Anxiety and depression significantly contribute to the overall burden of mental disorders, with depression being one of the leading causes of disability. Despite this, no biochemical test has been implemented for the diagnosis of these mental disorders, while recent studies have highlighted lipids as potential biomarkers. Using a streamlined high-throughput lipidome analysis method, direct-infusion mass spectrometry, we evaluated blood plasma lipid levels in 604 individuals from a general urban population and analysed their association with self-reported anxiety and depression symptoms. We also assessed lipidome profiles in 32 patients with clinical depression, matched to 21 healthy controls. We found a significant correlation between lipid abundances and the severity of self-reported depression symptoms. Moreover, lipid alterations detected in high scoring volunteers mirrored the lipidome profiles identified in patients with clinical depression included in our study. Based on these findings, we developed a lipid-based predictive model distinguishing individuals reporting severe depressive symptoms from non-depressed subjects with high accuracy. This study demonstrates the possibility of generalizing lipid alterations from a clinical cohort to the general population and underscores the potential of lipid-based biomarkers in assessing depressive states. This study was sponsored by the Moscow Center for Innovative Technologies in Healthcare, №2707-2, №2102-11.

Psychotic and affective disorders, including schizophrenia (SCZ) and depression (MDD), affect millions of people globally. The overlapping symptoms of these diseases and the lack of objective diagnostic tools could lead to misdiagnosis. Recent studies suggest that the analysis of plasma lipid levels may help to develop new diagnostic tools. In this study, we investigated the plasma lipidome of psychiatric patients and healthy controls to identify disease-specific lipid species. Using untargeted mass spectrometry, we profiled blood plasma lipids from 416 patients with common psychotic and affective disorders and 272 healthy individuals from two different cohorts. We observed lipidome alterations in SCZ and MDD consistent with earlier findings. In total, 144 lipids showed significant changes, with 107 of them being concordant across both disorders, and 37 being discordant. Lipids that differentiated SCZ from MDD were mainly triacylglycerols with polyunsaturated fatty acid residues decreased in MDD. In an additional group of 111 patients with bipolar, schizotypal, and schizoaffective disorders, these lipid markers suggested a trend toward separating psychotic and affective disorders. Furthermore, a logistic regression model trained on lipid data distinguished SCZ from MDD with an ROC AUC of 0.83. Taken together, these results suggest that blood lipid profiling may aid in the objective differentiation of psychotic and affective disorders.

Microscopic green algae are active producers of beneficial compounds, particularly those containing nitrogen. However, the metabolism of nitrogen-containing compounds is diverse and depends on the conditions of the nitrogen source. As a result, the approach to studying the metabolism of nitrogen-containing compounds becomes more complicated. This work demonstrates the metabolic changes in the high-productive green algae Neochlorella semenenkoi IPPAS C-1210 under conditions of nitrogen starvation and subsequent reintake, using high-performance liquid chromatography–mass spectrometry (HPLC–MS) with ¹⁵N isotopic labeling. The presented results include semi-quantitative chromatography–mass spectrometric analysis for 17 amino acids, a metabolomic profile of over 40 isotopically labeled compounds, an assessment of metabolic flux via isotopic incorporation, and an analysis of cellular lipid composition under varying growth conditions. The findings indicate that this strain can utilize ammonium acetate as a nitrogen source, consuming nitrogen in the ammonium form. The degree of isotopic labeling in compounds often diverged significantly from their quantitative changes (concentrations and chromatographic peak areas), suggesting that isotopic analysis may offer advantages over purely quantitative analysis for biological systems. Furthermore, in vivo biological isotopic labeling is shown to assist in identifying compounds absent from standard mass spectrometric databases.