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Transcriptionally and translationally silent sperm undergo functional maturation during epididymis traverse, which provides sperm ability to move and is crucial for successful fertilization. However, the molecular mechanisms governing sperm maturation remain poorly understood, especially at the protein post-translational modification level. In this study, we conducted a comprehensive quantitative phosphoproteomic analysis of mouse epididymal sperm from different regions (caput, corpus, and cauda) to unveil the dynamics of protein phosphorylation during sperm maturation. We identified 6447 phosphorylation sites in 1407 phosphoproteins, and 345 phosphoproteins were differentially phosphorylated between caput and cauda sperm. Gene ontology and KEGG pathway analyses showed enrichment of differentially phosphorylated proteins in energy metabolism, sperm motility, and fertilization. Kinase substrate network analysis followed by inhibition assay and quantitative phosphoproteomics analysis showed that TSSK2 kinase is important for sperm motility and progressive motility. This study systemically characterized the intricate phosphorylation regulation during sperm maturation in the mouse epididymis, which can be a basis to elucidate sperm motility acquisition, and to offer potential targets for male contraception and the treatment of male infertility., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

N-glycosylation is one of the most universal and complex protein post-translational modifications (PTMs), and it is involved in many physiological and pathological activities. Owing to the low abundance of N-glycoproteins, enrichment of N-glycopeptides for mass spectrometry analysis usually requires a large amount of peptides. Additionally, oocyte protein N-glycosylation has not been systemically characterized due to the limited sample amount. Here, we developed a glycosylation enrichment method based on lectin and a single-pot, solid-phase-enhanced sample preparation (SP3) technology, termed lectin-based SP3 technology (LectinSP3). LectinSP3 immobilized lectin on the SP3 beads for N-glycopeptide enrichment. It could identify over 1100 N-glycosylation sites and 600 N-glycoproteins from 10 μg of mouse testis peptides. Furthermore, using the LectinSP3 method, we characterized the N-glycoproteome of 1000 mouse oocytes in three replicates and identified a total of 363 N-glycosylation sites from 215 N-glycoproteins. Bioinformatics analysis revealed that these oocyte N-glycoproteins were mainly enriched in cell adhesion, fertilization, and sperm-egg recognition. Overall, the LectinSP3 method has all procedures performed in one tube, using magnetic beads. It is suitable for analysis of a low amount of samples and is expected to be easily adaptable for automation. In addition, our mouse oocyte protein N-glycosylation profiling could help further characterize the regulation of oocyte functions.

Tubulin-based microtubule is a core component of flagella axoneme and essential for sperm motility and male fertility. Structural components of the axoneme have been well explored. However, how tubulin folding is regulated in sperm flagella formation is still largely unknown. Here, we report a germ cell-specific co-factor of CCT complex, STYXL1. Deletion of Styxl1 results in male infertility and microtubule defects of sperm flagella. Proteomic analysis of Styxl1 -/- sperm reveals abnormal downregulation of flagella-related proteins including tubulins. The N-terminal rhodanese-like domain of STYXL1 is important for its interactions with CCT complex subunits, CCT1, CCT6 and CCT7. Styxl1 deletion leads to defects in CCT complex assembly and tubulin polymerization. Collectively, our findings reveal the vital roles of germ cell-specific STYXL1 in CCT-facilitated tubulin folding and sperm flagella development., (© 2024. The Author(s).)

Oocyte maturation is pertinent to the success of in vitro maturation (IVM), which is used to overcome female infertility, and produced over 5000 live births worldwide. However, the quality of human IVM oocytes has not been investigated at single-cell proteome level. Here, we quantified 2094 proteins in human oocytes during in vitro and in vivo maturation (IVO) by single-cell proteomic analysis and identified 176 differential proteins between IVO and germinal vesicle oocytes and 45 between IVM and IVO oocytes including maternal effect proteins, with potential contribution to the clinically observed decreased fertilization, implantation, and birth rates using human IVM oocytes. IVM and IVO oocytes showed separate clusters in principal component analysis, with higher inter-cell variability among IVM oocytes, and have little correlation between mRNA and protein changes during maturation. The patients with the most aberrantly expressed proteins in IVM oocytes had the lowest level of estradiol per mature follicle on trigger day. Our data provide a rich resource to evaluate effect of IVM on oocyte quality and study mechanism of oocyte maturation., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest with the contents of this article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target–decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics. [ABSTRACT FROM AUTHOR]