Energy metabolism as a target for cyclobenzaprine: A drug candidate against Visceral Leishmaniasis.

Leishmaniases have a broad spectrum of clinical manifestations, ranging from a cutaneous to a progressive and fatal visceral disease. Chemotherapy is nowadays the almost exclusive way to fight the disease but limited by its scarce therapeutic arsenal, on its own compromised by adverse side effects and clinical resistance. Cyclobenzaprine (CBP), an FDA-approved oral muscle relaxant drug has previously demonstrated in vitro and in vivo activity against Leishmania sp., but its targets were not fully unveiled. This study aimed to define the role of energy metabolism as a target for the leishmanicidal mechanisms of CBP. Methodology to assess CBP leishmanicidal mechanism variation of intracellular ATP levels using living Leishmania transfected with a cytoplasmic luciferase. Induction of plasma membrane permeability by assessing depolarization with DiSBAC(2)3 and entrance of the vital dye SYTOX® Green. Mitochondrial depolarization by rhodamine 123 accumulation. Mapping target site within the respiratory chain by oxygen consumption rate. Reactive oxygen species (ROS) production using MitoSOX. Morphological changes by transmission electron microscopy. CBP caused on L. infantum promastigotes a decrease of intracellular ATP levels, with irreversible depolarization of plasma membrane, the collapse of the mitochondrial electrochemical potential, mild uncoupling of the respiratory chain, and ROS production, with ensuing intracellular Ca2+ imbalance and DNA fragmentation. Electron microscopy supported autophagic features but not a massive plasma membrane disruption. The severe and irreversible mitochondrial damage induced by CBP endorsed the bioenergetics metabolism as a relevant target within the lethal programme induced by CBP in Leishmania. This, together with the mild-side effects of this oral drug, endorses CBP as an appealing novel candidate as a leishmanicidal drug under a drug repurposing strategy.

Applications of Metabolomics in Calcium Metabolism Disorders in Humans.

The pathogenesis of the disorders of calcium metabolism is not fully understood. This review discusses the studies in which metabolomics was applied in this area. Indeed, metabolomics could play an essential role in discovering biomarkers and elucidating pathological mechanisms. Despite the limited bibliography, the present review highlights the potential of metabolomics in identifying the biomarkers of some of the most common endocrine disorders, such as primary hyperparathyroidism (PHPT), secondary hyperparathyroidism (SHPT), calcium deficiency, osteoporosis and vitamin D supplementation. Metabolites related to above-mentioned diseorders were grouped into specific classes and mapped into metabolic pathways. Furthermore, disturbed metabolic pathways can open up new directions for the in-depth exploration of the basic mechanisms of these diseases at the molecular level.

CEU Mass Mediator 3.0: A Metabolite Annotation Tool.

CEU Mass Mediator (CMM, http://ceumass.eps.uspceu.es ) is an online tool that has evolved from a simple interface to query different metabolomic databases (CMM 1.0) to a tool that unifies the compounds from these databases and, using an expert system with knowledge about the experimental setup and the compounds properties, filters and scores the query results (CMM 2.0). Since this last major revision, CMM has continued to grow, expanding the knowledge base of its expert system and including new services to support researchers in the metabolite annotation and identification process. The information from external databases has been refreshed, and an in-house library with oxidized lipids not present in other sources has been added. This has increased the number of experimental metabolites up 332,665 and the number of predicted metabolites to 681,198. Furthermore, new taxonomy and ontology metadata have been included. CMM has expanded its functionalities with a service for the annotation of oxidized glycerophosphocholines, a service for spectral comparison from MS2 data, and a spectral quality-assessment service to determine the reliability of a spectrum for compound identification purposes. To facilitate the collaboration and integration of CMM with external tools and metabolomic platforms, a RESTful API has been created, and it has already been integrated into the HMDB (Human Metabolome Database). This paper will present the novel functionalities incorporated into version 3.0 of CMM.

Flow Cytometry Has a Significant Impact on the Cellular Metabolome.

