Lipid metabolism in sickness and in health: Emerging regulators of lipotoxicity.

Lipids play crucial roles in signal transduction, contribute to the structural integrity of cellular membranes, and regulate energy metabolism. Questions remain as to which lipid species maintain metabolic homeostasis and which disrupt essential cellular functions, leading to metabolic disorders. Here, we discuss recent advances in understanding lipid metabolism with a focus on catabolism, synthesis, and signaling. Technical advances, including functional genomics, metabolomics, lipidomics, lipid-protein interaction maps, and advances in mass spectrometry, have uncovered new ways to prioritize molecular mechanisms mediating lipid function. By reviewing what is known about the distinct effects of specific lipid species in physiological pathways, we provide a framework for understanding newly identified targets regulating lipid homeostasis with implications for ameliorating metabolic diseases.

Lipidomic scanning of self-lipids identifies headless antigens for natural killer T cells.

To broadly measure the spectrum of cellular self-antigens for natural killer T cells (NKT), we developed a sensitive lipidomics system to analyze lipids trapped between CD1d and NKT T cell receptors (TCRs). We captured diverse antigen complexes formed in cells from natural endogenous lipids, with or without inducing endoplasmic reticulum (ER) stress. After separating protein complexes with no, low, or high CD1d-TCR interaction, we eluted lipids to establish the spectrum of self-lipids that facilitate this interaction. Although this unbiased approach identified fifteen molecules, they clustered into only two related groups: previously known phospholipid antigens and unexpected neutral lipid antigens. Mass spectrometry studies identified the neutral lipids as ceramides, deoxyceramides, and diacylglycerols, which can be considered headless lipids because they lack polar headgroups that usually form the TCR epitope. The crystal structure of the TCR-ceramide-CD1d complex showed how the missing headgroup allowed the TCR to predominantly contact CD1d, supporting a model of CD1d autoreactivity. Ceramide and related headless antigens mediated physiological TCR binding affinity, weak NKT cell responses, and tetramer binding to polyclonal human and mouse NKT cells. Ceramide and sphingomyelin are oppositely regulated components of the "sphingomyelin cycle" that are altered during apoptosis, transformation, and ER stress. Thus, the unique molecular link of ceramide to NKT cell response, along with the recent identification of sphingomyelin blockers of NKT cell activation, provide two mutually reinforcing links for NKT cell response to sterile cellular stress conditions.

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Platelet lipidomics and de novo lipogenesis: impact on health and disease.

PURPOSE OF REVIEW: Lipids play vital roles in platelet structure, signaling, and metabolism. In addition to capturing exogenous lipids, platelets possess the capacity for de novo lipogenesis, regulated by acetyl-coA carboxylase 1 (ACC1). This review aims to cover the critical roles of platelet de novo lipogenesis and lipidome in platelet production, function, and diseases. RECENT FINDINGS: Upon platelet activation, approximately 20% of the platelet lipidome undergoes significant modifications, primarily affecting arachidonic acid-containing species. Multiple studies emphasize the impact of de novo lipogenesis, with ACC1 as key player, on platelet functions. Mouse models suggest the importance of the AMPK-ACC1 axis in regulating platelet membrane arachidonic acid content, associated with TXA 2 secretion, and thrombus formation. In human platelets, ACC1 inhibition leads to reduced platelet reactivity. Remodeling of the platelet lipidome, alongside with de novo lipogenesis, is also crucial for platelet biogenesis. Disruptions in the platelet lipidome are observed in various pathological conditions, including cardiovascular and inflammatory diseases, with associations between these alterations and shifts in platelet reactivity highlighted. SUMMARY: The platelet lipidome, partially regulated by ACC-driven de novo lipogenesis, is indispensable for platelet production and function. It is implicated in various pathological conditions involving platelets.

Imputation of missing values in lipidomic datasets.

