xiong, Lijun Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7113832/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The global burden of age-related respiratory diseases—most notably COPD and IPF—has reached unprecedented levels. Understanding the unique metabolic profiles of lung tissue is essential for elucidating the molecular mechanisms of natural lung aging. However, the spatially resolved metabolic drivers of pulmonary aging remain uncharacterized. We employed airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) to map lipid dysregulation in aging lungs. Methods : Lung tissues from young (3-month) and aged (24-month) C57BL/6J mice were analyzed using AFADESI-MSI for spatially resolved metabolomics. Key findings were validated by LC-MS/MS and immunohistochemical analysis of metabolic enzymes. Results : AFADESI-MSI revealed compartmentalized lipid remodeling in aged lungs, particularly in unsaturated fatty acid and arachidonic acid metabolism, showing significant perturbations. High-resolution spatial mapping demonstrated distinct distribution patterns of these metabolites. LC-MS/MS validation confirmed reduced levels of adrenic acid and palmitic acid, alongside elevated levels of 12-ketotetrahydroleukotriene B4, indicating pro-inflammatory and oxidative stress progression in lung aging. Additionally, five key metabolic enzymes (ELOVL, FABP6, LTA4H, PLA2, and PTGR1) associated with these metabolites exhibited altered expression patterns in aged mouse lungs. Conclusion : Our study highlights the potential of AFADESI-MSI as an innovative tool for metabolic biomarker research and suggests directions for future multi-omics and functional validation studies. Spatially resolved metabolomics approach uncovered multilevel molecular alterations during lung aging, offering insights into the metabolic reprogramming of unsaturated fatty acid and arachidonic acid pathways. These findings demonstrate that adrenic acid-LTB4 imbalance drives pro-inflammatory microenvironments in lung aging. Biological sciences/Biochemistry Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Aging aging lung mass spectrometry imaging lipid metabolic dysfunction unsaturated fatty acid arachidonic acid Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Global aging populations are projected to reach 2 billion (≥65 years) by 2050 1 . This demographic shift amplifies age-related disease burdens, particularly in the respiratory system. Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) incidence rises sharply with age 2-4 , disproportionately affecting Asia-Pacific regions where COPD prevalence is the world's highest 5 . Physiological lung aging manifests as emphysema-like alterations driving pulmonary functional decline including enlarged alveolar airspaces, reduced surface area, thickening of the bronchial wall, diminished elastic lung recoil, and small airway loss 6 , 7 . Molecular hallmarks of aging (e.g., cellular senescence, mitochondrial dysfunction) underlie pulmonary structural changes 8 . Despite the universality of aging, the underlying mechanisms associated with physiological, structural, and cellular changes in the aging lung have yet to be fully elucidated 7 , 9 . Metabolomics, which profiles metabolites in cells, tissues, and biofluids, holds promise for discovering biomarkers of lung aging. However, metabolome-wide analyses of healthy aging lung tissue remain scarce. A recent NMR-based study identified 13 altered metabolites in aged mouse lungs, though lung lysate profiles showed less age-related variation than other organs 10 . Conventional metabolomics (e.g., NMR) fails to capture spatial heterogeneity, as evidenced by homogenate-based studies reporting minimal lung metabolic changes during aging. Mass spectrometry imaging (MSI) is a novel, label-free bioanalytical technique that provides simultaneous molecular and spatial information, making it highly suitable for in situ metabolomics. Airflow-assisted desorption electrospray ionization (AFADESI)-MSI stands out as a high-coverage ambient MSI technique 11 . It enhances in situ droplet collection and sampling by incorporating a high-rate extracting airflow into a custom-built ion source. This advancement enables the visualization of over 1500 endogenous metabolites at 100 μm resolution in heterogeneous tissues through untargeted analysis, providing robust structural and functional references for metabolite-based molecular histology 11 , 12 . AFADESI-MSI is characterized by wide coverage, wide dynamic range, rapid analysis procedure, high sensitivity and high specificity, making it particularly useful for identifying structure-specific and functionally relevant metabolites 13 , 14 . In this study, we apply AFADESI-MSI to define spatially resolved metabolic dysregulation in aging lungs. This approach enabled visualization of the spatial distribution and changes in aging-associated metabolites, offering metabolism-based insights into the molecular mechanisms of lung aging. Methods 2.1 Animals C57BL/6J male mice (6-week-old, 18-20g/mouse, n=6/group) were purchased from Jiangsu GemPharmatech Co. Ltd. and maintained under specific pathogen-free conditions (22–26°C, 40%–60% humidity, 12 h light/dark cycles) with free access to food and water. At 3 months or 24 months of age, mice were euthanized using an overdose of isoflurane anesthesia followed by cervical dislocation. The superior lobe of the left lung was fixed in 4% paraformaldehyde for histopathological analysis. The superior lobe of the right lung was embedded in optimal cutting temperature compound (OCT) for AFADESI–MSI analysis. The remaining lobes were stored at -80°C for further analysis. All the animal studies were approved by the Institutional Review and Ethics Board of the School of Medicine, Zhejiang University (No. 20221105). 2.2 Histopathological analysis of lung sections Samples embedded in 4% paraformaldehyde were sectioned (5 μm) and H&E-stained for histology. Emphysematous lesions was quantified by mean alveolar area (MAA), diameter (MAD), and linear intercept (MLI) using Image-Pro Plus 6.0, as previously described 13 , with details provided in the Supplementary Materials. 2.3 Immunohistochemistry Dewaxed lung slides were subjected to antigen retrieval in citrate buffer (pH 6.0), blocked with 3% H₂O₂ (25 min) and 1% BSA-PBS (30 min), and incubated overnight at 4°C with primary antibodies against PTGR1 (1:200, CGB114486), LTA4H (1:200, 13662-1-AP), and ELOVL2 (1:200, CGB113896). Sections were then treated with HRP-conjugated goat anti-rabbit secondary antibody (60 min, 37°C), developed with DAB, counterstained with hematoxylin, dehydrated, and mounted. 2.4 Quantitative assessment of mRNA Total RNA was isolated from lung tissues, and the primers for the mRNAs were synthesized by Sangon Biotech (Shanghai) Co., Ltd., with details provided in the Supplementary Materials. 2.5 AFADESI-MSI AFADESI–MSI analysis has been previously described 11-13 . Right lung superior lobes from 3 young/old mice were embedded in OCT, sectioned at 10 μm (-20°C), and dried in a vacuum desiccator for 30 min. Analysis was conducted by Shanghai Lu-Ming Biotech using an AFADESI-MSI platform (Q-Orbitrap mass spectrometer) with parameters: scanning at 200 μm/s (x-direction), 100 μm step (y-direction), mass range 100–1000 Da, resolution 70,000, spray voltage ±7 kV, capillary temperature 350°C, nitrogen spray gas (0.6 MPa), acetonitrile/water (80:20, 5 μL/min) solvent, and extracting gas flow 45 L/min (Xcalibur software 3.0). Metabolites were tentatively identified by matching exact masses (<5 ppm error) to smetDB, pySM, and literature, with tandem MS validation. Ion adducts considered: [M-H]−, [M+H]+, [M+Na]+, etc. Signals with unique annotations in ≥1 database were used. Raw data were converted to .imzML, imported into Cardinal R for ion image reconstruction, background subtraction, and OPLS-DA analysis. Differential metabolites were selected by VIP ≥1.0 and P < 0.05. More information were provided in the Supplementary Materials. 2.6 Untargeted LC-MS/MS metabolomics analysis The sample preparation procedures were described in detail in our previous work 15 . Details are provided in the Supplementary Materials. 2.7 Statistical analysis Statistical analysis was performed via SPSS software (version 18.0) and GraphPad Prism (version 8.0). Unpaired two-sided Student's t tests were used to compare the statistical significance between two groups. The results are presented as the mean ± SEM. The level of statistical significance was set at P < 0.05. Results 3.1 Histopathological analyses To investigate age-related pulmonary structural changes, we performed lung morphometry in mice. Histology showed 24-month-old mice had enlarged alveoli similar to aging human lungs (Fig. 1A), confirmed by increased mean linear intercept (MLI), alveolar diameter (MAD), and area (MAA) (Fig. 1B). Aging markers p21, p19, p16, and Trp53 were upregulated in aged lungs (Fig. 1C), indicating successful induction of lung biological aging. 3.2 AFADESI-MSI analysis of aging lungs A flow chart of this strategy is shown in Fig. 2A. Quality control (QC) was performed to validate AFADESI-MSI reliability, using blank area datasets (without OCT) as QC samples. PCA analysis showed QC dataset deviations < 2 SD (Fig. S1A), confirming system stability for further analysis. PCA results of QC samples are shown in Fig. S1 B. In situ metabolomic profiling of frozen lung sections from 3 young (3-month-old) and 3 old (24-month-old) mice was performed via AFADESI-MSI (Fig. 2A). Mass spectra were precisely extracted, and Fig. 2B shows a representative mass spectrometry image in negative ion mode with the corresponding HE-stained section. According to image reconstruction using all ions, AFADESI-MSI detected 1032 (pos) and 971 (neg) metabolites, with UMAP segmentation resolving spatial clusters (Table S1). In the average mass spectrum, the metabolites below m/z 500 included organoheterocyclic compounds ( e.g., Temozolomide, m/z 215.029936344), organic acids ( e.g., Taurine, m/z 124.007395563), fatty acids ( e.g., Linoleic acid, m/z 279.23299993), and nucleotides ( e.g., Uridine, m/z 265.044243162), while metabolites above m/z 500 were mostly lipids and lipid-like molecules, such as diradylglycerols ( e.g., DG(36:1), m/z 657.522030983), phosphosphingolipids ( e.g., SM(d30:1), m/z 627.487165636), glycerophosphocholines ( e.g., LysoPC(18:3), m/z 552.285201244), glycerophosphates ( e.g., PA(38:6), m/z 719.465781352), and so on. In addition to the wide range of metabolite information, the ion intensities of the lung metabolites varied over a broad range from 10 3 to 10 6 . The ion intensities are shown in Fig. 2C. In addition to pathological division (HE-stained images), molecular segmentation via uniform manifold approximation and projection (UMAP) visualization was also utilized for ROI data extraction. In this study, UMAP spatial segmentation exhibited good clustering or grouping of different pixels based on metabolite profiling, which enabled the automatic discrimination of different physiological subregions (Fig. 2 D). These results indicate that the established AFADESI-MSI method features high sensitivity and wide coverage, allowing high-specificity detection of various types of endogenous metabolites. To identify global discriminative metabolites between aged and young mice, we performed differential analysis of lung metabolic profiles. Principal component analysis (PCA) of whole-lung metabolomes showed distinct clustering between groups (Fig. 3A), while supervised OPLS-DA further resolved discriminative metabolites, with score plots demonstrating clear separation in both positive and negative ion modes (Fig. 3B). The cumulative R2X, R2Y and Q2 values were 0.881, 0.975, and 0.588, respectively, for OPLS-DA in positive ion mode and 0.927, 0.99, and 0.879, respectively. Volcano plots were also drawn to visualize the dysregulated metabolites in the lungs between the two groups (Fig. 3C). Based on VIP ≥1.0 and P < 0.05, 96 (positive ion mode) and 208 (negative ion mode) discriminative metabolites were tentatively identified (Table S2). These altered metabolites are related to the metabolism of lipids, nucleotides and their derivatives, organic oxygen compounds, organoheterocyclic compounds, benzenoids, and organic acids and derivatives. In addition, KEGG pathway enrichment analysis identified a total of 69 pathways (Table S3). KEGG analysis highlighted lipid metabolism dysregulation, particularly in unsaturated fatty acid and arachidonic acid pathways (Fig. 3D). 