The characterization of specialized cell subpopulations in a heterogeneous tissue is essential for understanding organ function in health and disease. A popular method of cell isolation is fluorescence-activated cell sorting (FACS) based on probes that bind surface or intracellular markers. In this study, we analyze the impact of FACS on the cell metabolome of mouse peritoneal macrophages. Compared with directly pelleted macrophages, FACS-treated cells had an altered content of metabolites related to the plasma membrane, activating a mechanosensory signaling cascade causing inflammation-like stress. The procedure also triggered alterations related to energy consumption and cell damage. The observed changes mostly derive from the physical impact on cells during their passage through the instrument. These findings provide evidence of FACS-induced biochemical changes, which should be taken into account in the design of robust metabolic assays of cells separated by flow cytometry.

Metabolomics Reveal Altered Postprandial Lipid Metabolism After a High-Carbohydrate Meal in Men at High Genetic Risk of Diabetes.

BACKGROUND: The transcription factor 7-like 2 (TCF7L2) gene confers one of the strongest genetic predispositions to type 2 diabetes, but diabetes development can be modified by diet. OBJECTIVE: The aim of our study was to evaluate postprandial metabolic alterations in healthy men with a high genetic risk of diabetes, after two meals with varying macronutrient content. METHODS: The study was conducted in 21 homozygous nondiabetic men carrying the high-risk (HR, n = 8, age: 31.2 ± 6.3 y, body mass index (BMI, kg/m2) 28.5 ± 8.1) or low-risk (LR, n = 13, age: 35.2 ± 10.3 y, BMI: 28.1 ± 6.4) genotypes at the rs7901695 locus. During two meal challenge test visits subjects received standardized isocaloric (450 kcal) liquid meals: high-carbohydrate (HC, carbohydrates: 89% of energy) and normo-carbohydrate (NC, carbohydrates: 45% of energy). Fasting (0 min) and postprandial (30, 60, 120, 180 min) plasma samples were analyzed for metabolite profiles through untargeted metabolomics. Metabolic fingerprinting was performed on an ultra-high-performance liquid chromatography (UHPLC) system connected to an iFunnel quadrupole-time-of-flight (Q-TOF) mass spectrometer. RESULTS: In HR-genotype men, after the intake of an HC-meal, we noted a significantly lower area under the curves (AUCs) of postprandial plasma concentrations of most of the phospholipids (-37% to -53%, variable importance in the projection (VIP) = 1.2-1.5), lysophospholipids (-29% to -86%, VIP = 1.1-2.6), sphingolipids (-32% to -47%, VIP = 1.1-1.3), as well as arachidonic (-36%, VIP = 1.4) and oleic (-63%, VIP = 1.3) acids, their metabolites: keto- and hydoxy-fatty acids (-38% to -78%, VIP = 1.3-2.5), leukotrienes (-65% to -83%, VIP = 1.4-2.2), uric acid (-59%, VIP = 1.5), and pyroglutamic acid (-65%, VIP = 1.8). The AUCs of postprandial sphingosine concentrations were higher (125-832%, VIP = 1.9-3.2) after the NC-meal, AUCs of acylcarnitines were lower (-21% to -61%, VIP = 1.1-2.4), and AUCs of fatty acid amides were higher (51-508%, VIP = 1.7-3.1) after the intake of both meals. CONCLUSIONS: In nondiabetic men carrying the TCF7L2 HR genotype, subtle but detectable modifications in intermediate lipid metabolism are induced by an HC-meal. This trial was registered at www.clinicaltrials.gov as NCT03792685.

Complex Interplay between Sphingolipid and Sterol Metabolism Revealed by Perturbations to the Leishmania Metabolome Caused by Miltefosine.

With the World Health Organization reporting over 30,000 deaths and 200,000 to 400,000 new cases annually, visceral leishmaniasis is a serious disease affecting some of the world's poorest people. As drug resistance continues to rise, there is a huge unmet need to improve treatment. Miltefosine remains one of the main treatments for leishmaniasis, yet its mode of action (MoA) is still unknown. Understanding the MoA of this drug and parasite response to treatment could help pave the way for new and more successful treatments for leishmaniasis. A novel method has been devised to study the metabolome and lipidome of Leishmania donovani axenic amastigotes treated with miltefosine. Miltefosine caused a dramatic decrease in many membrane phospholipids (PLs), in addition to amino acid pools, while sphingolipids (SLs) and sterols increased. Leishmania major promastigotes devoid of SL biosynthesis through loss of the serine palmitoyl transferase gene (ΔLCB2) were 3-fold less sensitive to miltefosine than wild-type (WT) parasites. Changes in the metabolome and lipidome of miltefosine-treated L. major mirrored those of L. donovani A lack of SLs in the ΔLCB2 mutant was matched by substantial alterations in sterol content. Together, these data indicate that SLs and ergosterol are important for miltefosine sensitivity and, perhaps, MoA.

