Multiplatform Analytical Methodology for Metabolic Fingerprinting of Lung Tissue

Using multiplatform approaches providing wider information about the metabolome, is currently the main topic in the area of metabolomics, choosing from liquid chromatography–mass spectrometry (LC–MS), gas chromatography/mass spectrometry (GC/MS), capillary electrophoresis–mass spectrometry (CE–MS), and nuclear magnetic resonace (NMR). However, the reliability and suitability of sample treatment, data acquisition, data preprocessing, and data analysis are prerequisites for correct biological interpretation in metabolomics studies. The significance of differences between samples can only be determined when the performance characteristics of the entire method are known. This leads to performing method validation in order to assess the performance and the fitness-for-purpose of a method or analytical system for metabolomics research. The present study was designed for developing a nontargeted global fingerprinting approach of lung tissue, for the first time, applying multiple complementary analytical techniques (LC–MS, GC/MS, and CE–MS) with regards to analytical method optimization (sample treatment + analytical method), characterization, and validation as well as application to real samples. An initial solvent for homogenization has been optimized, which is usually overseen in the tissue homogenization protocol. A nontargeted fingerprinting approach was applied to a pooled sample of lung tissue using these three instruments to cover a wider range of metabolites. The linearity of the validated method for all metabolites was >0.99, with good recovery and precision in all techniques. The method has been successfully applied to lung samples from rats with sepsis compared to the control samples. Only 20 mg of tissue is required for the three analytical techniques, where only one metabolite was found in common between LC–MS and CE–MS analysis as statistically significant. This proves the importance of applying a multiplatform approach in a metabolomics study as well as for biomarker discovery

LC–MS-Based Metabolomics Identification of Novel Biomarkers of Chorioamnionitis and Its Associated Perinatal Neurological Damage

Chorioamnionitis is a complication of pregnancy associated with significant maternal and perinatal long-term adverse outcomes. We apply high-throughput amniotic fluid (AF) metabolomics analysis for better understanding the pathophysiological mechanism of chorioamnionitis and its associated perinatal neurological injury and to provide meaningful information about new potential biomarkers. AF samples (<i>n</i> = 40) were collected from women at risk of chorioamnionits. Detailed clinical information on each pregnancy was obtained from obstetrical and neonatal medical examination. Liquid chromatography (LC)/mass spectrometry (MS) followed by data alignment and filtration as well as univariate and multivariate statistical analysis was performed. Statistically significant differences were found in 60 masses in positive and 115 in negative ionization mode obtained with LC/quadrupole time-of-flight MS (LC–QTOF-MS) between women with and without chorioamnionitis. Identified compounds were mainly related to glycerophospholipids and sphingolipids metabolism. From them, LPE(16:0)/LPE­(P-16:0) and especially lactosylceramides emerged as the best biomarker candidates. Sulfocholic acid, trioxocholenoic acids, and LPC(18:2) were particularly increased in women with chorioamnionitis whose newborns developed perinatal brain damage. Therefore, we propose LPE(16:0)/LPE­(P-16:0) and lactosylceramides as biomarkers for chorioamnionitis as well as LPC(18:2), trioxocholenoic acid, and sulfocholic acid for its associated perinatal brain damage. Metabolomics fingerprinting of AF enables the prediction of pregnancy-related disorders and the development of new diagnostics strategies

Characteristics of study participants.

<p>COPD – chronic obstructive pulmonary disease.</p><p>ILT – intraluminal thrombus.</p

PLS-DA plot of plasma metabolic profiles obtained for patients and controls with prediction for QCs.

<p>▵ - small AAA, ▴ – large AAA, □ – control, + - Quality control Panel A shows PLS-DA model (R² = 0.852, Q² = 0.369) for all samples and all variables in three groups under investigation (A, S, and C). Panel B shows prediction for QC samples by the model obtained.</p

Identification of metabolites that were significantly differentiating plasma profiles of AAA patients from controls.

<p>A vs C - (+)/(−) means increased/decreased abundance in large aneurysm group in comparison to controls, S vs C - (+)/(−) means increased/decreased abundance in small aneurysm group in comparison to controls, A vs S - (+)/(−) means increased/decreased abundance in large aneurysm group in comparison to small aneurysm group. Identity of metabolites marked with asterix (*) was confirmed by analyzis of the standard.</p

PLS-DA plot of plasma metabolic profiles obtained for age matched aneurysm patients and controls.

<p>▵ - small AAA, ▴ – large AAA, + - predicted AAA samples not matching in age Panel A shows PLS-DA model (R² = 0.835, Q² = 0.335) for plasma samples obtained from patients and controls matching in age (n = 11). Panel B shows prediction for additional samples of patients (4 with large and 4 with small AAA). Sample marked by the circle was obtained from the patient with AAA size of 5.4 cm, and was assigned to large AAA group because the patient was operated on.</p

Identification of acylcarnitines that were significantly differentiating plasma profiles of AAA patients from controls.

<p>A vs C - (+)/(−) means increased/decreased abundance in large aneurysm group in comparison to controls, S vs C - (+)/(−) means increased/decreased abundance in small aneurysm group in comparison to controls, A vs S - (+)/(−) means increased/decreased abundance in large aneurysm group in comparison to small aneurysm group.</p

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

Identification of lysophospholipids that were significantly differentiating plasma profiles of AAA patients from controls.

<p>A vs C - (+)/(−) means increased/decreased abundance in large aneurysm group in comparison to controls, S vs C - (+)/(−) means increased/decreased abundance in small aneurysm group in comparison to controls, A vs S - (+)/(−) means increased/decreased abundance in large aneurysm group in comparison to small aneurysm group.</p

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