3.3 Perturbation of the biosynthesis of unsaturated fatty acids and arachidonic acid metabolism A total of 201 significantly altered lipid-related metabolites were identified, 23 of which were associated with unsaturated fatty acid and arachidonic acid metabolism. Among these, 12 key metabolites—adrenic acid (22:4n6), palmitic acid, oleic acid, linoleic acid, gamma-linolenic acid, dihomo-gamma-linolenic acid, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), 11Z-eicosenoic acid, arachidonic acid, leukotriene B4 (LTB4), and 12-keto-tetrahydro-LTB4—along with 11 isomers were annotated (Table S4). The spatial distribution and relative intensities of these 12 key metabolites are depicted in Fig. 4A‒B. Metabolic pathways involving adrenic acid (22:4n6), palmitic acid, oleic acid, linoleic acid, gamma-linolenic acid, dihomo-gamma-linolenic acid, EPA, DHA and 11Z-eicosenoic acid are closely associated with the biosynthesis of unsaturated fatty acids. In the lungs of aged mice, palmitic acid levels were significantly reduced, whereas the other metabolites were significantly increased. Notably, arachidonic acid and adrenic acid metabolism are associated with both the biosynthesis of unsaturated fatty acids and the downstream arachidonic acid metabolic pathways. MS imaging revealed pronounced upregulation of arachidonic acid and concurrent downregulation of adrenic acid in the lungs of 24-month-old mice compared with those of younger controls. Furthermore, downstream arachidonic acid metabolites, including LTB4 and its oxidized derivative 12-keto-tetrahydro-LTB4, accumulated extensively throughout aged lungs. To further elucidate the metabolic perturbations in unsaturated fatty acid and arachidonic acid metabolism in aging lungs, LC-MS/MS analysis was performed on lung tissue samples (Fig. S2 A). The PCA and OPLS-DA score plots derived from the LC-MS/MS data demonstrated distinct metabolic clustering between young and aged mice (Fig. S2 B‒C). Differentially abundant metabolites were identified using a threshold of VIP values > 1.0 and P values < 0.05, revealing 910 dysregulated metabolites in aged lungs, including 404 lipids and lipid-like molecules (Fig. S2 D and Table S5). KEGG pathway analysis revealed that these overlapping metabolites were significantly associated with lipid metabolism pathways, including glycerophospholipid metabolism, biosynthesis of unsaturated fatty acids, steroid hormone biosynthesis, alpha-linolenic acid metabolism, ether lipid metabolism, linoleic acid metabolism, arachidonic acid metabolism, sphingolipid metabolism, fatty acid elongation, fatty acid degradation, and fatty acid biosynthesis (Fig. 4C). LC-MS/MS confirmed three metabolites previously detected via AFADESI-MSI: 12-keto-tetrahydro-LTB4, adrenic acid and palmitic acid. Bar charts depicting the fold changes (Fc) and P values for these metabolites were generated to illustrate their differential regulation (Fig. 4D). Overall, spatial metabolomic profiling revealed profound perturbations in unsaturated fatty acids and arachidonic acid metabolism in aged lungs. Notably, the most prominent metabolic disturbances included elevated levels of LTB4, as well as decreased levels of adrenic acid and palmitic acid, highlighting the adrenic acid-LTB4 imbalance as a key driver of age-related pulmonary pathophysiology. 3.4 Validation of crucial metabolic enzymes Five crucial metabolic enzymes closely associated with the altered metabolites were selected as potential aging-related metabolic enzymes. Table 1 and Fig 5A offer detailed information on these metabolic enzymes and their related metabolites, along with the metabolic processes involved. qRT-PCR and IHC analyses of these metabolic enzymes are shown in Fig. 5B and Fig. 5C, respectively. Table 1 . Screened potential aging-associated metabolic enzymes in lung Full name EC number Related metabolites Function ELOVL elongation of very-long-chain fatty acids-like elongases 2.3.1.199 Arachidonic acid Adrenic acid Gamma-linoleic acid Dihomo-gamma-linoleic acid Eicosapentaenoic acid Docosahexaenoic acid Elongation FABP6 Fatty acid-binding protein subclass 6 Involved in fatty acid uptake, transport, and metabolism LTA4H Leukotriene A 4 hydrolase 3.3.2.6 Leukotriene A4 Leukotriene B4 Catalyze the conversion of leukotriene A4 to leukotriene B4 PLA2 phospholipase A2 3.1.1.4 Arachidonic acid Palmitic acid Oleic acid Docosahexaenoic acid Catabolize phospholipids to polyunsaturated fatty acids PTGR1 Prostaglandin reductase 1 1.3.1.74 Leukotriene B4 12-Keto-leukotriene B4 12-Keto-tetrahydro-leukotriene B4 Catalyzes the conversion of leukotriene B4 to 12-oxo-leukotriene B4 In fatty acid metabolism, elongation is the process of extending fatty acid carbon chains via specific enzymatic reactions, which are primarily catalyzed by the ELOVL family of enzymes 16 . On the basis of the AFADESI-MSI results, ELOVL elongases are involved in elongating gamma-linolenic acid and arachidonic acid to produce dihomo-gamma-linolenic acid and adrenic acid, respectively 17 . Additionally, ELOVL elongases contribute to the synthesis of EPA and DHA 18 . ELOVL2 is crucial for adrenic acid synthesis 19 . IHC staining revealed decreased ELOVL2 expression in the lungs of 24-month-old mice, which was consistent with the distribution of adrenic acid. qRT-PCR confirmed that Elovl2 expression was reduced in the lungs of aged mice. Leukotriene A4 hydrolase (LTA4H), the final and rate-limiting enzyme in the biosynthesis of leukotriene B4, was upregulated in the lungs of old mice compared with those of young mice, as shown by IHC and qPCR, which was consistent with the findings of MS imaging. Prostaglandin reductase 1 (PTGR1) catalyzes the conversion of LTB4 to 12-keto-tetrahydro-LTB4. IHC and qRT-PCR revealed higher PTGR1 expression in 24-month-old mice than in 3-month-old controls, aligning with the 12-keto-tetrahydro-LTB4 levels. The degradation of phospholipids by phospholipase A2 (PLA2) can release polyunsaturated fatty acids (PUFAs), such as arachidonic acid, palmitic acid, oleic acid, and docosahexaenoic acid, which play key roles in modulating cellular functions 20 . IHC revealed lower PLA2 expression in 24-month-old mice than in 3-month-old controls, but the qRT-PCR results were not significant. Fatty acid-binding protein subclass 6 (FABP6) is an enzyme that is involved in fatty acid uptake, transport, and metabolism 21 . IHC staining and qRT-PCR assessment of lung sections revealed a marked decrease in Fabp6 expression in the lungs of old mice compared with young mice. Discussion This study controlled for environmental variables to isolate age-specific lung effects. Natural aging induced pulmonary structural remodeling, shown by alveolar enlargement and upregulated senescence markers (p16, p21 and Trp53), confirming biological lung aging in 24-month-old mice. Using advanced AFADESI-MSI spatial metabolomics, we systematically mapped global metabolic perturbations in lung tissue. A total of 304 differentially abundant metabolites were identified in the aging lungs, revealing a striking age-dependent dysregulation of lipid homeostasis. Lipids are a diverse class of molecules (fatty acids, glycerophospholipids, sphingolipids, triacyl glycerides, and cholesterol esters) important for membrane formation, energy storage, and signaling 22 . Lipid metabolism and signaling have long been recognized to influence aging and longevity 22 , 23 . Importantly, emerging evidence implicates lipid-mediated immunomodulation in respiratory pathophysiology 24 , with documented metabolic alterations in chronic lung diseases including interstitial lung disease 25 , 26 , asthma 27 , COPD 28 , and lung cancer 29 . High-resolution spatial mapping revealed substantial metabolic perturbations in unsaturated fatty acid and arachidonic acid metabolism in aged lungs. LC-MS/MS analysis confirmed marked disruptions in the metabolic pathways of LTB4, adrenic acid, and palmitic acid. Furthermore, five key metabolic enzymes associated with these metabolites presented altered expression levels in the lungs of aged mice, corroborating the AFADESI-MSI findings. As a result, adrenic acid-LTB4 imbalance drives pro-inflammatory microenvironments in lung aging (Fig. 5 D). LTB4, an arachidonic acid-derived eicosanoid mediator, plays a pivotal role in pro-inflammatory responses by binding to its specific receptors on immune cells, e.g. , neutrophils, monocytes/macrophages, T cells, and dendritic cells. This ligand‒receptor interaction triggers cellular functions, including chemotaxis, adhesion to vascular endothelial cells, release of lysosomal enzymes, and generation of reactive oxygen species 30 . Therefore, LTB4 has been implicated in various inflammatory diseases, including asthma, atherosclerosis, glomerulonephritis, lupus nephritis, rheumatoid arthritis, and inflammatory bowel disease 31 . Recent study reported that LTB4 levels correlate with COPD exacerbation frequency. We observed evaluated lung LTB4 levels in naturally aged mice, extending prior reports of age-related LTB4 accumulation in the hippocampus and heart 32 , 33 . LTA4H, the rate-limiting enzyme in LTB4 biosynthesis, was upregulated in the lungs of aged mice. This enzymatic dysregulation aligns with clinical observations of LTA4H-mediated progression in acute lung injury, idiopathic pulmonary fibrosis, and allergic asthma 33 , 34 . Furthermore, 12-keto-tetrahydro-LTB4, a metabolite resulting from the lipid beta-oxidation of LTB4, exhibited significant accumulation in the lungs of aged mice. PTGR1, the enzyme that specifically catalyzes the conversion of LTB4 to 12-keto-tetrahydro-LTB4, showed concordant elevated expression in aged mice. This metabolic shift is correlated with reported PTGR1 overexpression in lung cancer patients with poor prognosis 14 , 35 , suggesting that pathways related to lung aging and carcinogenesis are shared. These findings demonstrate significant upregulation of LTB4 and its metabolites in the lungs of aged mice, indicating that pro-inflammatory mechanisms are closely associated with age-related pulmonary aging. Adrenic acid (22:4n6), an ω-6 polyunsaturated fatty acid (PUFA) downstream of arachidonic acid metabolism, has been shown to decrease in the human brain with age 36 , 37 , a trend that is consistent with our findings in natural aging lungs. This metabolic alteration is correlated with impaired expression of the lipid elongation enzyme ELOVL2, a conserved aging biomarker governing long-chain PUFA biosynthesis, in aging lungs. Decreased ELOVL2 expression is associated with age-related dysfunctions, such as visual impairment 11 . Notably, adrenic acid exhibits potent anti-inflammatory properties through competitive inhibition of LTB4 biosynthesis. In the K/BxN serum–transferred murine arthritis model, adrenic acid effectively blocks the production of leukotriene B4 and alleviates arthritis 38 . Our results also revealed an inverse relationship between adrenal acid and LTB4 levels, further supporting this antagonistic interaction. The accumulation of pro-inflammatory arachidonic acid-derived metabolites, such as LTB4 and 12-keto-tetrahydro-LTB4, alongside the depletion of anti-inflammatory adrenic acid, underscores a metabolic shift toward a pro-inflammatory microenvironment in aging lungs. These findings align with the "inflammaging" theory, where chronic low-grade inflammation drives age-related organ dysfunction 39 . Critically, this metabolic dysregulation directly links to pathogenesis in chronic lung diseases. Palmitic acid (16:0, PA), the most prevalent saturated fatty acid in the human body 40 , serves as a precursor for the synthesis of unsaturated fatty acids. Through the action of desaturase enzymes, which introduce double bonds into saturated fatty acids, palmitic acid can be converted into monounsaturated fatty acids such as oleic acid 41 . This process is crucial for maintaining the balance between saturated and unsaturated fatty acids within cells. Palmitic acid can drive cells into senescence by disrupting reactive oxygen species (ROS) generation 42 , mitochondrial dynamics 43 and endoplasmic reticulum homeostasis 44 . Palmitic acid can be released by PLA2 through the hydrolysis of phospholipids 40 . In our study, PLA2 expression was reduced in aged lungs, and the AFADESI-MSI and LC-MS/MS results indicated a significant decrease in palmitic acid levels. Previous studies have indicated that PLA2 loss contributes to age-related cognitive decline and neuroinflammation 45 . However, our results revealed an inverse trend in palmitic acid levels compared with previous studies 43 , 44 . The decrease in PLA2 expression and subsequent reduction in palmitic acid levels in aged lungs may reflect a complex metabolic adaptation to aging, which could explain the heterogeneity of the results. While palmitic acid can induce senescence through various mechanisms, the overall impact on aged lungs might be influenced by other concurrent changes. The decrease in palmitic acid levels in aging lungs might be a compensatory mechanism to mitigate the detrimental effects of excessive palmitic acid, such as lipotoxicity and cellular damage. Moreover, the disagreement in results could also be attributed to insufficient sample size in some studies. Our findings highlighted the complex palmitic acid biosynthesis disorder that occurs in aged lungs and suggested a shift in fatty acid metabolism from saturated to unsaturated fatty acids in aging-related lung pathologies. AFADESI-MSI demonstrated exceptional utility in mapping spatially resolved metabolic networks, overcoming the homogenization bias of traditional metabolomics. However, challenges persist 46 . Low-abundance metabolites, such as specialized