scRNA-seq reveals that origin recognition complex subunit 6 regulates mouse spermatogonial cell proliferation and apoptosis via activation of Wnt/β-catenin signaling

ABSTRACT

The regulation of spermatogonial proliferation and apoptosis is of great significance for maintaining spermatogenesis. The single-cell RNA sequencing (scRNA-seq) analysis of the testis was performed to identify genes upregulated in spermatogonia. Using scRNA-seq analysis, we identified the spermatogonia upregulated gene origin recognition complex subunit 6 (Orc6), which is involved in DNA replication and cell cycle regulation; its protein expression in the human and mouse testis was detected by western blot and immunofluorescence. To explore the potential function of Orc6 in spermatogonia, the C18-4 cell line was transfected with control or Orc6 siRNA. Subsequently, 5-ethynyl-2-deoxyuridine (EdU) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assays, flow cytometry, and western blot were used to evaluate its effects on proliferation and apoptosis. It was revealed that ORC6 could promote proliferation and inhibit apoptosis of C18-4 cells. Bulk RNA sequencing and bioinformatics analysis indicated that Orc6 was involved in the activation of wingless/integrated (Wnt)/ β-catenin signaling. Western blot revealed that the expression of β-catenin protein and its phosphorylation (Ser675) were significantly decreased when silencing the expression of ORC6. Our findings indicated that Orc6 was upregulated in spermatogonia, whereby it regulated proliferation and apoptosis by activating Wnt/β-catenin signaling.

Stratified Test Accurately Identifies Differentially Expressed Genes Under Batch Effects in Single-Cell Data

Analyzing single-cell sequencing data from large cohorts is challenging. Discrepancies across experiments and differences among participants often lead to omissions and false discoveries in differentially expressed genes. We find that the Van Elteren test, a stratified version of the widely used Wilcoxon rank-sum test, elegantly mitigates the problem. We also modified the common language effect size to supplement this test, further improving its utility. On both simulated and real patient data we show the ability of Van Elteren test to control for false positives and false negatives. A comprehensive assessment using receiver operating characteristic (ROC) curve shows that Van Elteren test achieves higher sensitivity and specificity on simulated datasets, compared with nine state-of-the-art differential expression analysis methods. The effect size also estimates the differences between cell types more accurately.