The complexity of living organisms does not scale with the predicted number of protein coding genes. Many factors contribute to increasing complexity, including non-coding RNA mediated control mechanisms and post-transcriptional and post-translation processing. The location of protein synthesis also plays a key role in expanding protein functionality, with aberrant spatial translation being a driver in multiple diseases.
In order understand cellular physiology, a thorough understanding of the spatial relationship of the transcriptome, translatome and proteome is required. Several existing methodologies are able to capture the transcriptome and proteome at specific sub-cellular locations. Holistic approaches, however, are required to construct cell-wide models that can give insight into the multi-purposing of components that leads to the expansion of cellular functions.
I will describe new approaches to capture the spatial relationship between RNA and protein on a cell-wide scale. I will discuss methods designed to map the cellular spatial proteome (1) (2) based on physicochemical fractionation of cellular components (LOPIT), that also give insights into the effect of post-translational modification on protein location. I will describe the tools we have developed to robustly capture dynamic re-localization of proteins utilising Bayesian analysis (3) to identify dynamic changes in the spatial and temporal proteome and uncover different types of translocation events. I will also discuss how we have significantly modified these approaches in order to capture the spatial transcriptome (LoRNA– localization of RNA). I will introduce the orthogonal organic phase separation (OOPS) protocol that recovers both RNA and protein from cross-linked RNA-protein complexes in an unbiased manner independent of polyadenylation status of RNA (4). Finally, I will demonstrate that when applied in concert, these approaches reveal the spatial interplay of the proteome and transcriptome on a cell–wide scale producing three over-lapping maps:
1. Protein map (LOPIT)
2. RNA binding protein (RBP) map
3. Total RNA map (LoRNA)
I will describe some unexpected findings from these maps including the RNA binding capacity of many metabolic enzymes and therapeutic targets and the steady state location of mRNA species that code for different protein families.