pro-resolving mediators (SPMs), often evade detection. This issue could be mitigated by employing derivatization strategies to increase the sensitivity. A notable complexity in spatial metabolomics is that a single m/z value may correspond to multiple metabolites. This primarily arises from three factors: isobaric interference due to the resolution limitations of the mass spectrometer; structural similarity among different metabolites, such as gamma- or alpha-linolenic acid; and metabolite derivatization during sample preparation or ionization. Furthermore, the high-throughput nature of spatial metabolomics, which aims to detect hundreds to thousands of metabolites in a single experiment, may lead to some resolution sacrifice to improve detection efficiency, which can result in signal overlap for low-abundance metabolites. Despite these challenges, significant improvements in detection specificity can be achieved through ongoing instrument upgrades, rigorous data validation processes, and advanced multidimensional analysis techniques. These advancements are crucial for enhancing the precision and reliability of spatial metabolomics studies. Our study revealed discrepancies between spatial (AFADESI-MSI) and traditional (LC-MS/MS) metabolomics: AFADESI-MSI identified 304 metabolites, whereas LC-MS/MS detected 910, with only 3/23 lipid metabolites showing consistent trends. These differences stem from technical biases: MSI excels in non-polar lipid detection (e.g., lipids) but misses polar metabolites, while LC-MS/MS captures hydrophilic metabolites but lacks spatial resolution. Homogenization in LC-MS/MS dilutes locally enriched lipids, explaining reduced lipid signals. Previous research has shown that high spatial expression of tetrahydropalmatine in angiogenic regions may be masked by homogenate untargeted data 47 . Some metabolites have region-specific effects resulting in the failure of untargeted data to capture their dual roles. Spatial metabolomics reveals metabolite co-localization (e.g., glycogen accumulation in pulmonary fibrosis regions 48 ), whereas untargeted metabolomics pathway enrichment analysis may yield opposite conclusions because spatial associations are ignored. Our MS images showed clustered lipid accumulation in aged lung regions, aligning with aging's spatial compartmentalization 49 . Additionally, systematic errors in sample processing and data pretreatment also might influence the results of spatial and traditional metabolomics. Conclusions This study presents a spatially resolved metabolic map of aging lungs, highlighting the pivotal role of unsaturated fatty acid and arachidonic acid pathway dysregulation in age-related pulmonary pathologies. Collectively, these results elucidate lipid metabolic reprogramming in aging lungs, where adrenic acid-LTB4 imbalance drives alveolar inflammaging. Future research should integrate multi-omics approaches with functional validation to unravel the mechanistic networks underlying inflammation-driven age-related lung diseases. Future pharmacological restoration of adrenic acid, e.g., via ELOVL2 activators or LTA4H inhibitors, could rebalance the pro-inflammatory axis. Abbreviations AFADESI-MSI airflow-assisted desorption electrospray ionization mass spectrometry imaging ARDS acute respiratory distress syndrome COPD chronic lung diseases, including chronic obstructive pulmonary disease DHA docosahexaenoic acid DNA deoxyribonucleic acid EPA eicosapentaenoic acid ELOVL elongation of very-long-chain fatty acids-like elongases FABP6 fatty acid-binding protein subclass 6 HE stain hematoxylin-eosin stain IPF idiopathic pulmonary fibrosis KEGG kyoto encyclopedia of genes and genomes LTA4H leukotriene A 4 hydrolase LC-MS/MS liquid chromatography-tandem mass spectrometry MAA mean alveolar area MAD mean alveolar diameter MALDI-MSI matrix-assisted laser desorption ionization mass spectrometry imaging MLI mean linear intercepts MSI mass spectrometry imaging NMR nuclear magnetic resonance OCT optimal cutting temperature compound OPLS-DA orthogonal partial least squares-discriminant analysis PCA principal component analysis PCR polymerase chain reaction PLA phospholipase A2 PLS-DA partial least squares discriminant analysis PTGR1 Prostaglandin reductase 1 QC quality control qRT‒PCR quantitative real-time polymerase chain reaction RNA ribonucleic acid ROIs regions of interest TIC total ion current UMAP uniform manifold approximation and projection VIP variable importance of projection Declarations Data availability The raw reads of all the samples were deposited in METASPACE as .imzML and .ibd files with the reference link: https://metaspace2020.eu/project/AFADESI_MSI_aging_lung. Each sample had a positive ion mode and a negative ion mode analytical dataset. There are a total of 12 datasets in this METASPACE project, and each dataset has corresponding .imzML and .ibd files. The file name contains ‘D-3 m’ for the young group, while the file name contains ‘A-24 m’ for the aging group. Fundings This work was funded by the National Natural Science Foundation of China (grant numbers 82090015, 82330001, 82470031, 82400080, 82400035, and 82271587), the Shanghai Municipal Health Commission Health Industry Research Special Young Project (grant number 20234Y0091). Acknowledgements We are grateful to Junyi Lin from Department of Forensic Medicine, Shanghai Medical College of Fudan University for his invaluable assistance with pathologic evaluation. We extend our thanks to Shanghai Lu Ming Biotech Co., Ltd. (Shanghai, China) for conducting the AFADESI spatially resolved metabolomics in this study. We also appreciate the essential suggestions and technical support provided by Zhenyu Xu and Chuanyu Liu. Author contributions TY. Z and LF. Y conceived the research. TY. Z also performed sample collection and data management. LF. Y, XH. G and HQ. G performed the data analysis and interpretation. Y.X contributed to the data analysis. WN.X supervised the study. LJ. Z established the animal model. The manuscript was written and revised by TY. Z, XH. G and LF. Y. All the authors reviewed and approved the submitted manuscript. Ethics declarations Ethics approval and consent to participante All the animal studies were approved by the Institutional Review and Ethics Board of the School of Medicine, Zhejiang University (No. 20221105). This study followed the ARRIVE guidelines and complied with the Guidelines for the Ethical Review of Laboratory Animal Welfare, People's Republic of China National Standard (GB/T 35892-2018). Competing interests All the authors declare no competing interests in this research. References Dzau VJ, Inouye SK, Rowe JW, Finkelman E, Yamada T. Enabling Healthful Aging for All - The National Academy of Medicine Grand Challenge in Healthy Longevity. N Engl J Med 2019;381:1699-701. Wang C, Hao X, Chen S. Calling for improved pulmonary and critical care medicine in China and beyond. Chinese Medical Journal Pulmonary and Critical Care Medicine 2023;1:1-2. Raghu G, Chen SY, Hou Q, Yeh WS, Collard HR. Incidence and prevalence of idiopathic pulmonary fibrosis in US adults 18-64 years old. Eur Respir J 2016;48:179-86. van Durme Y, Verhamme KMC, Stijnen T, et al. Prevalence, incidence, and lifetime risk for the development of COPD in the elderly: the Rotterdam study. Chest 2009;135:368-77. Adeloye D, Song P, Zhu Y, Campbell H, Sheikh A, Rudan I. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. Lancet Respir Med 2022;10:447-58. Verleden SE, Kirby M, Everaerts S, et al. Small airway loss in the physiologically ageing lung: a cross-sectional study in unused donor lungs. Lancet Respir Med 2021;9:167-74. Schneider JL, Rowe JH, Garcia-de-Alba C, Kim CF, Sharpe AH, Haigis MC. The aging lung: Physiology, disease, and immunity. Cell 2021;184:1990-2019. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell 2013;153:1194-217. Partridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature 2018;561:45-56. Wang X, Yin G, Zhang W, Song K, Zhang L, Guo Z. Prostaglandin Reductase 1 as a Potential Therapeutic Target for Cancer Therapy. Front Pharmacol 2021;12:717730. Chen D, Chao DL, Rocha L, et al. The lipid elongation enzyme ELOVL2 is a molecular regulator of aging in the retina. Aging Cell 2020;19:e13100. He J, Sun C, Li T, et al. A Sensitive and Wide Coverage Ambient Mass Spectrometry Imaging Method for Functional Metabolites Based Molecular Histology. Adv Sci (Weinh) 2018;5:1800250. Sun C, Li T, Song X, et al. Spatially resolved metabolomics to discover tumor-associated metabolic alterations. Proc Natl Acad Sci U S A 2019;116:52-7. Huo M, Wang Z, Fu W, et al. Spatially Resolved Metabolomics Based on Air-Flow-Assisted Desorption Electrospray Ionization-Mass Spectrometry Imaging Reveals Region-Specific Metabolic Alterations in Diabetic Encephalopathy. J Proteome Res 2021;20:3567-79. Want EJ, Masson P, Michopoulos F, et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc 2013;8:17-32. Nie L, Pascoa TC, Pike ACW, et al. The structural basis of fatty acid elongation by the ELOVL elongases. Nature structural & molecular biology 2021;28:512-20. Baker EJ, Valenzuela CA, van Dooremalen WTM, et al. Gamma-Linolenic and Pinolenic Acids Exert Anti-Inflammatory Effects in Cultured Human Endothelial Cells Through Their Elongation Products. Molecular nutrition & food research 2020;64:e2000382. Takić M, Ranković S, Girek Z, et al. Current Insights into the Effects of Dietary α-Linolenic Acid Focusing on Alterations of Polyunsaturated Fatty Acid Profiles in Metabolic Syndrome. Int J Mol Sci 2024;25. Shrestha N, Holland OJ, Kent NL, et al. Maternal High Linoleic Acid Alters Placental Fatty Acid Composition. Nutrients 2020;12. Frisardi V, Panza F, Seripa D, Farooqui T, Farooqui AA. Glycerophospholipids and glycerophospholipid-derived lipid mediators: a complex meshwork in Alzheimer's disease pathology. Prog Lipid Res 2011;50:313-30. Ding L, Yang L, Wang Z, Huang W. Bile acid nuclear receptor FXR and digestive system diseases. Acta Pharm Sin B 2015;5:135-44. Mutlu AS, Duffy J, Wang MC. Lipid metabolism and lipid signals in aging and longevity. Dev Cell 2021;56:1394-407. Parkhitko AA, Filine E, Mohr SE, Moskalev A, Perrimon N. Targeting metabolic pathways for extension of lifespan and healthspan across multiple species. Ageing Res Rev 2020;64:101188. Berthon BS, Wood LG. Nutrition and respiratory health--feature review. Nutrients 2015;7:1618-43. Zhao T, Y. S. Mechanisms and Therapeutic Potential of Myofibroblast Transformation in Pulmonary Fibrosis. . Journal of Respiratory Biology and Translational Medicine 2025;2. Kim JS, Steffen BT, Podolanczuk AJ, et al. Associations of omega-3 Fatty Acids With Interstitial Lung Disease and Lung Imaging Abnormalities Among Adults. Am J Epidemiol 2021;190:95-108. Lee-Sarwar K, Kelly RS, Lasky-Su J, et al. Dietary and Plasma Polyunsaturated Fatty Acids Are Inversely Associated with Asthma and Atopy in Early Childhood. J Allergy Clin Immunol Pract 2019;7:529-38 e8. Kotlyarov S, Kotlyarova A. Anti-Inflammatory Function of Fatty Acids and Involvement of Their Metabolites in the Resolution of Inflammation in Chronic Obstructive Pulmonary Disease. Int J Mol Sci 2021;22. Luu HN, Cai H, Murff HJ, et al. A prospective study of dietary polyunsaturated fatty acids intake and lung cancer risk. Int J Cancer 2018;143:2225-37. Samuelsson B, Dahlen SE, Lindgren JA, Rouzer CA, Serhan CN. Leukotrienes and lipoxins: structures, biosynthesis, and biological effects. Science 1987;237:1171-6. Nakamura M, Shimizu T. Leukotriene receptors. Chem Rev 2011;111:6231-98. Chinnici CM, Yao Y, Pratico D. The 5-lipoxygenase enzymatic pathway in the mouse brain: young versus old. Neurobiol Aging 2007;28:1457-62. Kain V, Ingle KA, Kachman M, et al. Excess omega-6 fatty acids influx in aging drives metabolic dysregulation, electrocardiographic alterations, and low-grade chronic inflammation. Am J Physiol Heart Circ Physiol 2018;314:H160-H9. Rao NL, Riley JP, Banie H, et al. Leukotriene A(4) hydrolase inhibition attenuates allergic airway inflammation and hyperresponsiveness. Am J Respir Crit Care Med 2010;181:899-907. Zhao Y, Weng CC, Tong M, Wei J, Tai HH. Restoration of leukotriene B(4)-12-hydroxydehydrogenase/15- oxo-prostaglandin 13-reductase (LTBDH/PGR) expression inhibits lung cancer growth in vitro and in vivo. Lung Cancer 2010;68:161-9. Hancock SE, Friedrich MG, Mitchell TW, Truscott RJW, Else PL. Changes in Phospholipid Composition of the Human Cerebellum and Motor Cortex during Normal Ageing. Nutrients 2022;14. Norris SE, Friedrich MG, Mitchell TW, Truscott RJW, Else PL. Human prefrontal cortex phospholipids containing docosahexaenoic acid increase during normal adult aging, whereas those containing arachidonic acid decrease. Neurobiol Aging 2015;36:1659-69. Brouwers H, Jonasdottir HS, Kuipers ME, et al. Anti-Inflammatory and Proresolving Effects of the Omega-6 Polyunsaturated Fatty Acid Adrenic Acid. J Immunol 2020;205:2840-9. Capri M, Conte M, Ciurca E, et al. Long-term human spaceflight and inflammaging: Does it promote aging? Ageing Res Rev 2023;87:101909. Carta G, Murru E, Banni S, Manca C. Palmitic Acid: Physiological Role, Metabolism and Nutritional Implications. Frontiers in physiology 2017;8:902. Palomer X, Pizarro-Delgado J, Barroso E, Vázquez-Carrera M. Palmitic and Oleic Acid: The Yin and Yang of Fatty Acids in Type 2 Diabetes Mellitus. Trends Endocrinol Metab 2018;29:178-90. Wan F, He X, Xie W. Canagliflozin Inhibits Palmitic Acid-Induced Vascular Cell Aging In Vitro through ROS/ERK and Ferroptosis Pathways. Antioxidants (Basel, Switzerland) 2024;13. Sun Y, Wang J, Guo X, et al. Oleic Acid and Eicosapentaenoic Acid Reverse Palmitic Acid-induced Insulin Resistance in Human HepG2 Cells via the Reactive Oxygen Species/JUN Pathway. Genomics, proteomics & bioinformatics 2021;19:754-71. Chen X, Chen K, Hu J, et al. Palmitic acid induces lipid droplet accumulation and senescence in nucleus pulposus cells via ER-stress pathway. Communications biology 2024;7:539. Jiao L, Shao W, Quan W, et al. iPLA2β loss leads to age-related cognitive decline and neuroinflammation by disrupting neuronal mitophagy. Journal of neuroinflammation 2024;21:228. Wang Z, He B, Liu Y, et al. In situ metabolomics in nephrotoxicity of aristolochic acids based on air flow-assisted desorption electrospray ionization mass spectrometry imaging. Acta Pharm Sin B 2020;10:1083-93. Cui H, Yang X, Wang Z, et al. Tetrahydropalmatine triggers angiogenesis via regulation of arginine biosynthesis. Pharmacological research 2021;163:105242. Conroy LR, Clarke HA, Allison DB, et al. Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis. Nature communications 2023;14:2759. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell 2023;186:243-78. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterials.docx Figure S1. Quality control data. (A) The deviation in the AFADESI-MSI analysis was evaluated by the distribution of the runs. Line plots of the data extracted from a blank area generated via PCA using components 1 and 2 show the stability of the AFAI-MSI system. Line plots depicting the first and second components of positive ion mode MSIs acquired from masses > 500 Da and ≤ 500 Da and negative ion mode MSIs acquired from masses > 500 Da and ≤ 500 Da. X-axis: run order; Y-axis: standard deviation. (B) PCA of quality control samples in positive and negative ion mode. QC: quality control samples; PCA: principal component analysis. The values are shown as the means ± SDs. n = 3‒4 for each independent group. * p < 0.05, ** p < 0.01. Figure S2. LC-MS/MS analysis of lungs. (A) Flow chart of LC-MS/MS analysis of lungs. (B) PCA of the LC-MS/MS data. (C) Score scatter plots for OPLS−DA. (D) Volcano plot of differentiated expressed metabolites of LC-MS/MS in lungs. TableS1thespatialdistributionofobtainedmetaboliteions.xlsx Table S1 the spatial distribution of obtained metabolite ions TableS2significantexpressedmetabolitesofAFADESIMSI.xlsx Table S2 significant expressed metabolites of AFADESI-MSI TableS369KEGGpathwayenrichmentofAFADESIMSI.xlsx Table S3 69 KEGG pathway enrichment of AFADESI-MSI TableS4metabolitesassociatedwiththebiosynthesisofunsaturatedfattyacidsandarachidonicacidmetabolisminaginglungs.docx Table S4 metabolites associated with the biosynthesis of unsaturated fatty acids and arachidonic acid metabolism in aging lungs TableS5significantexpressedmetabolitesofuntargetedmetabolomicanalysis.xlsx Table S5 significant expressed metabolites of untargeted metabolomic analysis Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7113832","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490399819,"identity":"b74784dc-a09d-402a-8bbb-da90a0240648","order_by":0,"name":"Lifeng Yan","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lifeng","middleName":"","lastName":"Yan","suffix":""},{"id":490399820,"identity":"4ca80949-099e-4120-a223-859ee8427c5d","order_by":1,"name":"Xiahui Ge","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiahui","middleName":"","lastName":"Ge","suffix":""},{"id":490399821,"identity":"92bf4e5c-61ac-4756-b521-6d06ecfd5be8","order_by":2,"name":"Huaqi Guo","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huaqi","middleName":"","lastName":"Guo","suffix":""},{"id":490399822,"identity":"ab3267cf-ac78-4440-8ab0-2640de7413f9","order_by":3,"name":"Yu Xie","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Xie","suffix":""},{"id":490399823,"identity":"d264db65-dec7-4baa-aea3-207fbd65a1b5","order_by":4,"name":"Weining Xiong","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weining","middleName":"","lastName":"Xiong","suffix":""},{"id":490399824,"identity":"114c4f39-7260-470e-8d04-15b5edc35249","order_by":5,"name":"Lijun Zhu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Zhu","suffix":""},{"id":490399825,"identity":"e9a457ca-0490-439a-b76d-e22100920bc5","order_by":6,"name":"Tianyu Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3PsWoCQRDG8ZGFMcWA7RTBe4VPhGDhw6wEtDoQ8gJKIBYqPoKvsJ3YCYfYmN7DJhBIlSJlwIO4Z2dzbilk/9UW82NniGKx+6z2WaDbxOWNTgBgMpBhvw0yJdEgwio/Wc9dCAWQZDmDKsxgVV9s3e9QqTGZ2kpSexP4OF3PMs49J92/u0piWGAtJHWHZz6IJ9C0mrAnmw10AE/yIoQIS2s8AmxJjkG/KPdfDMG23D57Oj5C5eYtyWu2OlHxl2A3/sq/i26zMZlXE6IHXG96Y7ys/hEwFIvFYv+6MyGtQPe2fYZZAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Tianyu","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-07-13 14:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7113832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7113832/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87709957,"identity":"11e8b6db-2a58-46e1-83eb-3d19e3fd35eb","added_by":"auto","created_at":"2025-07-28 08:30:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":253182,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistological analysis and aging-associated markers of lung tissues.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) HE staining of the lung. The gray scale bars (upper) represent 600 μm,and the black scale bars (bottom) represent 50 μm. (B) Quantification of the mean linear intercept (MLI), mean alveolar diameter (MAD) and mean alveolar area (MAA) of the lung tissues. (C) mRNA expression levels of the senescence-associated markers \u003cem\u003ep21\u003c/em\u003e, \u003cem\u003ep19\u003c/em\u003e, \u003cem\u003ep16\u003c/em\u003e, and \u003cem\u003eTrp53\u003c/em\u003e in the lung tissues of each group, with Actb as the internal reference. The values are shown as the means ± SDs. n = 6 for each independent group. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/6e7b159cb42a7b246b0a8a24.png"},{"id":87708617,"identity":"fd955ca6-50f8-46b8-8375-9e37867ed8a8","added_by":"auto","created_at":"2025-07-28 08:22:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":327904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReproducibility validation of the AFADESI-MSI data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Strategy for the discovery of aging-associated metabolic alterations. (B) Example of a microscopy-MSI overlay. (C) Representative mass spectra overlaid in positive and negative ion modes, highlighting the m/z 125-425 range (purple box). (D) UMAP and spatial visualization in positive and negative ion modes. AFADESI: airflow-assisted desorption electrospray ionization; MSI: mass spectrometry imaging; UMAP: uniform manifold approximation and projection.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/5ed7e51ea1537ca4908f90c0.png"},{"id":87709958,"identity":"88dc7ba5-ef4a-4cf9-bb09-12773cff19ee","added_by":"auto","created_at":"2025-07-28 08:30:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential analysis of the metabolic profiles of whole lungs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PCA of samples in positive (up) and negative (down) ion modes. (B) The score scatter plots for OPLS−DA in positive (up) and negative (down) ion modes. (C) Volcano plots of the positive (up) and negative (down) ion modes. (D) Top 20 enriched KEGG pathways. PCA: principal component analysis, OPLS−DA: orthogonal partial least squares-discriminant analysis.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/0643b61d48e54a84bb56a3b5.png"},{"id":87709973,"identity":"a31714ec-5172-4b8f-8340-0b248f0f45d0","added_by":"auto","created_at":"2025-07-28 08:30:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":430200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn situ visualization of crucial metabolites in unsaturated fatty acid and arachidonic acid metabolism.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Distributions and (B) relative intensities of representative metabolites in AFADESI-MSI analysis. (C) Lipid metabolism related KEGG pathway analysis of LC-MS/MS. (D)A column chart of Fc and P values of 12-ketotetrahydroleukotriene B4, adrenic and palmitic acid in AFADESI–MSI (*** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001) and LC-MS/MS (## \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ### \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001). PCA: Principal Component Analysis, OPLS−DA: Orthogonal Partial Least Squares-Discriminant Analysis. AFADESI: airflow-assisted desorption electrospray ionization; MSI: mass spectrometry imaging.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/495ac1752c86163aae88971c.png"},{"id":87709963,"identity":"744d9187-a1aa-4c64-b34b-eca1424fd325","added_by":"auto","created_at":"2025-07-28 08:30:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":459675,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of crucial metabolic enzymes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Diagrammatic sketch illustratingthe reactions among 12 key metabolites and 5 crucial metabolic enzymes. Lung expression of ELOVL2, LTA4H, PTGR1, PLA2 and FABP6 in each group via (B) qRT-PCR and (C) IHC staining. The values are shown as the means ± SDs. n = 6 for each independent group. *p \u0026lt; 0.05, ***p \u0026lt; 0.001. (D) Pathways of unsaturated fatty acids and arachidonic acid, with a focus on adrenic acid-LTB4 imbalance in aging lungs. qRT-PCR: quantitative real-time polymerase chain reaction.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/f70f2abf6190eb1ba9c8fea5.png"},{"id":88875592,"identity":"4a806e5e-8e69-46c5-b811-b469e2f9ea0f","added_by":"auto","created_at":"2025-08-12 10:02:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1971609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/64573047-27b6-4575-b78b-03986d8388b0.pdf"},{"id":87709962,"identity":"954cd06e-5ae0-4c15-90bc-0d20ecdef589","added_by":"auto","created_at":"2025-07-28 08:30:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":626821,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. Quality control data. \u003c/strong\u003e(A) The deviation in the AFADESI-MSI analysis was evaluated by the distribution of the runs. Line plots of the data extracted from a blank area generated via PCA using components 1 and 2 show the stability of the AFAI-MSI system. Line plots depicting the first and second components of positive ion mode MSIs acquired from masses \u0026gt; 500 Da and ≤ 500 Da and negative ion mode MSIs acquired from masses \u0026gt; 500 Da and ≤ 500 Da. X-axis: run order; Y-axis: standard deviation. (B) PCA of quality control samples in positive and negative ion mode. QC: quality control samples; PCA: principal component analysis. The values are shown as the means ± SDs. n = 3‒4 for each independent group. * \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2. LC-MS/MS analysis of lungs.\u003c/strong\u003e (A) Flow chart of LC-MS/MS analysis of lungs. (B) PCA of the LC-MS/MS data. (C) Score scatter plots for OPLS−DA. (D) Volcano plot of differentiated expressed metabolites of LC-MS/MS in lungs.\u003c/p\u003e","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/eb2f14641bbebd1324788e87.docx"},{"id":87708620,"identity":"7b6e7de0-542a-483f-ab27-571d150c4b93","added_by":"auto","created_at":"2025-07-28 08:22:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":250209,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1 the spatial distribution of obtained metabolite ions\u003c/p\u003e","description":"","filename":"TableS1thespatialdistributionofobtainedmetaboliteions.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/d7ba452264d64c017b5cd616.xlsx"},{"id":87709961,"identity":"35d7d562-4eea-4d4a-825d-2d9478148d61","added_by":"auto","created_at":"2025-07-28 08:30:15","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":61654,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2 significant expressed metabolites of AFADESI-MSI\u003c/p\u003e","description":"","filename":"TableS2significantexpressedmetabolitesofAFADESIMSI.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/8ae218e6c272767db756baa1.xlsx"},{"id":87708632,"identity":"12275890-80ca-4070-9098-bc16c877822f","added_by":"auto","created_at":"2025-07-28 08:22:16","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17767,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3 69 KEGG pathway enrichment of AFADESI-MSI\u003c/p\u003e","description":"","filename":"TableS369KEGGpathwayenrichmentofAFADESIMSI.