Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition.

Physiological processes, such as the epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions.

Cellular control of protein levels: A systems biology perspective.

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

PDBcor: An automated correlation extraction calculator for multi-state protein structures.

Allostery and correlated motion are key elements linking protein dynamics with the mechanisms of action of proteins. Here, we present PDBCor, an automated and unbiased method for the detection and analysis of correlated motions from experimental multi-state protein structures. It uses torsion angle and distance statistics and does not require any structure superposition. Clustering of protein conformers allows us to extract correlations in the form of mutual information based on information theory. With PDBcor, we elucidated correlated motion in the WW domain of PIN1, the protein GB3, and the enzyme cyclophilin, in line with reported findings. Correlations extracted with PDBcor can be utilized in subsequent assays including nuclear magnetic resonance (NMR) multi-state structure optimization and validation. As a guide for the interpretation of PDBcor results, we provide a series of protein structure ensembles that exhibit different levels of correlation, including non-correlated, locally correlated, and globally correlated ensembles.

Multiple Testing and Pattern Recognition in 2-DE Proteomics.

After separation through two-dimensional gel electrophoresis (2-DE), several hundreds of individual protein abundances can be quantified in a cell population or sample tissue. However, gel-based proteomics has the reputation of being a slow and cumbersome art. But art is not dead! While 2-DE may no longer be the tool of choice in high-throughput differential proteomics, it is still very effective to identify and quantify protein species caused by genetic variations, alternative splicing, and/or PTMs. This chapter reviews some typical statistical exploratory and confirmatory tools available and suggests case-specific guidelines for (1) the discovery of potentially interesting protein spots, and (2) the further characterization of protein families and their possible PTMs.