Lipidomic data often exhibit missing data points, which can be categorized as missing completely at random (MCAR), missing at random, or missing not at random (MNAR). In order to utilize statistical methods that require complete datasets or to improve the identification of potential effects in statistical comparisons, imputation techniques can be employed. In this study, we investigate commonly used methods such as zero, half-minimum, mean, and median imputation, as well as more advanced techniques such as k-nearest neighbor and random forest imputation. We employ a combination of simulation-based approaches and application to real datasets to assess the performance and effectiveness of these methods. Shotgun lipidomics datasets exhibit high correlations and missing values, often due to low analyte abundance, characterized as MNAR. In this context, k-nearest neighbor approaches based on correlation and truncated normal distributions demonstrate best performance. Importantly, both methods can effectively impute missing values independent of the type of missingness, the determination of which is nearly impossible in practice. The imputation methods still control the type I error rate.

Four-dimensional lipidomics profiling in X-linked adrenoleukodystrophy using trapped ion mobility mass spectrometry.

Lipids play pivotal roles in an extensive range of metabolic and physiological processes. In recent years, the convergence of trapped ion mobility spectrometry and MS has enabled 4D-lipidomics, a highly promising technology for comprehensive lipid analysis. 4D-lipidomics assesses lipid annotations across four distinct dimensions-retention time, collisional cross section, m/z (mass-to-charge ratio), and MS/MS spectra-providing a heightened level of confidence in lipid annotation. These advantages prove particularly valuable when investigating complex disorders involving lipid metabolism, such as adrenoleukodystrophy (ALD). ALD is characterized by the accumulation of very-long-chain fatty acids (VLCFAs) due to pathogenic variants in the ABCD1 gene. A comprehensive 4D-lipidomics strategy of ALD fibroblasts demonstrated significant elevations of various lipids from multiple classes. This indicates that the changes observed in ALD are not confined to a single lipid class and likely impacts a broad spectrum of lipid-mediated physiological processes. Our findings highlight the incorporation of mainly saturated and monounsaturated VLCFA variants into a range of lipid classes, encompassing phosphatidylcholines, triacylglycerols, and cholesterol esters. These include ultra-long-chain fatty acids with a length of up to thirty carbon atoms. Lipid species containing C26:0 and C26:1 were the most frequently detected VLCFA lipids in our study. Furthermore, we report a panel of 121 new candidate biomarkers in fibroblasts, exhibiting significant differentiation between controls and individuals with ALD. In summary, this study demonstrates the capabilities of a 4D-lipid profiling workflow in unraveling novel insights into the intricate lipid modifications associated with metabolic disorders like ALD.

Lipidomics and mass spectrometry imaging unveil alterations in mice hippocampus lipid composition exposed to hypoxia.

Lipids are components of cytomembranes that are involved in various biochemical processes. High-altitude hypoxic environments not only affect the body's energy metabolism, but these environments can also cause abnormal lipid metabolism involved in the hypoxia-induced cognitive impairment. Thus, comprehensive lipidomic profiling of the brain tissue is an essential step toward understanding the mechanism of cognitive impairment induced by hypoxic exposure. In the present study, mice showed reduced new-object recognition and spatial memory when exposed to hypobaric hypoxia for 1 day. Histomorphological staining revealed significant morphological and structural damage to the hippocampal tissue, along with prolonged exposure to hypobaric hypoxia. Dynamic lipidomics of the mouse hippocampus showed a significant shift in both the type and distribution of phospholipids, as verified by spatial lipid mapping. Collectively, a diverse and dynamic lipid composition in mice hippocampus was uncovered, which deepens our understanding of biochemical changes during sustained hypoxic exposure and could provide new insights into the cognitive decline induced by high-altitude hypoxia exposure.

Identification of TFG- and Autophagy-Regulated Proteins and Glycerophospholipids in B Cells.