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/7350247b82e0706394db0be0.xlsx"},{"id":87708625,"identity":"6e94bd79-efc4-49ec-bd45-3b84a758806b","added_by":"auto","created_at":"2025-07-28 08:22:15","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":16428,"visible":true,"origin":"","legend":"\u003cp\u003eTable S4 metabolites associated with the biosynthesis of unsaturated fatty acids and arachidonic acid metabolism in aging lungs\u003c/p\u003e","description":"","filename":"TableS4metabolitesassociatedwiththebiosynthesisofunsaturatedfattyacidsandarachidonicacidmetabolisminaginglungs.docx","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/63482bd15868ab050ca0e435.docx"},{"id":87708636,"identity":"129df741-7a07-4cd7-b684-724451836f30","added_by":"auto","created_at":"2025-07-28 08:22:16","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":384117,"visible":true,"origin":"","legend":"\u003cp\u003eTable S5 significant expressed metabolites of untargeted metabolomic analysis\u003c/p\u003e","description":"","filename":"TableS5significantexpressedmetabolitesofuntargetedmetabolomicanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7113832/v1/0e955d4e8b72ffbc89bc7211.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Resolved Metabolomics via AFADESI-MSI Reveals Lipid Metabolic Alterations in Aging Lungs","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobal aging populations are projected to reach 2 billion (≥65 years) by 2050\u003csup\u003e1\u003c/sup\u003e. This demographic shift amplifies age-related disease burdens, particularly in the respiratory system. Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) incidence rises sharply with age\u003csup\u003e2-4\u003c/sup\u003e, disproportionately affecting Asia-Pacific regions where COPD prevalence is the world's highest\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePhysiological lung aging manifests as emphysema-like alterations driving pulmonary functional decline including enlarged alveolar airspaces, reduced surface area, thickening of the bronchial wall, diminished elastic lung recoil, and small airway loss\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e. Molecular hallmarks of aging (e.g., cellular senescence, mitochondrial dysfunction) underlie pulmonary structural changes\u003csup\u003e8\u003c/sup\u003e. Despite the universality of aging, the underlying mechanisms associated with physiological, structural, and cellular changes in the aging lung have yet to be fully elucidated\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMetabolomics, which profiles metabolites in cells, tissues, and biofluids, holds promise for discovering biomarkers of lung aging. However, metabolome-wide analyses of healthy aging lung tissue remain scarce. A recent NMR-based study identified 13 altered metabolites in aged mouse lungs, though lung lysate profiles showed less age-related variation than other organs\u003csup\u003e10\u003c/sup\u003e. Conventional metabolomics (e.g., NMR) fails to capture spatial heterogeneity, as evidenced by homogenate-based studies reporting minimal lung metabolic changes during aging. \u003c/p\u003e\n\u003cp\u003eMass spectrometry imaging (MSI) is a novel, label-free bioanalytical technique that provides simultaneous molecular and spatial information, making it highly suitable for \u003cem\u003ein situ\u003c/em\u003e metabolomics. Airflow-assisted desorption electrospray ionization (AFADESI)-MSI stands out as a high-coverage ambient MSI technique\u003csup\u003e11\u003c/sup\u003e. It enhances\u003cem\u003e in situ\u003c/em\u003e droplet collection and sampling by incorporating a high-rate extracting airflow into a custom-built ion source. This advancement enables the visualization of over 1500 endogenous metabolites at 100 μm resolution in heterogeneous tissues through untargeted analysis, providing robust structural and functional references for metabolite-based molecular histology\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e. AFADESI-MSI is characterized by wide coverage, wide dynamic range, rapid analysis procedure, high sensitivity and high specificity, making it particularly useful for identifying structure-specific and functionally relevant metabolites\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we apply AFADESI-MSI to define spatially resolved metabolic dysregulation in aging lungs. This approach enabled visualization of the spatial distribution and changes in aging-associated metabolites, offering metabolism-based insights into the molecular mechanisms of lung aging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e2.1 Animals\u003c/p\u003e\n\u003cp\u003eC57BL/6J male mice (6-week-old, 18-20g/mouse, n=6/group) were purchased from Jiangsu GemPharmatech Co. Ltd. and maintained under specific pathogen-free conditions (22–26°C, 40%–60% humidity, 12 h light/dark cycles) with free access to food and water. At 3 months or 24 months of age, mice were euthanized using an overdose of isoflurane anesthesia followed by cervical dislocation. The superior lobe of the left lung was fixed in 4% paraformaldehyde for histopathological analysis. The superior lobe of the right lung was embedded in optimal cutting temperature compound (OCT) for AFADESI–MSI analysis. The remaining lobes were stored at -80°C for further analysis. All the animal studies were approved by the Institutional Review and Ethics Board of the School of Medicine, Zhejiang University (No. 20221105). \u003c/p\u003e\n\u003cp\u003e2.2 Histopathological analysis of lung sections\u003c/p\u003e\n\u003cp\u003eSamples embedded in 4% paraformaldehyde were sectioned (5 μm) and H\u0026amp;E-stained for histology. Emphysematous lesions was quantified by mean alveolar area (MAA), diameter (MAD), and linear intercept (MLI) using Image-Pro Plus 6.0, as previously described\u003csup\u003e13\u003c/sup\u003e, with details provided in the Supplementary Materials. \u003c/p\u003e\n\u003cp\u003e2.3 Immunohistochemistry\u003c/p\u003e\n\u003cp\u003eDewaxed lung slides were subjected to antigen retrieval in citrate buffer (pH 6.0), blocked with 3% H₂O₂ (25 min) and 1% BSA-PBS (30 min), and incubated overnight at 4°C with primary antibodies against PTGR1 (1:200, CGB114486), LTA4H (1:200, 13662-1-AP), and ELOVL2 (1:200, CGB113896). Sections were then treated with HRP-conjugated goat anti-rabbit secondary antibody (60 min, 37°C), developed with DAB, counterstained with hematoxylin, dehydrated, and mounted.\u003c/p\u003e\n\u003cp\u003e2.4 Quantitative assessment of mRNA \u003c/p\u003e\n\u003cp\u003eTotal RNA was isolated from lung tissues, and the primers for the mRNAs were synthesized by Sangon Biotech (Shanghai) Co., Ltd., with details provided in the Supplementary Materials. \u003c/p\u003e\n\u003cp\u003e2.5 AFADESI-MSI \u003c/p\u003e\n\u003cp\u003eAFADESI–MSI analysis has been previously described\u003csup\u003e11-13\u003c/sup\u003e. Right lung superior lobes from 3 young/old mice were embedded in OCT, sectioned at 10 μm (-20°C), and dried in a vacuum desiccator for 30 min. Analysis was conducted by Shanghai Lu-Ming Biotech using an AFADESI-MSI platform (Q-Orbitrap mass spectrometer) with parameters: scanning at 200 μm/s (x-direction), 100 μm step (y-direction), mass range 100–1000 Da, resolution 70,000, spray voltage ±7 kV, capillary temperature 350°C, nitrogen spray gas (0.6 MPa), acetonitrile/water (80:20, 5 μL/min) solvent, and extracting gas flow 45 L/min (Xcalibur software 3.0). Metabolites were tentatively identified by matching exact masses (\u0026lt;5 ppm error) to smetDB, pySM, and literature, with tandem MS validation. Ion adducts considered: [M-H]−, [M+H]+, [M+Na]+, etc. Signals with unique annotations in ≥1 database were used. Raw data were converted to .imzML, imported into Cardinal R for ion image reconstruction, background subtraction, and OPLS-DA analysis. Differential metabolites were selected by VIP ≥1.0 and P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eMore information were provided in the Supplementary Materials.\u003c/p\u003e\n\u003cp\u003e2.6 Untargeted LC-MS/MS metabolomics analysis\u003c/p\u003e\n\u003cp\u003eThe sample preparation procedures were described in detail in our previous work\u003csup\u003e15\u003c/sup\u003e. Details are provided in the Supplementary Materials.\u003c/p\u003e\n\u003cp\u003e2.7 Statistical analysis\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed via SPSS software (version 18.0) and GraphPad Prism (version 8.0). Unpaired two-sided Student's t tests were used to compare the statistical significance between two groups. The results are presented as the mean ± SEM. The level of statistical significance was set at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 Histopathological analyses\u003c/p\u003e\n\u003cp\u003eTo investigate age-related pulmonary structural changes, we performed lung morphometry in mice. Histology showed 24-month-old mice had enlarged alveoli similar to aging human lungs (Fig. 1A), confirmed by increased mean linear intercept (MLI), alveolar diameter (MAD), and area (MAA) (Fig. 1B). Aging markers p21, p19, p16, and Trp53 were upregulated in aged lungs (Fig. 1C), indicating successful induction of lung biological aging.\u003c/p\u003e\n\u003cp\u003e3.2 AFADESI-MSI analysis of aging lungs\u003c/p\u003e\n\u003cp\u003eA flow chart of this strategy is shown in Fig. 2A. Quality control (QC) was performed to validate AFADESI-MSI reliability, using blank area datasets (without OCT) as QC samples. PCA analysis showed QC dataset deviations \u0026lt; 2 SD (Fig. S1A), confirming system stability for further analysis. PCA results of QC samples are shown in Fig. S1 B.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn situ\u003c/em\u003e metabolomic profiling of frozen lung sections from 3 young (3-month-old) and 3 old (24-month-old) mice was performed via AFADESI-MSI (Fig. 2A). Mass spectra were precisely extracted, and Fig. 2B shows a representative mass spectrometry image in negative ion mode with the corresponding HE-stained section. According to image reconstruction using all ions, AFADESI-MSI detected 1032 (pos) and 971 (neg) metabolites, with UMAP segmentation resolving spatial clusters (Table S1).\u003c/p\u003e\n\u003cp\u003eIn the average mass spectrum, the metabolites below m/z 500 included organoheterocyclic compounds (\u003cem\u003ee.g.,\u003c/em\u003e Temozolomide, m/z 215.029936344), organic acids (\u003cem\u003ee.g.,\u003c/em\u003e Taurine, m/z 124.007395563), fatty acids (\u003cem\u003ee.g.,\u0026nbsp;\u003c/em\u003eLinoleic acid, m/z 279.23299993), and nucleotides (\u003cem\u003ee.g.,\u003c/em\u003e Uridine, m/z 265.044243162), while metabolites above m/z 500 were mostly lipids and lipid-like molecules, such as diradylglycerols (\u003cem\u003ee.g.,\u003c/em\u003e DG(36:1), m/z 657.522030983), phosphosphingolipids (\u003cem\u003ee.g.,\u003c/em\u003e SM(d30:1), m/z 627.487165636), glycerophosphocholines (\u003cem\u003ee.g.,\u003c/em\u003e LysoPC(18:3), m/z 552.285201244), glycerophosphates (\u003cem\u003ee.g.,\u003c/em\u003e PA(38:6), m/z 719.465781352), and so on. In addition to the wide range of metabolite information, the ion intensities of the lung metabolites varied over a broad range from 10\u003csup\u003e3\u003c/sup\u003e to 10\u003csup\u003e6\u003c/sup\u003e. The ion intensities are shown in Fig. 2C. In addition to pathological division (HE-stained images), molecular segmentation via uniform manifold approximation and projection (UMAP) visualization was also utilized for ROI data extraction. In this study, UMAP spatial segmentation exhibited good clustering or grouping of different pixels based on metabolite profiling, which enabled the automatic discrimination of different physiological subregions (Fig. 2 D). These results indicate that the established AFADESI-MSI method features high sensitivity and wide coverage, allowing high-specificity detection of various types of endogenous metabolites.\u003c/p\u003e\n\u003cp\u003eTo identify global discriminative metabolites between aged and young mice, we performed differential analysis of lung metabolic profiles. Principal component analysis (PCA) of whole-lung metabolomes showed distinct clustering between groups (Fig. 3A), while supervised OPLS-DA further resolved discriminative metabolites, with score plots demonstrating clear separation in both positive and negative ion modes (Fig. 3B). The cumulative R2X, R2Y and Q2 values were 0.881, 0.975, and 0.588, respectively, for OPLS-DA in positive ion mode and 0.927, 0.99, and 0.879, respectively. Volcano plots were also drawn to visualize the dysregulated metabolites in the lungs between the two groups (Fig. 3C). Based on VIP \u0026ge;1.0 and P \u0026lt; 0.05, 96 (positive ion mode) and 208 (negative ion mode) discriminative metabolites were tentatively identified (Table S2). These altered metabolites are related to the metabolism of lipids, nucleotides and their derivatives, organic oxygen compounds, organoheterocyclic compounds, benzenoids, and organic acids and derivatives. In addition, KEGG pathway enrichment analysis identified a total of 69 pathways (Table S3). KEGG analysis highlighted lipid metabolism dysregulation, particularly in unsaturated fatty acid and arachidonic acid pathways (Fig. 3D).