Chiral metabolomics is starting to become a well-defined research field, powered by the recent advances in separation techniques. This review aimed to cover the most relevant advances in indirect enantioseparations of endogenous metabolites that were published over the last 10 years, including improvements and development of new chiral derivatizing agents, along with advances in separation methodologies. Moreover, special emphasis is put on exciting advances in separation techniques combined with mass spectrometry, such as chiral discrimination by ion-mobility mass spectrometry together with untargeted strategies for profiling of chiral metabolites in complex matrices. These advances signify a leap in chiral metabolomics technologies that will surely offer a solid base to better understand the specific roles of enantiomeric metabolites in systems biology.

Background: Gastric cancer ranks fifth among the most prevalent malignancies, with poor prognosis due to limited early-stage diagnosis. Metabolic reprogramming plays a central role in GC development, sustaining carcinogenic processes. Methods: In this study, flow-injection tandem mass spectrometry was used to analyse plasma amino acids and acylcarnitines in 62 gastric cancer patients and 70 healthy individuals. Metabolic profiles were correlated with clinical parameters, tumour histology, and recurrence. Results: Gastric cancer patients showed significantly reduced levels of Trp, Arg, Tyr, Met, and sum of aromatic AAs—metabolites usually implicated in supporting tumour cell growth and proliferation. At the same time, elevated unsaturated, hydroxylated, and dicarboxylic acylcarnitines suggest mitochondrial and peroxisomal dysfunction. Marked metabolic heterogeneity was observed across histological subtypes, with the indeterminate subtype exhibiting the most pronounced disruption in fatty acid oxidation and widespread acylcarnitine alterations. Decreased levels of C6DC-carnitine and Cit synthesis were correlated with higher tumour recurrence, warranting further confirmatory investigations. Conclusions: These findings underscore the value of plasma profiling of amino acids and acylcarnitines for understanding gastric cancer biology, revealing distinct metabolic adaptations reflecting tumour biology, histological subtype, and treatment response.

Probing chirality, one of the fundamental properties of all biological systems, remains analytically challenging and often requires specialized analytical setups or reagents, limited to few laboratories. This study aimed to develop an efficient liquid chromatography coupled to ion-mobility mass spectrometry (LC-IM-MS) method for the simultaneous analysis of all proteinogenic d-amino acids (d-AAs) in human plasma, using exclusively commercially available reagents. An indirect enantioseparation strategy was developed based on derivatization with (S)-N-(4-nitrophenoxycarbonyl) phenylalanine methoxyethyl ester ((S)-NIFE), with five stationary phases evaluated for diastereomer separation capability. A design of experiments approach was employed for further optimization, achieving baseline separation of all proteinogenic AA diastereomers within 20 min on a phenyl-based stationary phase. A straightforward sample preparation protocol was characterized, then the entire workflow was used for the assessment of d-AAs alterations across 60 plasma samples obtained from patients with chronic kidney disease (CKD, stages 4 and 5) and healthy controls. The analysis revealed increased levels of d-Pro, d-Arg, d-Ser, d-Asn, d-Gln, and d-Ala in CKD samples, highlighting the effectiveness of the developed analytical workflow to detect disease-related changes in plasmatic d-AAs.

Background/Objectives: Chronic kidney disease (CKD) is a major global public health problem, and conventional biomarkers such as serum creatinine primarily reflect excretory function with limited sensitivity for early detection and for predicting progression. Amino acids (AAs) and acylcarnitines (ACs) reflect metabolic processes related to nitrogen metabolism and mitochondrial fatty-acid oxidation that are influenced by renal function. This study aimed to characterize CKD-associated alterations in AAs and ACs profiles using an integrated analytical framework combining differential analysis, supervised multivariate modeling, differential network enrichment analysis (DNEA), and cross-compartment plasma–urine profiling, moving beyond individual metabolite associations toward a multi-level characterization of CKD-associated metabolic reprogramming. Methods: Plasma and urine samples from 78 patients with CKD and plasma samples from 70 healthy controls were analyzed using flow-injection tandem mass spectrometry (FIA-MS/MS). An integrated targeted metabolomics framework combining differential, multivariate and network-based analyses including DNEA was applied to plasma and paired urine samples to characterize systemic and urinary metabolic alterations in CKD, focusing on AAs and ACs. Results: CKD was characterized by significant elevated short-chain dicarboxylic acylcarnitines, increased methylhistidine (MetHis) and argininosuccinic acid (ASA), together with reduced tryptophan, serine, methionine, and tyrosine in plasma. These metabolites were consistently identified across different analyses and correlated with kidney function markers. DNEA revealed coherent network-level reorganization, with acylcarnitine pathways gaining connectivity and centrality while amino acid modules lost integration in CKD. Cross-compartment analysis identified both systemic and compartment-specific patterns of metabolite distribution. An exploratory clustering-guided biomarker panel combining MetHis, C3DC, and Trp achieved an area under the curve (AUC) of 0.881 for discriminating patients with CKD from controls. Moreover, C6, C8, and C10 remained significantly associated with CKD after additional adjustment for estimated glomerular filtration rate (eGFR). Conclusions: Targeted metabolomic profiling revealed a coordinated metabolic signature in CKD suggesting disturbances in pathways related to fatty-acid oxidation, nitrogen imbalance, and altered amino acid metabolism. Network-level analysis provided evidence of systemic metabolic reorganization beyond individual metabolite changes. As findings derive from an observational cohort with high comorbidity prevalence, the identified signatures should be considered CKD-associated rather than CKD-specific.