Differentiating signals to make biological sense - A guide through databases for MS-based non-targeted metabolomics.

Metabolite identification is one of the most challenging steps in metabolomics studies and reflects one of the greatest bottlenecks in the entire workflow. The success of this step determines the success of the entire research, therefore the quality at which annotations are given requires special attention. A variety of tools and resources are available to aid metabolite identification or annotation, offering different and often complementary functionalities. In preparation for this article, almost 50 databases were reviewed, from which 17 were selected for discussion, chosen for their online ESI-MS functionality. The general characteristics and functions of each database is discussed in turn, considering the advantages and limitations of each along with recommendations for optimal use of each tool, as derived from experiences encountered at the Centre for Metabolomics and Bioanalysis (CEMBIO) in Madrid. These databases were evaluated considering their utility in non-targeted metabolomics, including aspects such as identifier assignment, structural assignment and interpretation of results.

A Single In-Vial Dual Extraction Strategy for the Simultaneous Lipidomics and Proteomics Analysis of HDL and LDL Fractions.

A single in-vial dual extraction (IVDE) procedure for the subsequent analysis of lipids and proteins in the high-density lipoprotein (HDL) and low-density lipoprotein (LDL) fractions derived from the same biological sample is presented. On the basis of methyl-tert-butyl ether (MTBE) extraction, IVDE leads to the formation of three phases: a protein pellet at the bottom, an aqueous phase with polar compounds, and an ether phase with lipophilic compounds. After sample extraction, performed within a high-performance liquid chromatography vial insert, the ether phase was directly injected for lipid fingerprinting, while the protein pellet, after evaporation of the remaining sample, was used for proteomics analysis. Human HDL and LDL isolates were used to test the suitability of the IVDE methodology for lipid and protein analysis from a single sample in terms of data quality and matching composition to that of HDL and LDL. Subsequently, HDL and LDL fractions isolated from ApoE-KO and wild-type mice were used to validate the capacity of IVDE for revealing changes in lipid and protein abundance. Results indicate that IVDE can be successfully used for the subsequent analysis of lipids and proteins with the advantages of time saving, simplicity, and reduced sample amount.

Rapid and Reliable Identification of Phospholipids for Untargeted Metabolomics with LC-ESI-QTOF-MS/MS.

Lipids are important components of biological systems, and their role can be currently investigated by the application of untargeted, holistic approaches such as metabolomics and lipidomics. Acquired data are analyzed to find significant signals responsible for the differentiation between the investigated conditions. Subsequently, identification has to be performed to bring biological meaning to the obtained results. Lipid identification seems to be relatively easy due to the known characteristic fragments; however, the large number of structural isomers and the formation of different adducts makes it challenging and at risk of misidentification. The inspection of data, acquired for plasma samples by a standard metabolic fingerprinting method, revealed multisignal formations for phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins by the formation of ions such as [M + H](+), [M + Na](+), and [M + K](+) in positive ionization mode and [M - H](-), [M + HCOO](-), and [M + Cl](-) in negative mode. Moreover, sodium formate cluster formation was found for [M + H·HCOONa](+) and [H-H·HCOONa](-). The MS/MS spectrum obtained for each of the multi-ions revealed significant differences in the fragmentation, which were confirmed by the analysis of the samples in two independent research centers. After the inspection of an acquired spectra, a list of characteristic and diagnostic fragments was proposed that allowed for easy, quick, and robust lipid identification that provides information about the headgroup, formed adduct, and fatty acyl composition. This ensures successful identification, which is of great importance for the contextualization of data and results validation.

Missing value imputation strategies for metabolomics data.

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k-means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a "gray area" and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k-means nearest neighbor and the best approximation of positioning real zeros.

CE-MS-based serum fingerprinting to track evolution of type 2 diabetes mellitus.

