Epigenetic regulation, particularly post-translational modifications (PTMs) of histones, participates in spermatogonial stem cell (SSCs) differentiation. However, there is a lack of systemic studies of histone PTM regulation during the differentiation of SSCs due to its low number in vivo. Herein, we quantified dynamic changes of 46 different PTMs on histone H3.1 by targeted quantitative proteomics using mass spectrometry during SSCs differentiation in vitro, in combination with our RNA-seq data. We identified seven histone H3.1 modifications to be differentially regulated. In addition, we selected H3K9me2 and H3S10ph for subsequent biotinylated peptide pull-down experiments and identified 38 H3K9me2-binding proteins and 42 H3S10ph-binding proteins, which contain several transcription factors, such as GTF2E2 and SUPT5H, which appear to be crucial for epigenetic regulation of SSC differentiation.

Protein subcellular localization (PSL) is very important in order to understand its functions, and its movement between subcellular niches within cells plays fundamental roles in biological process regulation. Mass spectrometry–based spatio-temporal proteomics technologies can help provide new insights of protein translocation, but bring the challenge in identifying reliable protein translocation events due to the noise interference and insufficient data mining. We propose a semi-supervised graph convolution network (GCN)–based framework termed TransGCN that infers protein translocation events from spatio-temporal proteomics. Based on expanded multiple distance features and joint graph representations of proteins, TransGCN utilizes the semi-supervised GCN to enable effective knowledge transfer from proteins with known PSLs for predicting protein localization and translocation. Our results demonstrate that TransGCN outperforms current state-of-the-art methods in identifying protein translocations, especially in coping with batch effects. It also exhibited excellent predictive accuracy in PSL prediction. TransGCN is freely available on GitHub at https://github.com/XuejiangGuo/TransGCN.