Moving Profiling Spatial Proteomics Beyond Discrete Classification

Abstract

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. 1. Protein Subcellular Localization is a Key Component of Function Correct subcellular localization affects protein function. This includes the availability of binding partners, cofactors, and substrates, as well as the presence of regulatory factors such as kinases. [1] The interrogation of localization is therefore a necessary step to elucidate function. The localization of the proteome is highly dynamic, for example, trafficking of secretory pathway components through membrane-bound compartments [2] and shuttling of proteins, such as transcription factors, between compartments based on their phosphorylation status, [3-6] in response to activation of signaling pathways. [7] Furthermore, there is mounting evidence that many proteins "moonlight" and perform multiple discrete functions depending on the

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