Autophagy supervises the proteostasis and survival of B lymphocytic cells. Trk-fused gene (TFG) promotes autophagosome-lysosome flux in murine CH12 B cells, as well as their survival. Hence, quantitative proteomics of CH12tfgKO and WT B cells in combination with lysosomal inhibition should identify proteins that are prone to lysosomal degradation and contribute to autophagy and B cell survival. Lysosome inhibition via NH4Cl unexpectedly reduced a number of proteins but increased a large cluster of translational, ribosomal, and mitochondrial proteins, independent of TFG. Hence, we propose a role for lysosomes in ribophagy in B cells. TFG-regulated proteins include CD74, BCL10, or the immunoglobulin JCHAIN. Gene ontology (GO) analysis reveals that proteins regulated by TFG alone, or in concert with lysosomes, localize to mitochondria and membrane-bound organelles. Likewise, TFG regulates the abundance of metabolic enzymes, such as ALDOC and the fatty acid-activating enzyme ACOT9. To test consequently for a function of TFG in lipid metabolism, we performed shotgun lipidomics of glycerophospholipids. Total phosphatidylglycerol is more abundant in CH12tfgKO B cells. Several glycerophospholipid species with similar acyl side chains, such as 36:2 phosphatidylethanolamine and 36:2 phosphatidylinositol, show a dysequilibrium. We suggest a role for TFG in lipid homeostasis, mitochondrial functions, translation, and metabolism in B cells.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases.

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease.

Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism's role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.

Development of a Global Metabo-Lipid-Prote-omics Workflow to Compare Healthy Proximal and Distal Colonic Epithelium in Mice.

A multimetabo-lipid-prote-omics workflow was developed to characterize the molecular interplay within proximal (PC) and distal (DC) colonic epithelium of healthy mice. This multiomics data set lays the foundation to better understand the two tissue types and can be used to study, for example, colon-related diseases like colorectal cancer or inflammatory bowel disease. First, the methyl tert-butyl ether extraction method was optimized, so that from a single tissue biopsy >350 reference-matched metabolites, >1850 reference-matched lipids, and >4500 proteins were detected by using targeted and untargeted metabolomics, untargeted lipidomics, and proteomics. Next, each omics-data set was analyzed individually and then merged with the additional omics disciplines to generate a deep understanding of the underlying complex regulatory network within the colon. Our data demonstrates, for example, differences in mucin formation, detected on substrate level as well as on enzyme level, and altered lipid metabolism by the detection of phospholipases hydrolyzing sphingomyelins to ceramides. In conclusion, the combination of the three mass spectrometry-based omics techniques can better entangle the functional and regional differences between PC and DC tissue compared to each single omics technique.

Multiomics Evaluation of Human iPSCs and iPSC-Derived Neurons.

Human induced pluripotent stem cells (iPSCs) can be differentiated into neurons, providing living human neurons to model brain diseases. However, it is unclear how different types of molecules work together to regulate stem cell and neuron biology in healthy and disease states. In this study, we conducted integrated proteomics, lipidomics, and metabolomics analyses with confident identification, accurate quantification, and reproducible measurements to compare the molecular profiles of human iPSCs and iPSC-derived neurons. Proteins, lipids, and metabolites related to mitosis, DNA replication, pluripotency, glycosphingolipids, and energy metabolism were highly enriched in iPSCs, whereas synaptic proteins, neurotransmitters, polyunsaturated fatty acids, cardiolipins, and axon guidance pathways were highly enriched in neurons. Mutations in the GRN gene lead to the deficiency of the progranulin (PGRN) protein, which has been associated with various neurodegenerative diseases. Using this multiomics platform, we evaluated the impact of PGRN deficiency on iPSCs and neurons at the whole-cell level. Proteomics, lipidomics, and metabolomics analyses implicated PGRN's roles in neuroinflammation, purine metabolism, and neurite outgrowth, revealing commonly altered pathways related to neuron projection, synaptic dysfunction, and brain metabolism. Multiomics data sets also pointed toward the same hypothesis that neurons seem to be more susceptible to PGRN loss compared to iPSCs, consistent with the neurological symptoms and cognitive impairment from patients carrying inherited GRN mutations.

Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning.

Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including autism, Alzheimer's disease, dementia, cardiovascular disease, dry eye disease, and diabetes, and thus are molecules worthy of biomarker exploration studies. Since triacylglycerols and wax esters make up the majority of skin-surface lipid secretions, a viable sampling method for these potential biomarkers would be that of groomed latent fingerprints. Currently, however, blood-based sampling protocols predominate in the field. The invasiveness of a blood draw limits its utility to protected populations, including children and the elderly. Herein we describe a noninvasive means for sample collection (from fingerprints) paired with fast MS data-acquisition (MassIVE data set MSV000092742) and efficient data analysis via machine learning. Using both supervised and unsupervised classification, we demonstrate the usefulness of this method in determining whether a variable of interest imparts measurable change within the lipidomic data set. As a proof-of-concept, we show that the method is capable of distinguishing between the fingerprints of different individuals as well as between anatomical sebum collection regions. This noninvasive, high-throughput approach enables future lipidomic biomarker researchers to more easily include underrepresented, protected populations, such as children and the elderly, thus moving the field closer to definitive disease diagnoses that apply to all.

