Mechanistic insight into glioma through spatially multidimensional proteomics.

Characterizing the tumor microenvironment at the molecular level is essential for understanding the mechanisms of tumorigenesis and evolution. However, the specificity of the blood proteome in localized region of the tumor and its linkages with other systems is difficult to investigate. Here, we propose a spatially multidimensional comparative proteomics strategy using glioma as an example. The blood proteome signature of tumor microenvironment was specifically identified by in situ collection of arterial and venous blood from the glioma region of the brain for comparison with peripheral blood. Also, by integrating with different dimensions of tissue and peripheral blood proteomics, the information on the genesis, migration, and exchange of glioma-associated proteins was revealed, which provided a powerful method for tumor mechanism research and biomarker discovery. The study recruited multidimensional clinical cohorts, allowing the proteomic results to corroborate each other, reliably revealing biological processes specific to gliomas, and identifying highly accurate biomarkers.

Stable-Isotope N-Me Aziridination Enables Accurate Quantitative C═C Isomeric Lipidomics.

Accurate lipid quantification is essential to revealing their roles in physiological and pathological processes. However, difficulties in the structural resolution of lipid isomers hinder their further accurate quantification. To address this challenge, we developed a novel stable-isotope N-Me aziridination strategy that enables simultaneous qualification and quantification of unsaturated lipid isomers. The one-step introduction of the 1-methylaziridine structure not only serves as an activating group for the C═C bond to facilitate positional identification but also as an isotopic inserter to achieve accurate relative quantification. The high performance of this reaction for the identification of unsaturated lipids was verified by large-scale resolution of the C═C positions of 468 lipids in serum. More importantly, by using this bifunctional duplex labeling method, various unsaturated lipids such as fatty acids, phospholipids, glycerides, and cholesterol ester were accurately and individually quantified at the C═C bond isomeric level during the mouse brain ischemia. This study provides a new approach to quantitative structural lipidomics.

Nontargeted metabolomics analysis of follicular fluid in patients with endometriosis provides a new direction for the study of oocyte quality.

Endometriosis is a common, estrogen-dependent chronic gynecological disease that endangers the reproductive system and systemic metabolism of patients. We aimed to investigate the differences in metabolic profiles in the follicular fluid between infertile patients with endometriosis and controls. A total of 25 infertile patients with endometriosis and 25 infertile controls who were similar in age, BMI, fertilization method and ovulation induction treatment were recruited in this study. Metabolomics analysis of follicular fluid was performed by two methods of high-performance liquid chromatography tandem mass spectrometry. There were 36 upregulated and 17 downregulated metabolites in the follicular fluid of patients in the endometriosis group. KEGG pathway analysis revealed that these metabolites were enriched in phenylalanine, tyrosine and tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, phenylalanine metabolism and pyrimidine metabolism pathways. A biomarker panel consisting of 20 metabolites was constructed by random forest, with an accuracy of 0.946 and an AUC of 0.988. This study characterizes differences in follicular fluid metabolites and associated pathway profiles in infertile patients with endometriosis. These findings can provide a better comprehensive understanding of the disease and a new direction for the study of oocyte quality, as well as potential metabolic markers for the prognosis of endometriosis.

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry.

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multidimensional Predictions.

Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is a promising technology. However, its full performance is restricted by the time-consuming building and limited coverage of a project-specific experimental library. Herein, we developed a versatile multifunctional deep learning model Deep4D based on self-attention that could predict the collisional cross section, retention time, fragment ion intensity, and charge state with high accuracies for both the unmodified and phosphorylated peptides and thus established the complete workflows for high-coverage 4D DIA proteomics and phosphoproteomics based on multidimensional predictions. A 4D predicted library containing ∼2 million peptides was established that could realize experimental library-free DIA analysis, and 33% more proteins were identified than using an experimental library of single-shot measurement in the example of HeLa cells. These results show the great values of the convenient high-coverage 4D DIA proteomics methods.

Metabonomic analysis of follicular fluid in patients with diminished ovarian reserve.

Background: Ovarian reserve is an important factor determining female reproductive potential. The number and quality of oocytes in patients with diminished ovarian reserve (DOR) are reduced, and even if in vitro fertilization-embryo transfer (IVF-ET) is used to assist their pregnancy, the clinical pregnancy rate and live birth rate are still low. Infertility caused by reduced ovarian reserve is still one of the most difficult clinical problems in the field of reproduction. Follicular fluid is the microenvironment for oocyte survival, and the metabolic characteristics of follicular fluid can be obtained by metabolomics technology. By analyzing the metabolic status of follicular fluid, we hope to find the metabolic factors that affect the quality of oocytes and find new diagnostic markers to provide clues for early detection and intervention of patients with DOR. Methods: In this research, 26 infertile women with DOR and 28 volunteers with normal ovarian reserve receiving IVF/ET were recruited, and their follicular fluid samples were collected for a nontargeted metabonomic study. The orthogonal partial least squares discriminant analysis model was used to understand the separation trend of the two groups, KEGG was used to analyze the possible metabolic pathways involved in differential metabolites, and the random forest algorithm was used to establish the diagnostic model. Results: 12 upregulated and 32 downregulated differential metabolites were detected by metabolic analysis, mainly including amino acids, indoles, nucleosides, organic acids, steroids, phospholipids, fatty acyls, and organic oxygen compounds. Through KEGG analysis, these metabolites were mainly involved in aminoacyl-tRNA biosynthesis, tryptophan metabolism, pantothenate and CoA biosynthesis, and purine metabolism. The AUC value of the diagnostic model based on the top 10 metabolites was 0.9936. Conclusion: The follicular fluid of patients with DOR shows unique metabolic characteristics. These data can provide us with rich biochemical information and a research basis for exploring the pathogenesis of DOR and predicting ovarian reserve function.

Genome-wide gene expression profiles of the pea aphid (Acyrthosiphon pisum) under cold temperatures provide insights into body color variation.

Cold temperatures are one of the factors influencing color polymorphisms in Acyrthosiphon pisum, resulting in a change from a red to greenish color. Here we characterized gene expression profiles of A. pisum under different low temperatures (1°C, 4°C, 8°C, and 14°C) and durations (3, 6, 12, and 24 h). The number of differentially expressed genes (DEGs) increased as temperatures decreased and time increased, but only a small number of significant DEGs were identified. Genes involved in pigment metabolism were downregulated. An interaction network analysis for 506 common DEGs in comparisons among aphids exposed to 1°C for four durations indicated that a cytochrome P450 gene (CYP, LOC112935894) significantly downregulated may interact with a carotenoid metabolism gene (LOC100574964), similar to other genes encoding CYP, lycopene dehydrogenase and fatty acid synthase. We proposed that the body color shift in A. pisum responding to low temperatures may be regulated by CYPs.