\u003c/p\u003e\n\u003cp\u003e3.3 Perturbation of the biosynthesis of unsaturated fatty acids and arachidonic acid metabolism\u003c/p\u003e\n\u003cp\u003eA total of 201 significantly altered lipid-related metabolites were identified, 23 of which were associated with unsaturated fatty acid and arachidonic acid metabolism. Among these, 12 key metabolites\u0026mdash;adrenic acid (22:4n6), palmitic acid, oleic acid, linoleic acid, gamma-linolenic acid, dihomo-gamma-linolenic acid, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), 11Z-eicosenoic acid, arachidonic acid, leukotriene B4 (LTB4), and 12-keto-tetrahydro-LTB4\u0026mdash;along with 11 isomers were annotated (Table S4). The spatial distribution and relative intensities of these 12 key metabolites are depicted in Fig. 4A‒B.\u003c/p\u003e\n\u003cp\u003eMetabolic pathways involving adrenic acid (22:4n6), palmitic acid, oleic acid, linoleic acid, gamma-linolenic acid, dihomo-gamma-linolenic acid, EPA, DHA and 11Z-eicosenoic acid are closely associated with the biosynthesis of unsaturated fatty acids. In the lungs of aged mice, palmitic acid levels were significantly reduced, whereas the other metabolites were significantly increased. Notably, arachidonic acid and adrenic acid metabolism are associated with both the biosynthesis of unsaturated fatty acids and the downstream arachidonic acid metabolic pathways. MS imaging revealed pronounced upregulation of arachidonic acid and concurrent downregulation of adrenic acid in the lungs of 24-month-old mice compared with those of younger controls. Furthermore, downstream arachidonic acid metabolites, including LTB4 and its oxidized derivative 12-keto-tetrahydro-LTB4, accumulated extensively throughout aged lungs.\u003c/p\u003e\n\u003cp\u003eTo further elucidate the metabolic perturbations in unsaturated fatty acid and arachidonic acid metabolism in aging lungs, LC-MS/MS analysis was performed on lung tissue samples (Fig. S2 A). The PCA and OPLS-DA score plots derived from the LC-MS/MS data demonstrated distinct metabolic clustering between young and aged mice (Fig. S2 B‒C). Differentially abundant metabolites were identified using a threshold of VIP values \u0026gt; 1.0 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05, revealing 910 dysregulated metabolites in aged lungs, including 404 lipids and lipid-like molecules (Fig. S2 D and Table S5). KEGG pathway analysis revealed that these overlapping metabolites were significantly associated with lipid metabolism pathways, including glycerophospholipid metabolism, biosynthesis of unsaturated fatty acids, steroid hormone biosynthesis, alpha-linolenic acid metabolism, ether lipid metabolism, linoleic acid metabolism, arachidonic acid metabolism, sphingolipid metabolism, fatty acid elongation, fatty acid degradation, and fatty acid biosynthesis (Fig. 4C). LC-MS/MS confirmed three metabolites previously detected via AFADESI-MSI: 12-keto-tetrahydro-LTB4, adrenic acid and palmitic acid. Bar charts depicting the fold changes (Fc) and P values for these metabolites were generated to illustrate their differential regulation (Fig. 4D). Overall, spatial metabolomic profiling revealed profound perturbations in unsaturated fatty acids and arachidonic acid metabolism in aged lungs. Notably, the most prominent metabolic disturbances included elevated levels of LTB4, as well as decreased levels of adrenic acid and palmitic acid, highlighting the adrenic acid-LTB4 imbalance as a key driver of age-related pulmonary pathophysiology.\u003c/p\u003e\n\u003cp\u003e3.4 Validation of crucial metabolic enzymes\u003c/p\u003e\n\u003cp\u003eFive crucial metabolic enzymes closely associated with the altered metabolites were selected as potential aging-related metabolic enzymes. Table 1 and Fig 5A offer detailed information on these metabolic enzymes and their related metabolites, along with the metabolic processes involved. qRT-PCR and IHC analyses of these metabolic enzymes are shown in Fig. 5B and Fig. 5C, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eScreened potential aging-associated metabolic enzymes in lung\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull name\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEC number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelated metabolites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eELOVL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eelongation of very-long-chain fatty acids-like elongases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.3.1.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eArachidonic acid\u003c/p\u003e\n \u003cp\u003eAdrenic acid\u003c/p\u003e\n \u003cp\u003eGamma-linoleic acid\u003c/p\u003e\n \u003cp\u003eDihomo-gamma-linoleic acid\u003c/p\u003e\n \u003cp\u003eEicosapentaenoic acid\u003c/p\u003e\n \u003cp\u003eDocosahexaenoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eElongation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFABP6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eFatty acid-binding protein subclass 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eInvolved in fatty acid uptake, transport, and metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLTA4H\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eLeukotriene A 4 hydrolase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.3.2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eLeukotriene A4\u003c/p\u003e\n \u003cp\u003eLeukotriene B4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eCatalyze the conversion of leukotriene A4 to leukotriene B4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLA2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ephospholipase A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1.1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eArachidonic acid\u003c/p\u003e\n \u003cp\u003ePalmitic acid\u003c/p\u003e\n \u003cp\u003eOleic acid\u003c/p\u003e\n \u003cp\u003eDocosahexaenoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eCatabolize phospholipids to polyunsaturated fatty acids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTGR1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eProstaglandin reductase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.3.1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eLeukotriene B4\u003c/p\u003e\n \u003cp\u003e12-Keto-leukotriene B4\u003c/p\u003e\n \u003cp\u003e12-Keto-tetrahydro-leukotriene B4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eCatalyzes the conversion of leukotriene B4 to 12-oxo-leukotriene B4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn fatty acid metabolism, elongation is the process of extending fatty acid carbon chains via specific enzymatic reactions, which are primarily catalyzed by the ELOVL family of enzymes\u003csup\u003e16\u003c/sup\u003e. On the basis of the AFADESI-MSI results, ELOVL elongases are involved in elongating gamma-linolenic acid and arachidonic acid to produce dihomo-gamma-linolenic acid and adrenic acid, respectively\u003csup\u003e17\u003c/sup\u003e. Additionally, ELOVL elongases contribute to the synthesis of EPA and DHA\u003csup\u003e18\u003c/sup\u003e. ELOVL2 is crucial for adrenic acid synthesis\u003csup\u003e19\u003c/sup\u003e. IHC staining revealed decreased ELOVL2 expression in the lungs of 24-month-old mice, which was consistent with the distribution of adrenic acid. qRT-PCR confirmed that Elovl2 expression was reduced in the lungs of aged mice.\u003c/p\u003e\n\u003cp\u003eLeukotriene A4 hydrolase (LTA4H), the final and rate-limiting enzyme in the biosynthesis of leukotriene B4, was upregulated in the lungs of old mice compared with those of young mice, as shown by IHC and qPCR, which was consistent with the findings of MS imaging. Prostaglandin reductase 1 (PTGR1) catalyzes the conversion of LTB4 to 12-keto-tetrahydro-LTB4. IHC and qRT-PCR revealed higher PTGR1 expression in 24-month-old mice than in 3-month-old controls, aligning with the 12-keto-tetrahydro-LTB4 levels. The degradation of phospholipids by phospholipase A2 (PLA2) can release polyunsaturated fatty acids (PUFAs), such as arachidonic acid, palmitic acid, oleic acid, and docosahexaenoic acid, which play key roles in modulating cellular functions \u003csup\u003e20\u003c/sup\u003e. IHC revealed lower PLA2 expression in 24-month-old mice than in 3-month-old controls, but the qRT-PCR results were not significant. Fatty acid-binding protein subclass 6 (FABP6) is an enzyme that is involved in fatty acid uptake, transport, and metabolism\u003csup\u003e21\u003c/sup\u003e. IHC staining and qRT-PCR assessment of lung sections revealed a marked decrease in Fabp6 expression in the lungs of old mice compared with young mice.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study controlled for environmental variables to isolate age-specific lung effects. Natural aging induced pulmonary structural remodeling, shown by alveolar enlargement and upregulated senescence markers (p16, p21 and Trp53), confirming biological lung aging in 24-month-old mice. Using advanced AFADESI-MSI spatial metabolomics, we systematically mapped global metabolic perturbations in lung tissue. A total of 304 differentially abundant metabolites were identified in the aging lungs, revealing a striking age-dependent dysregulation of lipid homeostasis. Lipids are a diverse class of molecules (fatty acids, glycerophospholipids, sphingolipids, triacyl glycerides, and cholesterol esters) important for membrane formation, energy storage, and signaling\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Lipid metabolism and signaling have long been recognized to influence aging and longevity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Importantly, emerging evidence implicates lipid-mediated immunomodulation in respiratory pathophysiology\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, with documented metabolic alterations in chronic lung diseases including interstitial lung disease\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, asthma\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, COPD\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and lung cancer\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. High-resolution spatial mapping revealed substantial metabolic perturbations in unsaturated fatty acid and arachidonic acid metabolism in aged lungs. LC-MS/MS analysis confirmed marked disruptions in the metabolic pathways of LTB4, adrenic acid, and palmitic acid. Furthermore, five key metabolic enzymes associated with these metabolites presented altered expression levels in the lungs of aged mice, corroborating the AFADESI-MSI findings. As a result, adrenic acid-LTB4 imbalance drives pro-inflammatory microenvironments in lung aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eLTB4, an arachidonic acid-derived eicosanoid mediator, plays a pivotal role in pro-inflammatory responses by binding to its specific receptors on immune cells, \u003cem\u003ee.g.\u003c/em\u003e, neutrophils, monocytes/macrophages, T cells, and dendritic cells. This ligand‒receptor interaction triggers cellular functions, including chemotaxis, adhesion to vascular endothelial cells, release of lysosomal enzymes, and generation of reactive oxygen species\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Therefore, LTB4 has been implicated in various inflammatory diseases, including asthma, atherosclerosis, glomerulonephritis, lupus nephritis, rheumatoid arthritis, and inflammatory bowel disease\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Recent study reported that LTB4 levels correlate with COPD exacerbation frequency. We observed evaluated lung LTB4 levels in naturally aged mice, extending prior reports of age-related LTB4 accumulation in the hippocampus and heart\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. LTA4H, the rate-limiting enzyme in LTB4 biosynthesis, was upregulated in the lungs of aged mice. This enzymatic dysregulation aligns with clinical observations of LTA4H-mediated progression in acute lung injury, idiopathic pulmonary fibrosis, and allergic asthma\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Furthermore, 12-keto-tetrahydro-LTB4, a metabolite resulting from the lipid beta-oxidation of LTB4, exhibited significant accumulation in the lungs of aged mice. PTGR1, the enzyme that specifically catalyzes the conversion of LTB4 to 12-keto-tetrahydro-LTB4, showed concordant elevated expression in aged mice. This metabolic shift is correlated with reported PTGR1 overexpression in lung cancer patients with poor prognosis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, suggesting that pathways related to lung aging and carcinogenesis are shared. These findings demonstrate significant upregulation of LTB4 and its metabolites in the lungs of aged mice, indicating that pro-inflammatory mechanisms are closely associated with age-related pulmonary aging.