Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.

Delivery of therapeutic agents into the brain is a major challenge in central nervous system drug development. The blood–brain barrier (BBB) prevents access of biotherapeutics to their targets in the central nervous system and, therefore, prohibits the effective treatment of many neurological disorders. To find blood–brain barrier shuttle peptides that could target therapeutics to the brain, we applied a phage display technology on a primary endothelial rat cellular model. Two identified peptides from a 12 mer phage library, GLHTSATNLYLH and VAARTGEIYVPW, were selected and their permeability was validated using the in vitro BBB model. The permeability of peptides through the BBB was measured by ultra-performance liquid chromatography-tandem mass spectrometry coupled to a triple-quadrupole mass spectrometer (UHPLC-MS/MS). We showed higher permeability for both peptides compared to N–C reversed-sequence peptides through in vitro BBB: for peptide GLHTSATNLYLH 3.3 × 10⁻⁷ cm/s and for peptide VAARTGEIYVPW 1.5 × 10⁻⁶ cm/s. The results indicate that the peptides identified by the in vitro phage display technology could serve as transporters for the administration of biopharmaceuticals into the brain. Our results also demonstrated the importance of proper BBB model for the discovery of shuttle peptides through phage display libraries.

Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and visualize statistically significant trends and biologically relevant differences. Besides tailored methods developed by individual labs, a solid core of freely accessible tools exists for exploratory data analysis and visualization, which we have compiled here, including preparation of descriptive statistics, annotated box plots, hypothesis testing, volcano plots, lipid maps and fatty acyl chain plots, unsupervised and supervised dimensionality reduction, dendrograms, and heat maps. This review is intended for those who would like to develop their skills in data analysis and visualization using freely available R or Python solutions. Beginners are guided through a selection of R and Python libraries for producing publication-ready graphics without being overwhelmed by the code complexity. This manuscript, along with associated GitBook code repository containing step-by-step instructions, offers readers a comprehensive guide, encouraging the application of R and Python for robust and reproducible chemometric analysis of omics data.

Urine represents a convenient biofluid for metabolomic studies due to its noninvasive collection and richness in metabolites. Here, amino acids are valuable biomarkers for their ability to reflect imbalances of different biochemical pathways. An impact of amino acids on pathology, prognosis and therapy of various diseases, including inflammatory bowel disease (IBD), is therefore the subject of current clinical research. This work is aimed to develop a capillary electrophoresis-tandem mass spectrometry (CE-MS/MS) method for the quantification of the 20 proteinogenic amino acids in human urine samples obtained from patients suffering from IBD and treated with thiopurines. The optimized CE-MS/MS method, with minimum sample preparation (just “dilute and shoot”), exhibited excellent linearity for all the analytes (coefficients of determination were higher than 0.99), with inter-day and intra-day precision yielding relative standard deviations in the range of 0.91–15.12% and with accuracy yielding relative errors in the range of 85.47–112.46%. Total analysis time, an important parameter for the sample throughput demanded in routine practice, was shorter in ca. 17% when compared to established CE-MS methods. Favorable performance of the proposed CE-MS/MS method was also confirmed by the comparison with corresponding ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) method. Consistent data for the investigated amino acid metabolome were obtained using both methods. For the first time, the amino acid profiling by CE-MS approach was applied on the clinical IBD samples. Here, significant differences observed in the concentration levels of some amino acids between IBD patients undergoing thiopurine treatment and healthy volunteers could result from the simultaneous action of the disease and the corresponding therapy. These findings indicate that amino acids analysis could be a valuable tool for the study of mechanism of the IBD treatment by thiopurines.

Autism spectrum disorder is a heterogeneous neurodevelopmental disease. Currently, no biomarker of this disease is known. Diagnosis is performed through observation, standardized behavioral scales, and interviews with parents. In practice, diagnosis is often delayed to the average age of four years or even more which adversely affects a child’s perspective. A laboratory method allowing to detect the disorder at earlier stages is of a great need, as this could help the patients to start with treatment at a younger age, even prior to the clinical diagnosis. Recent evidence indicates that metabolomic markers should be considered as diagnostic markers, also serving for further differentiation and characterization of different subgroups of the autism spectrum. In this study, we developed an ultra-high performance liquid chromatography-tandem triple quadrupole mass spectrometry method for the simultaneous determination of six metabolites in human urine. These metabolites, namely methylguanidine, N-acetyl arginine, inosine, indole-3-acetic acid, indoxyl sulfate and xanthurenic acid were selected as potential biomarkers according to prior metabolomic studies. The analysis was carried out by means of reversed-phase liquid chromatography with gradient elution. Separation of the metabolites was performed on a Phenomenex Luna^® Omega Polar C18 (100 × 1.0 mm, 1.6 µm) column at a flow rate of 0.15 mL/min with acetonitrile/water 0.1% formic acid aqueous as the mobile phase. The analysis was performed on a group of children with autism spectrum disorder and age-matched controls. In school children, we have detected disturbances in the levels of oxidative stress markers connected to arginine and purine metabolism, namely methylguanidine and N-acetylargine. Also, products of gut bacteria metabolism, namely indoxyl sulfate and indole-3-acetic acid, were found to be elevated in the patients’ group. We can conclude that this newly developed method is fast, sensitive, reliable, and well suited for the quantification of proposed markers.

In the presented study, a capillary electrophoresis-mass spectrometry method combining high separation efficiency and sensitive detection has been developed and validated, for the first time, to quantify branched chain amino acids (valine, isoleucine, leucine) in commercial food and sport supplement samples and human plasma samples. The separations were performed in a bare fused silica capillary. The background electrolyte was composed of 500 mM formic acid with pH 2.0. The plasma sample pretreatment was realized by simple protein precipitation with acetonitrile. Injection of a short zone of highly basic electrolyte before the sample injection and application of the negative pressure on the separation were accompanied by enhanced resolution of the isobaric amino acids—isoleucine and leucine. The developed method was characterized by favorable validation parameters, such as linearity (r² > 0.99), accuracy and precision, the limit of detection, lower limit of quantification, or robustness. These parameters were more than sufficient for the quantification of branched chain amino acids in various samples. The determined concentrations of branched chain amino acids in food and sports supplements were in very good agreement with the content declared by the manufacturer. The investigated concentrations of branched chain amino acids were in the range 294.68–359.24 µM for valine, 91.76–95.67 µM for isoleucine, and 196.78–251.24 µM for leucine. These concentrations fall within the physiological limits. The developed CE-MS/MS method represents a suitable alternative to traditional approaches used in branched chain amino acid quality control and bioanalysis.