Elucidating the Underlying Reactivities of Alternating Current Electrosynthesis by Time-Resolved Mapping of Short-Lived Reactive Intermediates.

Alternating current (AC) electrolysis is an emerging field in synthetic chemistry, however its mechanistic studies are challenged by the effective characterization of the elusive intermediate processes. Herein, we develop an operando electrochemical mass spectrometry platform that allows time-resolved mapping of stepwise electrosynthetic reactive intermediates in both direct current and alternating current modes. By dissecting the key intermediate processes of electrochemical functionalization of arylamines, the unique reactivities of AC electrosynthesis, including minimizing the over-oxidation/reduction through the inverse process, and enabling effective reaction of short-lived intermediates generated by oxidation and reduction in paired electrolysis, were evidenced and verified.

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.

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.

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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.