\u003c/p\u003e\u003cp\u003eAdrenic acid (22:4n6), an ω-6 polyunsaturated fatty acid (PUFA) downstream of arachidonic acid metabolism, has been shown to decrease in the human brain with age\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, a trend that is consistent with our findings in natural aging lungs. This metabolic alteration is correlated with impaired expression of the lipid elongation enzyme ELOVL2, a conserved aging biomarker governing long-chain PUFA biosynthesis, in aging lungs. Decreased ELOVL2 expression is associated with age-related dysfunctions, such as visual impairment\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Notably, adrenic acid exhibits potent anti-inflammatory properties through competitive inhibition of LTB4 biosynthesis. In the K/BxN serum\u0026ndash;transferred murine arthritis model, adrenic acid effectively blocks the production of leukotriene B4 and alleviates arthritis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Our results also revealed an inverse relationship between adrenal acid and LTB4 levels, further supporting this antagonistic interaction. The accumulation of pro-inflammatory arachidonic acid-derived metabolites, such as LTB4 and 12-keto-tetrahydro-LTB4, alongside the depletion of anti-inflammatory adrenic acid, underscores a metabolic shift toward a pro-inflammatory microenvironment in aging lungs. These findings align with the \"inflammaging\" theory, where chronic low-grade inflammation drives age-related organ dysfunction\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Critically, this metabolic dysregulation directly links to pathogenesis in chronic lung diseases.\u003c/p\u003e\u003cp\u003ePalmitic acid (16:0, PA), the most prevalent saturated fatty acid in the human body\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, serves as a precursor for the synthesis of unsaturated fatty acids. Through the action of desaturase enzymes, which introduce double bonds into saturated fatty acids, palmitic acid can be converted into monounsaturated fatty acids such as oleic acid\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This process is crucial for maintaining the balance between saturated and unsaturated fatty acids within cells. Palmitic acid can drive cells into senescence by disrupting reactive oxygen species (ROS) generation\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, mitochondrial dynamics\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and endoplasmic reticulum homeostasis\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Palmitic acid can be released by PLA2 through the hydrolysis of phospholipids\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In our study, PLA2 expression was reduced in aged lungs, and the AFADESI-MSI and LC-MS/MS results indicated a significant decrease in palmitic acid levels. Previous studies have indicated that PLA2 loss contributes to age-related cognitive decline and neuroinflammation\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. However, our results revealed an inverse trend in palmitic acid levels compared with previous studies\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The decrease in PLA2 expression and subsequent reduction in palmitic acid levels in aged lungs may reflect a complex metabolic adaptation to aging, which could explain the heterogeneity of the results. While palmitic acid can induce senescence through various mechanisms, the overall impact on aged lungs might be influenced by other concurrent changes. The decrease in palmitic acid levels in aging lungs might be a compensatory mechanism to mitigate the detrimental effects of excessive palmitic acid, such as lipotoxicity and cellular damage. Moreover, the disagreement in results could also be attributed to insufficient sample size in some studies. Our findings highlighted the complex palmitic acid biosynthesis disorder that occurs in aged lungs and suggested a shift in fatty acid metabolism from saturated to unsaturated fatty acids in aging-related lung pathologies.\u003c/p\u003e\u003cp\u003eAFADESI-MSI demonstrated exceptional utility in mapping spatially resolved metabolic networks, overcoming the homogenization bias of traditional metabolomics. However, challenges persist\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Low-abundance metabolites, such as specialized pro-resolving mediators (SPMs), often evade detection. This issue could be mitigated by employing derivatization strategies to increase the sensitivity. A notable complexity in spatial metabolomics is that a single m/z value may correspond to multiple metabolites. This primarily arises from three factors: isobaric interference due to the resolution limitations of the mass spectrometer; structural similarity among different metabolites, such as gamma- or alpha-linolenic acid; and metabolite derivatization during sample preparation or ionization. Furthermore, the high-throughput nature of spatial metabolomics, which aims to detect hundreds to thousands of metabolites in a single experiment, may lead to some resolution sacrifice to improve detection efficiency, which can result in signal overlap for low-abundance metabolites. Despite these challenges, significant improvements in detection specificity can be achieved through ongoing instrument upgrades, rigorous data validation processes, and advanced multidimensional analysis techniques. These advancements are crucial for enhancing the precision and reliability of spatial metabolomics studies.\u003c/p\u003e\u003cp\u003eOur study revealed discrepancies between spatial (AFADESI-MSI) and traditional (LC-MS/MS) metabolomics: AFADESI-MSI identified 304 metabolites, whereas LC-MS/MS detected 910, with only 3/23 lipid metabolites showing consistent trends. These differences stem from technical biases: MSI excels in non-polar lipid detection (e.g., lipids) but misses polar metabolites, while LC-MS/MS captures hydrophilic metabolites but lacks spatial resolution. Homogenization in LC-MS/MS dilutes locally enriched lipids, explaining reduced lipid signals. Previous research has shown that high spatial expression of tetrahydropalmatine in angiogenic regions may be masked by homogenate untargeted data\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Some metabolites have region-specific effects resulting in the failure of untargeted data to capture their dual roles. Spatial metabolomics reveals metabolite co-localization (e.g., glycogen accumulation in pulmonary fibrosis regions\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e), whereas untargeted metabolomics pathway enrichment analysis may yield opposite conclusions because spatial associations are ignored. Our MS images showed clustered lipid accumulation in aged lung regions, aligning with aging's spatial compartmentalization\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Additionally, systematic errors in sample processing and data pretreatment also might influence the results of spatial and traditional metabolomics.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study presents a spatially resolved metabolic map of aging lungs, highlighting the pivotal role of unsaturated fatty acid and arachidonic acid pathway dysregulation in age-related pulmonary pathologies. Collectively, these results elucidate lipid metabolic reprogramming in aging lungs, where adrenic acid-LTB4 imbalance drives alveolar inflammaging. Future research should integrate multi-omics approaches with functional validation to unravel the mechanistic networks underlying inflammation-driven age-related lung diseases. Future pharmacological restoration of adrenic acid, e.g., via ELOVL2 activators or LTA4H inhibitors, could rebalance the pro-inflammatory axis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAFADESI-MSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eairflow-assisted desorption electrospray ionization mass spectrometry imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eARDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eacute respiratory distress syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic lung diseases, including chronic obstructive pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDHA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edocosahexaenoic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edeoxyribonucleic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eeicosapentaenoic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eELOVL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eelongation of very-long-chain fatty acids-like elongases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFABP6\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efatty acid-binding protein subclass 6\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHE stain\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehematoxylin-eosin stain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIPF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eidiopathic pulmonary fibrosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ekyoto encyclopedia of genes and genomes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLTA4H\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eleukotriene A 4 hydrolase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLC-MS/MS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eliquid chromatography-tandem mass spectrometry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean alveolar area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean alveolar diameter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMALDI-MSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ematrix-assisted laser desorption ionization mass spectrometry imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean linear intercepts\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emass spectrometry imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enuclear magnetic resonance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoptimal cutting temperature compound\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOPLS-DA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eorthogonal partial least squares-discriminant analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epolymerase chain reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ephospholipase A2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLS-DA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epartial least squares discriminant analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePTGR1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProstaglandin reductase 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003equality control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eqRT‒PCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003equantitative real-time polymerase chain reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eribonucleic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eregions of interest\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etotal ion current\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUMAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003euniform manifold approximation and projection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evariable importance of projection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe raw reads of all the samples were deposited in METASPACE as .imzML and .ibd files with the reference link: https://metaspace2020.eu/project/AFADESI_MSI_aging_lung. Each sample had a positive ion mode and a negative ion mode analytical dataset. There are a total of 12 datasets in this METASPACE project, and each dataset has corresponding .imzML and .ibd files. The file name contains \u0026lsquo;D-3 m\u0026rsquo; for the young group, while the file name contains \u0026lsquo;A-24 m\u0026rsquo; for the aging group.\u003c/p\u003e\n\u003cp\u003eFundings\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (grant numbers 82090015, 82330001, 82470031, 82400080, 82400035, and 82271587), the Shanghai Municipal Health Commission Health Industry Research Special Young Project (grant number 20234Y0091).\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe are grateful to Junyi Lin from Department of Forensic Medicine, Shanghai Medical College of Fudan University for his invaluable assistance with pathologic evaluation. We extend our thanks to Shanghai Lu Ming Biotech Co., Ltd. (Shanghai, China) for conducting the AFADESI spatially resolved metabolomics in this study. We also appreciate the essential suggestions and technical support provided by Zhenyu Xu and Chuanyu Liu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eTY. Z and LF. Y conceived the research. TY. Z also performed sample collection and data management. LF. Y, XH. G and HQ. G performed the data analysis and interpretation. Y.X contributed to the data analysis. WN.X supervised the study. LJ. Z established the animal model. The manuscript was written and revised by TY. Z, XH. G and LF. Y. All the authors reviewed and approved the submitted manuscript.\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEthics approval and consent to participante\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll the animal studies were approved by the Institutional Review and Ethics Board of the School of Medicine, Zhejiang University (No. 20221105). This study followed the ARRIVE guidelines and complied with the Guidelines for the Ethical Review of Laboratory Animal Welfare, People\u0026apos;s Republic of China National Standard (GB/T 35892-2018).