Development of type 2 diabetes mellitus (T2DM) is preceded by insulin resistance (IR), which may evolve to impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT). IFG and IGT are considered as prediabetic states (PD). Prediabetes indicates the high risk for the future development of diabetes, it is estimated that up to 70% of prediabetics eventually develop T2DM. The risk of T2DM development is increased in overweight (OW) and obese (OB) people; however normal weight (NW) individuals also suffer from T2DM. The present study was designed to evaluate whether changes in polar metabolites induced by T2DM evolution are different between NW, overweight and obese individuals. CE-MS serum fingerprinting was performed on 197 serum samples obtained from OW, OB, and NW humans whom were IR, prediabetics, diabetics or with normal glucose homeostasis. Metabolic changes evoked by the progression of T2DM differ between obese, overweight, and normal weight subjects. Based on obtained results several metabolites can be proposed as a promising target to track T2DM evolution; BCAA in OW and NW humans, lysine in OB, while acetylcarnitine and methionine independently on body mass index. Validation of obtained results on larger population is required.

In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics.

The role of non-targeted metabolomics with its discovery power is constantly growing in many different fields of science. However, its biggest advantage of uncovering the unexpected is turning into one of its biggest bottlenecks, particularly in metabolite identification. Among different methods for metabolite identification or ID confirmation, tandem MS analysis plays a very important role. However, this method is limited to only certain types of MS analysers, making for example TOF-MS inaccessible for this type of metabolite identification. To overcome this, in-source fragmentation has been used to fragment molecules and obtain product ions. Since the molecule of interest is not isolated prior to its fragmentation, the acquired spectrum contains many different signals arising from the fragmentation of all compounds present in the sample. Therefore, to assign product ions to their precursors, a novel use of correlation analysis was tested with r ≥0.9 as an assignation of a product ion belonging to the precursor. This method and chosen cut-off was tested on three different sample complexity levels: conducting the analysis on a single standard, mix of co-eluting standards and on a plasma sample. Obtained results clearly proved the effectiveness of the proposed methodology for metabolite ID confirmation. Moreover, the proposed strategy can be successfully applied for semi-quantification of co-eluting molecules with the same monoisotopic mass but that differ in fragmentation pattern. The proposed methodology can greatly improve the robustness and throughput of identification in metabolomics studies by use of TOF-MS, which is crucial to obtain meaningful and trustful results.

Metabolic profiling reveals the nutraceutical effect of Gongolaria abies-marina and Rosmarinus officinalis extracts in a type 1 diabetes animal model.

Nutraceuticals have gained increasing interest, prompting the need to investigate plant extracts for their beneficial properties and potential side effects. This study aimed to assess the nutraceutical effects of environmentally clean extracts from Rosmarinus officinalis and Gongolaria abies-marina (formerly Cystoseira abies-marina (Phaeophyceae)) on the metabolic profile of streptozotocin-induced diabetic rats. We conducted untargeted LC-QTOF-MS metabolic profiling on six groups of rats: three diabetic groups receiving either a placebo, R. officinalis, or G. abies-marina extracts, and three corresponding control groups. The metabolic analysis revealed significant alterations in the levels of various glycerophospholipids, sterol lipids, and fatty acyls. Both extracts influenced the metabolic profile, partially mitigating diabetes-induced changes. Notably, G. abies-marina extract had a more pronounced impact on the animals' metabolic profiles compared to R. officinalis. In conclusion, our findings suggest that environmentally clean extracts from R. officinalis and G. abies-marina possess nutraceutical potential, as they were able to modulate the metabolic profile in streptozotocin-induced diabetic rats. G. abies-marina extract exhibited a more substantial effect on metabolic alterations induced by diabetes compared to R. officinalis. These results warrant further exploration of these plant extracts for their potential in managing diabetes-related metabolic disturbances.

First insight about the ability of specific glycerophospholipids to discriminate non-small cell lung cancer subtypes.

