Zhesheng Zhou

The cell membrane system comprises the plasma membrane, endoplasmic reticulum, Golgi apparatus, lysosome, mitochondria, and nuclear membrane, which are essential for maintaining normal physiological functions of cells. The proteins associated with these membrane-organelles are frequently modified to regulate their functions, the most common of which is ubiquitin modification. So far, many ubiquitin E3 ligases anchored in the membrane system have been identified as critical players facilitating intracellular biofunctions whose dysfunction is highly related to cancer. In this review, we summarized membrane-associated E3 ligases and revealed their relationship with cancer, which is of great significance for discovering novel drug targets of cancer and may open up new avenues for inducing ubiquitination-mediated degradation of cancer-associated membrane proteins via small chemicals such as PROTAC and molecular glue.

Integrating single-cell datasets from multiple studies provides a cost-effective way to build comprehensive cell atlases, granting deeper insights into cellular characteristics across diverse biological systems. However, current data integration methods struggle with interference in partially overlapping datasets and varying annotation granularities. Here, a multiselective adversarial network is introduced for the first time and present UniMap, which functions as a "discerner" to identify and exclude interfering cells from various data sources during dataset integration. Compared to other state-of-the-art methods, UniMap emphasizes type-level integration and proves to be the best model for preserving biological variability, achieving noticeably higher accuracy in single-cell automated annotation under various circumstances. Additionally, it enhances interpretability by revealing shared and domain-specific cell types and providing prediction confidence. The efficacy of UniMap is demonstrated in terms of identifying new cell types, creating high-resolution cell atlases, annotating cells along developmental trajectories, and performing cross-species analysis, underscoring its potential as a robust tool for single-cell research.