Lipidomics Study of Type 1 Diabetic Rats Using Online Phase Transition Trapping-Supercritical Fluid Extraction-Chromatography Coupled with Quadrupole Time-of-Flight Tandem Mass Spectrometry.

Chromatography-mass spectrometry-based lipidomics represents an essential tool for elucidating lipid dysfunction mechanisms and is extensively employed in investigating disease mechanisms and identifying biomarkers. However, the detection of low-abundance lipids in biological matrices, along with cumbersome operational procedures, complicates comprehensive lipidomic analyses, necessitating the development of highly sensitive, environmentally friendly, and automated methods. In this study, an online phase transition trapping-supercritical fluid extraction-chromatography-mass spectrometry (PTT-SFEC-MS/MS) method was developed and successfully applied to plasma lipidomics analysis in Type 1 diabetes (T1D) rats. The PTT strategy captured entire extracts at the column head by converting CO2 from a supercritical state to a gaseous state, thereby preventing peak spreading, enhancing peak shape for precise quantification, and boosting sensitivity without any sample loss. This method utilized only 5 µL of plasma and accomplished sample extraction, separation, and detection within 27 min. Ultimately, 77 differential lipids were identified, including glycerophospholipids, sphingolipids, and glycerolipids, in T1D rat plasma. The results indicated that the progression of the disease might be linked to alterations in glycerophospholipid and sphingolipid metabolism. Our findings demonstrated a green, highly efficient, and automated method for the lipidomics analysis of biological samples, providing a scientific foundation for understanding the pathogenesis and diagnosis of T1D.

Reinforcing the Evidence of Mitochondrial Dysfunction in Long COVID Patients Using a Multiplatform Mass Spectrometry-Based Metabolomics Approach.

Despite the recent and increasing knowledge surrounding COVID-19 infection, the underlying mechanisms of the persistence of symptoms for a long time after the acute infection are still not completely understood. Here, a multiplatform mass spectrometry-based approach was used for metabolomic and lipidomic profiling of human plasma samples from Long COVID patients (n = 40) to reveal mitochondrial dysfunction when compared with individuals fully recovered from acute mild COVID-19 (n = 40). Untargeted metabolomic analysis using CE-ESI(+/-)-TOF-MS and GC-Q-MS was performed. Additionally, a lipidomic analysis using LC-ESI(+/-)-QTOF-MS based on an in-house library revealed 447 lipid species identified with a high confidence annotation level. The integration of complementary analytical platforms has allowed a comprehensive metabolic and lipidomic characterization of plasma alterations in Long COVID disease that found 46 relevant metabolites which allowed to discriminate between Long COVID and fully recovered patients. We report specific metabolites altered in Long COVID, mainly related to a decrease in the amino acid metabolism and ceramide plasma levels and an increase in the tricarboxylic acid (TCA) cycle, reinforcing the evidence of an impaired mitochondrial function. The most relevant alterations shown in this study will help to better understand the insights of Long COVID syndrome by providing a deeper knowledge of the metabolomic basis of the pathology.

Distinctive Lipid Characteristics of Colorectal Cancer Revealed through Non-targeted Lipidomics Analysis of Tongue Coating.