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare no competing interests in this research.\u003cbr\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDzau VJ, Inouye SK, Rowe JW, Finkelman E, Yamada T. Enabling Healthful Aging for All - The National Academy of Medicine Grand Challenge in Healthy Longevity. N Engl J Med 2019;381:1699-701.\u003c/li\u003e\n \u003cli\u003eWang C, Hao X, Chen S. Calling for improved pulmonary and critical care medicine in China and beyond. Chinese Medical Journal Pulmonary and Critical Care Medicine 2023;1:1-2.\u003c/li\u003e\n \u003cli\u003eRaghu G, Chen SY, Hou Q, Yeh WS, Collard HR. Incidence and prevalence of idiopathic pulmonary fibrosis in US adults 18-64 years old. Eur Respir J 2016;48:179-86.\u003c/li\u003e\n \u003cli\u003evan Durme Y, Verhamme KMC, Stijnen T, et al. Prevalence, incidence, and lifetime risk for the development of COPD in the elderly: the Rotterdam study. Chest 2009;135:368-77.\u003c/li\u003e\n \u003cli\u003eAdeloye D, Song P, Zhu Y, Campbell H, Sheikh A, Rudan I. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. Lancet Respir Med 2022;10:447-58.\u003c/li\u003e\n \u003cli\u003eVerleden SE, Kirby M, Everaerts S, et al. Small airway loss in the physiologically ageing lung: a cross-sectional study in unused donor lungs. Lancet Respir Med 2021;9:167-74.\u003c/li\u003e\n \u003cli\u003eSchneider JL, Rowe JH, Garcia-de-Alba C, Kim CF, Sharpe AH, Haigis MC. The aging lung: Physiology, disease, and immunity. Cell 2021;184:1990-2019.\u003c/li\u003e\n \u003cli\u003eLopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell 2013;153:1194-217.\u003c/li\u003e\n \u003cli\u003ePartridge L, Deelen J, Slagboom PE. Facing up to the global challenges of ageing. Nature 2018;561:45-56.\u003c/li\u003e\n \u003cli\u003eWang X, Yin G, Zhang W, Song K, Zhang L, Guo Z. Prostaglandin Reductase 1 as a Potential Therapeutic Target for Cancer Therapy. Front Pharmacol 2021;12:717730.\u003c/li\u003e\n \u003cli\u003eChen D, Chao DL, Rocha L, et al. The lipid elongation enzyme ELOVL2 is a molecular regulator of aging in the retina. Aging Cell 2020;19:e13100.\u003c/li\u003e\n \u003cli\u003eHe J, Sun C, Li T, et al. A Sensitive and Wide Coverage Ambient Mass Spectrometry Imaging Method for Functional Metabolites Based Molecular Histology. Adv Sci (Weinh) 2018;5:1800250.\u003c/li\u003e\n \u003cli\u003eSun C, Li T, Song X, et al. Spatially resolved metabolomics to discover tumor-associated metabolic alterations. Proc Natl Acad Sci U S A 2019;116:52-7.\u003c/li\u003e\n \u003cli\u003eHuo M, Wang Z, Fu W, et al. Spatially Resolved Metabolomics Based on Air-Flow-Assisted Desorption Electrospray Ionization-Mass Spectrometry Imaging Reveals Region-Specific Metabolic Alterations in Diabetic Encephalopathy. J Proteome Res 2021;20:3567-79.\u003c/li\u003e\n \u003cli\u003eWant EJ, Masson P, Michopoulos F, et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc 2013;8:17-32.\u003c/li\u003e\n \u003cli\u003eNie L, Pascoa TC, Pike ACW, et al. The structural basis of fatty acid elongation by the ELOVL elongases. Nature structural \u0026amp; molecular biology 2021;28:512-20.\u003c/li\u003e\n \u003cli\u003eBaker EJ, Valenzuela CA, van Dooremalen WTM, et al. Gamma-Linolenic and Pinolenic Acids Exert Anti-Inflammatory Effects in Cultured Human Endothelial Cells Through Their Elongation Products. Molecular nutrition \u0026amp; food research 2020;64:e2000382.\u003c/li\u003e\n \u003cli\u003eTakić M, Ranković S, Girek Z, et al. Current Insights into the Effects of Dietary \u0026alpha;-Linolenic Acid Focusing on Alterations of Polyunsaturated Fatty Acid Profiles in Metabolic Syndrome. Int J Mol Sci 2024;25.\u003c/li\u003e\n \u003cli\u003eShrestha N, Holland OJ, Kent NL, et al. Maternal High Linoleic Acid Alters Placental Fatty Acid Composition. Nutrients 2020;12.\u003c/li\u003e\n \u003cli\u003eFrisardi V, Panza F, Seripa D, Farooqui T, Farooqui AA. Glycerophospholipids and glycerophospholipid-derived lipid mediators: a complex meshwork in Alzheimer\u0026apos;s disease pathology. Prog Lipid Res 2011;50:313-30.\u003c/li\u003e\n \u003cli\u003eDing L, Yang L, Wang Z, Huang W. Bile acid nuclear receptor FXR and digestive system diseases. Acta Pharm Sin B 2015;5:135-44.\u003c/li\u003e\n \u003cli\u003eMutlu AS, Duffy J, Wang MC. Lipid metabolism and lipid signals in aging and longevity. Dev Cell 2021;56:1394-407.\u003c/li\u003e\n \u003cli\u003eParkhitko AA, Filine E, Mohr SE, Moskalev A, Perrimon N. Targeting metabolic pathways for extension of lifespan and healthspan across multiple species. Ageing Res Rev 2020;64:101188.\u003c/li\u003e\n \u003cli\u003eBerthon BS, Wood LG. Nutrition and respiratory health--feature review. Nutrients 2015;7:1618-43.\u003c/li\u003e\n \u003cli\u003eZhao T, Y. S. Mechanisms and Therapeutic Potential of Myofibroblast Transformation in Pulmonary Fibrosis. . Journal of Respiratory Biology and Translational Medicine 2025;2.\u003c/li\u003e\n \u003cli\u003eKim JS, Steffen BT, Podolanczuk AJ, et al. Associations of omega-3 Fatty Acids With Interstitial Lung Disease and Lung Imaging Abnormalities Among Adults. Am J Epidemiol 2021;190:95-108.\u003c/li\u003e\n \u003cli\u003eLee-Sarwar K, Kelly RS, Lasky-Su J, et al. Dietary and Plasma Polyunsaturated Fatty Acids Are Inversely Associated with Asthma and Atopy in Early Childhood. J Allergy Clin Immunol Pract 2019;7:529-38 e8.\u003c/li\u003e\n \u003cli\u003eKotlyarov S, Kotlyarova A. Anti-Inflammatory Function of Fatty Acids and Involvement of Their Metabolites in the Resolution of Inflammation in Chronic Obstructive Pulmonary Disease. Int J Mol Sci 2021;22.\u003c/li\u003e\n \u003cli\u003eLuu HN, Cai H, Murff HJ, et al. A prospective study of dietary polyunsaturated fatty acids intake and lung cancer risk. Int J Cancer 2018;143:2225-37.\u003c/li\u003e\n \u003cli\u003eSamuelsson B, Dahlen SE, Lindgren JA, Rouzer CA, Serhan CN. Leukotrienes and lipoxins: structures, biosynthesis, and biological effects. Science 1987;237:1171-6.\u003c/li\u003e\n \u003cli\u003eNakamura M, Shimizu T. Leukotriene receptors. Chem Rev 2011;111:6231-98.\u003c/li\u003e\n \u003cli\u003eChinnici CM, Yao Y, Pratico D. The 5-lipoxygenase enzymatic pathway in the mouse brain: young versus old. Neurobiol Aging 2007;28:1457-62.\u003c/li\u003e\n \u003cli\u003eKain V, Ingle KA, Kachman M, et al. Excess omega-6 fatty acids influx in aging drives metabolic dysregulation, electrocardiographic alterations, and low-grade chronic inflammation. Am J Physiol Heart Circ Physiol 2018;314:H160-H9.\u003c/li\u003e\n \u003cli\u003eRao NL, Riley JP, Banie H, et al. Leukotriene A(4) hydrolase inhibition attenuates allergic airway inflammation and hyperresponsiveness. Am J Respir Crit Care Med 2010;181:899-907.\u003c/li\u003e\n \u003cli\u003eZhao Y, Weng CC, Tong M, Wei J, Tai HH. Restoration of leukotriene B(4)-12-hydroxydehydrogenase/15- oxo-prostaglandin 13-reductase (LTBDH/PGR) expression inhibits lung cancer growth in vitro and in vivo. Lung Cancer 2010;68:161-9.\u003c/li\u003e\n \u003cli\u003eHancock SE, Friedrich MG, Mitchell TW, Truscott RJW, Else PL. Changes in Phospholipid Composition of the Human Cerebellum and Motor Cortex during Normal Ageing. Nutrients 2022;14.\u003c/li\u003e\n \u003cli\u003eNorris SE, Friedrich MG, Mitchell TW, Truscott RJW, Else PL. Human prefrontal cortex phospholipids containing docosahexaenoic acid increase during normal adult aging, whereas those containing arachidonic acid decrease. Neurobiol Aging 2015;36:1659-69.\u003c/li\u003e\n \u003cli\u003eBrouwers H, Jonasdottir HS, Kuipers ME, et al. Anti-Inflammatory and Proresolving Effects of the Omega-6 Polyunsaturated Fatty Acid Adrenic Acid. J Immunol 2020;205:2840-9.\u003c/li\u003e\n \u003cli\u003eCapri M, Conte M, Ciurca E, et al. Long-term human spaceflight and inflammaging: Does it promote aging? Ageing Res Rev 2023;87:101909.\u003c/li\u003e\n \u003cli\u003eCarta G, Murru E, Banni S, Manca C. Palmitic Acid: Physiological Role, Metabolism and Nutritional Implications. Frontiers in physiology 2017;8:902.\u003c/li\u003e\n \u003cli\u003ePalomer X, Pizarro-Delgado J, Barroso E, V\u0026aacute;zquez-Carrera M. Palmitic and Oleic Acid: The Yin and Yang of Fatty Acids in Type 2 Diabetes Mellitus. Trends Endocrinol Metab 2018;29:178-90.\u003c/li\u003e\n \u003cli\u003eWan F, He X, Xie W. Canagliflozin Inhibits Palmitic Acid-Induced Vascular Cell Aging In Vitro through ROS/ERK and Ferroptosis Pathways. Antioxidants (Basel, Switzerland) 2024;13.\u003c/li\u003e\n \u003cli\u003eSun Y, Wang J, Guo X, et al. Oleic Acid and Eicosapentaenoic Acid Reverse Palmitic Acid-induced Insulin Resistance in Human HepG2 Cells via the Reactive Oxygen Species/JUN Pathway. Genomics, proteomics \u0026amp; bioinformatics 2021;19:754-71.\u003c/li\u003e\n \u003cli\u003eChen X, Chen K, Hu J, et al. Palmitic acid induces lipid droplet accumulation and senescence in nucleus pulposus cells via ER-stress pathway. Communications biology 2024;7:539.\u003c/li\u003e\n \u003cli\u003eJiao L, Shao W, Quan W, et al. iPLA2\u0026beta; loss leads to age-related cognitive decline and neuroinflammation by disrupting neuronal mitophagy. Journal of neuroinflammation 2024;21:228.\u003c/li\u003e\n \u003cli\u003eWang Z, He B, Liu Y, et al. In situ metabolomics in nephrotoxicity of aristolochic acids based on air flow-assisted desorption electrospray ionization mass spectrometry imaging. Acta Pharm Sin B 2020;10:1083-93.\u003c/li\u003e\n \u003cli\u003eCui H, Yang X, Wang Z, et al. Tetrahydropalmatine triggers angiogenesis via regulation of arginine biosynthesis. Pharmacological research 2021;163:105242.\u003c/li\u003e\n \u003cli\u003eConroy LR, Clarke HA, Allison DB, et al. Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis. Nature communications 2023;14:2759.\u003c/li\u003e\n \u003cli\u003eL\u0026oacute;pez-Ot\u0026iacute;n C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell 2023;186:243-78.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aging, aging lung, mass spectrometry imaging, lipid metabolic dysfunction, unsaturated fatty acid, arachidonic acid","lastPublishedDoi":"10.21203/rs.3.rs-7113832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7113832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The global burden of age-related respiratory diseases—most notably COPD and IPF—has reached unprecedented levels. Understanding the unique metabolic profiles of lung tissue is essential for elucidating the molecular mechanisms of natural lung aging. However, the spatially resolved metabolic drivers of pulmonary aging remain uncharacterized. We employed airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) to map lipid dysregulation in aging lungs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Lung tissues from young (3-month) and aged (24-month) C57BL/6J mice were analyzed using AFADESI-MSI for spatially resolved metabolomics. Key findings were validated by LC-MS/MS and immunohistochemical analysis of metabolic enzymes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: AFADESI-MSI revealed compartmentalized lipid remodeling in aged lungs, particularly in unsaturated fatty acid and arachidonic acid metabolism, showing significant perturbations. High-resolution spatial mapping demonstrated distinct distribution patterns of these metabolites. LC-MS/MS validation confirmed reduced levels of adrenic acid and palmitic acid, alongside elevated levels of 12-ketotetrahydroleukotriene B4, indicating pro-inflammatory and oxidative stress progression in lung aging. Additionally, five key metabolic enzymes (ELOVL, FABP6, LTA4H, PLA2, and PTGR1) associated with these metabolites exhibited altered expression patterns in aged mouse lungs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Our study highlights the potential of AFADESI-MSI as an innovative tool for metabolic biomarker research and suggests directions for future multi-omics and functional validation studies. Spatially resolved metabolomics approach uncovered multilevel molecular alterations during lung aging, offering insights into the metabolic reprogramming of unsaturated fatty acid and arachidonic acid pathways. These findings demonstrate that adrenic acid-LTB4 imbalance drives pro-inflammatory microenvironments in lung aging.\u003c/p\u003e","manuscriptTitle":"Spatial Resolved Metabolomics via AFADESI-MSI Reveals Lipid Metabolic Alterations in Aging Lungs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 08:22:10","doi":"10.21203/rs.3.rs-7113832/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e60af3fa-73f8-4462-be6f-e335b4b91943","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52071065,"name":"Biological sciences/Biochemistry"},{"id":52071066,"name":"Health sciences/Biomarkers"},{"id":52071067,"name":"Health sciences/Diseases"},{"id":52071068,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-08-12T09:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 08:22:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7113832","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7113832","identity":"rs-7113832","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}