Introduction: Discrimination between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) subtypes in non-small cell lung cancer (NSCLC) patients is a significant challenge in oncology. Lipidomics analysis provides a promising approach for this differentiation. Methods: In an accompanying paper, we explored oxPCs levels in a cohort of 200 NSCLC patients. In this research, we utilized liquid chromatography coupled with mass spectrometry (LC-MS) to analyze the lipidomics profile of matching tissue and plasma samples from 25 NSCLC patients, comprising 11 ADC and 14 SCC cases. This study builds upon our previous findings, which highlighted the elevation of oxidised phosphatidylcholines (oxPCs) in NSCLC patients. Results: We identified eight lipid biomarkers that effectively differentiate between ADC and SCC subtypes using an untargeted approach. Notably, we observed a significant increase in plasma LPA 20:4, LPA 18:1, and LPA 18:2 levels in the ADC group compared to the SCC group. Conversely, tumour PC 16:0/18:2, PC 16:0/4:0; CHO, and plasma PC 16:0/18:2; OH, PC 18:0/20:4; OH, PC 16:0/20:4; OOH levels were significantly higher in the ADC group. Discussion: Our study is the first to report that plasma LPA levels can distinguish between ADC and SCC patients in NSCLC, suggesting a potential role for LPAs in NSCLC subtyping. This finding warrants further investigation into the mechanisms underlying these differences and their clinical implications.

From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS.

Application of high-throughput technologies in metabolomics studies increases the quantity of data obtained, which in turn imposes several problems during data analysis. Correctly and clearly addressed biological question and comprehensive knowledge about data structure and properties are definitely necessary to select proper chemometric tools. However, there is a broad range of chemometric tools available for use with metabolomics data, which makes this choice challenging. Precisely performed data treatment enables valuable extraction of information and its proper interpretation. The effect of an error made at an early stage will be enhanced throughout the later stages, which in combination with other errors made at each step can accumulate and significantly affect the data interpretation. Moreover, adequate application of these tools may help not only to detect, but sometimes also to correct, biological, analytical, or methodological errors, which may affect truthfulness of obtained results. This report presents steps and tools used for LC-MS based metabolomics data extraction, reduction, and visualization. Following such steps as data reprocessing, data pretreatment, data treatment, and data revision, authors want to show how to extract valuable information and how to avoid misinterpretation of results obtained. The purpose of this work was to emphasize problematic characteristics of metabolomics data and the necessity for their attentive and precise treatment. The dataset used to illustrate metabolomics data properties and to illustrate major data treatment challenges was obtained utilizing an animal model of control and diabetic rats, both with and without rosemary treatment. Urine samples were fingerprinted employing LC-QTOF-MS.

Metabolomic profile of acute myeloid leukaemia parallels of prognosis and response to therapy.

The heterogeneity of acute myeloid leukemia (AML), a complex hematological malignancy, is caused by mutations in myeloid cells affecting their differentiation and proliferation. Thus, various cytogenetic alterations in AML cells may be characterized by a unique metabolome and require different treatment approaches. In this study, we performed untargeted metabolomics to assess metabolomics differences between AML patients and healthy controls, AML patients with different treatment outcomes, AML patients in different risk groups based on the 2017 European LeukemiaNet (ELN) recommendations for the diagnosis and management of AML, AML patients with and without FLT3-ITD mutation, and a comparison between patients with FLT3-ITD, CBF-AML (Core binding factor acute myelogenous leukemia), and MLL AML (mixed-lineage leukemia gene) in comparison to control subjects. Analyses were performed in serum samples using liquid chromatography coupled with mass spectrometry (LC-MS). The obtained metabolomics profiles exhibited many alterations in glycerophospholipid and sphingolipid metabolism and allowed us to propose biomarkers based on each of the above assessments as an aid for diagnosis and eventual classification, allowing physicians to choose the best-suited treatment approach. These results highlight the application of LC-MS-based metabolomics of serum samples as an aid in diagnostics and a potential minimally invasive prognostic tool for identifying various cytogenetic and treatment outcomes of AML.

Exploration of oxidized phosphocholine profile in non-small-cell lung cancer.