The metabolites and microbiota in tongue coating display distinct characteristics in certain digestive disorders, yet their relationship with colorectal cancer (CRC) remains unexplored. Here, we employed liquid chromatography coupled with tandem mass spectrometry to analyze the lipid composition of tongue coating using a nontargeted approach in 30 individuals with colorectal adenomas (CRA), 32 with CRC, and 30 healthy controls (HC). We identified 21 tongue coating lipids that effectively distinguished CRC from HC (AUC = 0.89), and 9 lipids that differentiated CRC from CRA (AUC = 0.9). Furthermore, we observed significant alterations in the tongue coating lipid composition in the CRC group compared to HC/CRA groups. As the adenoma-cancer sequence progressed, there was an increase in long-chain unsaturated triglycerides (TG) levels and a decrease in phosphatidylethanolamine plasmalogen (PE-P) levels. Furthermore, we noted a positive correlation between N-acyl ornithine (NAOrn), sphingomyelin (SM), and ceramide phosphoethanolamine (PE-Cer), potentially produced by members of the Bacteroidetes phylum. The levels of inflammatory lipid metabolite 12-HETE showed a decreasing trend with colorectal tumor progression, indicating the potential involvement of tongue coating microbiota and tumor immune regulation in early CRC development. Our findings highlight the potential utility of tongue coating lipid analysis as a noninvasive tool for CRC diagnosis.

Mapping the lipidome in mitochondria-associated membranes (MAMs) in an in vitro model of Alzheimer's disease.

The disruption of mitochondria-associated endoplasmic reticulum (ER) membranes (MAMs) plays a relevant role in Alzheimer's disease (AD). MAMs have been implicated in neuronal dysfunction and death since it is associated with impairment of functions regulated in this subcellular domain, including lipid synthesis and trafficking, mitochondria dysfunction, ER stress-induced unfolded protein response (UPR), apoptosis, and inflammation. Since MAMs play an important role in lipid metabolism, in this study we characterized and investigated the lipidome alterations at MAMs in comparison with other subcellular fractions, namely microsomes and mitochondria, using an in vitro model of AD, namely the mouse neuroblastoma cell line (N2A) over-expressing the APP familial Swedish mutation (APPswe) and the respective control (WT) cells. Phospholipids (PLs) and fatty acids (FAs) were isolated from the different subcellular fractions and analyzed by HILIC-LC-MS/MS and GC-MS, respectively. In this in vitro AD model, we observed a down-regulation in relative abundance of some phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and lysophosphatidylethanolamine (LPE) species with PUFA and few PC with saturated and long-chain FA. We also found an up-regulation of CL, and antioxidant alkyl acyl PL. Moreover, multivariate analysis indicated that each organelle has a specific lipid profile adaptation in N2A APPswe cells. In the FAs profile, we found an up-regulation of C16:0 in all subcellular fractions, a decrease of C18:0 levels in total fraction (TF) and microsomes fraction, and a down-regulation of 9-C18:1 was also found in mitochondria fraction in the AD model. Together, these results suggest that the over-expression of the familial APP Swedish mutation affects lipid homeostasis in MAMs and other subcellular fractions and supports the important role of lipids in AD physiopathology.

Mass spectrometry imaging highlights dynamic patterns of lipid co-expression with Aß plaques in mouse and human brains.

Lipids play crucial roles in the susceptibility and brain cellular responses to Alzheimer's disease (AD) and are increasingly considered potential soluble biomarkers in cerebrospinal fluid (CSF) and plasma. To delineate the pathological correlations of distinct lipid species, we conducted a comprehensive characterization of both spatially localized and global differences in brain lipid composition in AppNL-G-F mice with spatial and bulk mass spectrometry lipidomic profiling, using human amyloid-expressing (h-Aß) and WT mouse brains controls. We observed age-dependent increases in lysophospholipids, bis(monoacylglycerol) phosphates, and phosphatidylglycerols around Aß plaques in AppNL-G-F mice. Immunohistology-based co-localization identified associations between focal pro-inflammatory lipids, glial activation, and autophagic flux disruption. Likewise, in human donors with varying Braak stages, similar studies of cortical sections revealed co-expression of lysophospholipids and ceramides around Aß plaques in AD (Braak stage V/VI) but not in earlier Braak stage controls. Our findings in mice provide evidence of temporally and spatially heterogeneous differences in lipid composition as local and global Aß-related pathologies evolve. Observing similar lipidomic changes associated with pathological Aß plaques in human AD tissue provides a foundation for understanding differences in CSF lipids with reported clinical stage or disease severity.