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RNA-protein interactions play a pivotal role in cell homeostasis and disease, but current approaches to study them require a considerable amount of starting material, favor the recovery of only a subset of RNA species or are complex and time-consuming. We recently developed orthogonal organic phase separation (OOPS): a quick, efficient and reproducible method to purify cross-linked RNA-protein adducts in an unbiased way. OOPS avoids molecular tagging or the capture of polyadenylated RNA. Instead, it is based on sampling the interface of a standard TRIzol extraction to enrich RNA-binding proteins (RBPs) and their cognate bound RNA. OOPS specificity is achieved by digesting the enriched interfaces with RNases or proteases to release the RBPs or protein-bound RNA, respectively. Here we present a step-by-step protocol to purify protein-RNA adducts, free protein and free RNA from the same sample. We further describe how OOPS can be applied in human cell lines, Arabidopsis thaliana, Schizosaccharomyces pombe and Escherichia coli and how it can be used to study RBP dynamics.

Spatial proteomics has provided important insights into the relationship between protein function and subcellular location. Localization of Organelle Proteins by Isotope Tagging (LOPIT) and its variants are proteome-wide techniques, not matched in scale by microscopy-based or proximity tagging-based techniques, allowing holistic mapping of protein subcellular location and re-localization events downstream of cellular perturbations. LOPIT can be a powerful and versatile tool in drug discovery for unlocking important information on disease pathophysiology, drug mechanism of action, and off-target toxicity screenings. Here, we discuss technical concepts of LOPIT with its potential applications in drug discovery and development research.

The spatial subcellular proteome is a dynamic environment; one that can be perturbed by molecular cues and regulated by post-translational modifications. Compartmentalization of this environment and management of these biomolecular dynamics allows for an array of ancillary protein functions. Profiling spatial proteomics has proved to be a powerful technique in identifying the primary subcellular localization of proteins. The approach has also been refashioned to study multi-localization and localization dynamics. Here, the analytical approaches that have been applied to spatial proteomics thus far are critiqued, and challenges particularly associated with multi-localization and dynamic relocalization is identified. To meet some of the current limitations in analytical processing, it is suggested that Bayesian modeling has clear benefits over the methods applied to date and should be favored whenever possible. Careful consideration of the limitations and challenges, and development of robust statistical frameworks, will ensure that profiling spatial proteomics remains a valuable technique as its utility is expanded.