Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data.

This letter is devoted to the application of machine learning, namely, convolutional neural networks to solve problems in the initial steps of the common pipeline for data analysis in metabolomics. These steps are the peak detection and the peak integration in raw liquid chromatography-mass spectrometry (LC-MS) data. Widely used algorithms suffer from rather poor precision for these tasks, yielding many false positive signals. In the present work, we developed an algorithm named peakonly, which has high flexibility for the detection or exclusion of low-intensity noisy peaks, and shows excellent quality in the detection of true positive peaks, approaching the highest possible precision. The current approach was developed for the analysis of high-resolution LC-MS data for the purposes of metabolomics, but potentially it can be applied with several adaptations in other fields, which utilize high-resolution GC- or LC-MS techniques. Peakonly is freely available on GitHub ( https://github.com/arseha/peakonly ) under an MIT license.

Post-mortem changes in metabolomic profiles of human serum, aqueous humor and vitreous humor.

INTRODUCTION: Application of metabolomic methods to forensic studies may expand the limits of the post-mortem interval (PMI) estimation, and improve the accuracy of the estimation. To this end, it is important to determine which tissue is the most suitable for analysis, and which compounds are the most promising candidates for PMI estimation. OBJECTIVES: This work is aimed at the comparison of human serum, aqueous humor (AH), and vitreous humor (VH) as perspective tissues for metabolomic-based PMI estimation, at the determination of most promising PMI biomarkers, and at the development of method of PMI estimation based on the measurement of concentrations of PMI biomarkers. METHODS: Quantitative metabolomic profiling of samples of the human serum, AH, and VH taken at different PMIs has been performed with the use of NMR spectroscopy. RESULTS: It is found that the metabolomic changes in anatomically isolated ocular fluids are slower and smoother than that in blood. A good positive time correlation (Pearson coefficient r > 0.5) was observed for several metabolites, including hypoxanthine, choline, creatine, betaine, glutamate, and glycine. A model for PMI estimation based on concentrations of several metabolites in AH and VH is proposed. CONCLUSIONS: The obtained results demonstrate that the metabolomic analysis of AH and VH is more suitable for the PMI estimation than that of serum. The compounds with good positive time correlation can be considered as potential PMI biomarkers.

Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data.

The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.

Comparative Metabolomic Profiling of Rat Embryonic and Induced Pluripotent Stem Cells.

Metabolomic profiles of somatic cells, embryonic stem cells (ESCs), and induced pluripotent stem cells (iPSCs) reflect their metabolic phenotypes. The comparative study of metabolomes of these cells is important for understanding the differences in metabolism between somatic and pluripotent cells, and also the possible differences between ESCs and iPSCs. Here, we performed for the first time the metabolomic analysis of rat ESCs, iPSCs, and embryonic fibroblasts (EFs) at both quantitative and semi-quantitative levels using NMR spectroscopy and liquid chromatography with mass spectrometric detection, respectively. The total of 106 metabolites has been identified, and the concentrations of 51 compounds have been measured. It is found that the reprogramming of rat EFs into iPSCs affects virtually all metabolic pathways and causes drastic changes in the cell metabolomic profile. The difference between ESCs and iPSCs is much less pronounced: the concentrations of the majority of metabolites in ESCs and iPSCs are similar, and significant differences were observed for only several compounds, including adenosine, cysteic acid, glycerophosphoglycerol, inositol phosphate, glucose, myo-inositol, phosphoserine, xanthosine, guanosine. The observed differences between the metabolomic compositions of ESCs and iPSCs do not influence the pluripotent ability of iPSCs. Graphical Abstract.

Seasonal Variations and Interspecific Differences in Metabolomes of Freshwater Fish Tissues: Quantitative Metabolomic Profiles of Lenses and Gills.

This work represents the first comprehensive report on quantitative metabolomic composition of tissues of pike-perch (Sander lucioperca) and Siberian roach (Rutilus rutilus lacustris). The total of 68 most abundant metabolites are identified and quantified in the fish lenses and gills by the combination of LC-MS and NMR. It is shown that the concentrations of some compounds in the lens are much higher than that in the gills; that indicates the importance of these metabolites for the adaptation to the specific living conditions and maintaining the homeostasis of the fish lens. The lens metabolome undergoes significant seasonal changes due to the variations of dissolved oxygen level and fish feeding activity. The most season-affected metabolites are osmolytes and antioxidants, and the most affected metabolic pathway is the histidine pathway. In late autumn, the major lens osmolytes are N-acetyl-histidine and threonine phosphoethanolamine (Thr-PETA), while in winter the highest concentrations were observed for serine phosphoethanolamine (Ser-PETA) and myo-inositol. The presence of Thr-PETA and Ser-PETA in fish tissues and their role in cell osmotic protection are reported for the first time. The obtained concentrations can be used as baseline levels for studying the influence of environmental factors on fish health.