Introduction: Lung cancer is one of the most frequently studied types of cancer and represents the most common and lethal neoplasm. Our previous research on non-small cell lung cancer (NSCLC) has revealed deep lipid profile reprogramming and redox status disruption in cancer patients. Lung cell membranes are rich in phospholipids that are susceptible to oxidation, leading to the formation of bioactive oxidized phosphatidylcholines (oxPCs). Persistent and elevated levels of oxPCs have been shown to induce chronic inflammation, leading to detrimental effects. However, recent reports suggest that certain oxPCs possess anti-inflammatory, pro-survival, and endothelial barrier-protective properties. Thus, we aimed to measure the levels of oxPCs in NSCLC patients and investigate their potential role in lung cancer. Methods: To explore the oxPCs profiles in lung cancer, we performed in-depth, multi-level metabolomic analyses of nearly 350 plasma and lung tissue samples from 200 patients with NSCLC, including adenocarcinoma (ADC) and squamous cell carcinoma (SCC), the two most prevalent NSCLC subtypes and COPD patients as a control group. First, we performed oxPC profiling of plasma samples. Second, we analyzed tumor and non-cancerous lung tissues collected during the surgical removal of NSCLC tumors. Because of tumor tissue heterogeneity, subsequent analyses covered the surrounding healthy tissue and peripheral and central tumors. To assess whether the observed phenotypic changes in the patients were associated with measured oxPC levels, metabolomics data were augmented with data from medical records. Results: We observed a predominance of long-chain oxPCs in plasma samples and of short-chain oxPCs in tissue samples from patients with NSCLC. The highest concentration of oxPCs was observed in the central tumor region. ADC patients showed higher levels of oxPCs compared to the control group, than patients with SCC. Conclusion: The detrimental effects associated with the accumulation of short-chain oxPCs suggest that these molecules may have greater therapeutic utility than diagnostic value, especially given that elevated oxPC levels are a hallmark of multiple types of cancer.

Combination of LC-MS- and GC-MS-based metabolomics to study the effect of ozonated autohemotherapy on human blood.

Ozonated autohemotherapy (O3-AHT) is a medical approach during which blood obtained from the patient is ozonated and injected back into the body. Despite an increasing number of evidence that O3-AHT is safe, this type of therapy remains controversial. To extend knowledge about the changes in blood evoked by O3-AHT, LC-MS- and GC-MS-based metabolic fingerprinting was used to compare plasma samples obtained from blood before and after the treatment with potentially therapeutic concentrations of ozone. The procedure was performed in PVC bags utilized for blood storage to study also possible interactions between ozone and plastic. By use of GC-MS, an increase in lactic acid and pyruvic acid was observed, which indicated an increased rate of glycolysis. With LC-MS, changes in plasma antioxidants were observed. Moreover, concentrations of lipid oxidation products (LOP) and lysophospholipids were increased after ozone treatment. This is the first report of increased LOPs metabolites after ozonation of blood. Seven metabolites detected by LC-QTOF-MS only in ozonated samples could be considered as novel biomarkers of oxidative stress. Several plasticizers have been detected by both techniques in blood stored in PVC bags. PVC is known to be an ozone resistant material, but ozonation of blood in PVC bags stimulates leaching of plasticizers into the blood.

In-vial dual extraction for direct LC-MS analysis of plasma for comprehensive and highly reproducible metabolic fingerprinting.

Metabolic fingerprinting of biological tissues has become an important area of research, particularly in the biomarker discovery field. Methods have inherent analytical variation, and new approaches are necessary to ensure that the vast numbers of intact metabolites present in biofluids are detected. Here, we describe an in-vial dual extraction (IVDE) method and a direct injection method that shows the total number of features recovered to be over 4500 from a single 20 µL plasma aliquot. By applying a one-step extraction consisting of a lipophilic and hydrophilic layer within a single vial insert, we showed that analytical variation was decreased. This was achieved by reducing sample preparation stages including procedures of drying and transfers. The two phases in the vial, upper and lower, underwent HPLC-QTOF analysis on individually customized LC gradients in both positive and negative ionization modes. A 60 min lipid profiling HPLC-QTOF method for the lipophilic phase was specifically developed, enabling the separation and putative identification of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, and sterols. The aqueous phase of the extract underwent direct injection onto a 45 min gradient, enabling the detection of both polarities. The IVDE method was compared to two traditional extraction methods. The first method was a two-step ether evaporation and IPA resuspension, and the second method was a methanol precipitation typically used in fingerprinting studies. The IVDE provided a 378% increase in reproducible features when compared to evaporation and a 269% increase when compared to the precipitate and inject method. As a proof of concept, the method was applied to an animal model of diabetes. A 2-fold increase in discriminant metabolites was found when comparing diabetic and control rats with IVDE. These discriminant metabolites accounted for around 600 entities, out of which 388 were identified in available databases.