An ultra-rapid 3D immunostaining protocol can uniformly label organ-scale tissues.

Multiple computational methods have been developed to understand how mutations drive tumourigenesis, but their insights are largely non-overlapping. Here, the authors introduce NetFlow3D, a framework for integrating protein structure and interaction networks to map the mechanistic effects of somatic mutations in cancer.

LC-MS-based proteomics often relies on data-dependent acquisition (DDA) for quality control. Here, the authors demonstrate that data-independent acquisition (DIA) outperforms DDA in detecting subtle changes in LC-MS status in large-scale quantitative proteomics experiments. They further prioritized 15 QC metrics and developed an AI model, implemented in a free software called iDIA-QC, for detecting LC-MS faults.

Aligning trapped ion mobility with a mass-selective quadrupole and time-of-flight mass spectrometry enables parallel accumulation–serial fragmentation, which improves proteomic analysis. This protocol comprehensively describes PASEF workflows.

Longitudinal proteomics study identifies variability in proteome alterations in different tissues, reveals distinct consensus semitryptic motives in different organs and provides criteria for designing proteomic studies involving post-mortem samples.

Shifting gears in the latter part of one’s career is, for some, a way to do science differently.

The field of chemical biology has changed dramatically over the past 20 years. Here, we speak to Sarah E. O’Connor, Benjamin F. Cravatt and Chengqi Yi about the major changes that have affected the field, the advances that have affected them and their research, and their thoughts about the most important questions currently facing chemical biologists.

Experimental and computational studies are paving way for a deeper understanding of the dynamic nature of protein–protein interactions.

Approaches for profiling the spatial proteome in tissues are the basis of atlas-scale projects that are delivering on their promise for understanding biological complexity in health and disease.