Statistical Inference and Community Detection in Proximity and Spatial Proteomics: Resolving the Organization of the Neuronal Proteome

Abstract

Technological advances in protein mass spectrometry (MS), aka proteomics, haveenabled high-throughput quantification of spatially-resolved, subcellular-specific proteomes. Biological insight in these experiments depends upon sound statistical analysis. Despite the myriad of existing proprietary and open-source software solutions for statistical analysis of proteomics data, these tools suffer a drawback inherent in any general solution: a loss of specificity. These tools often fail to be easily adapted to analyze experiment-specific designs. I present a flexible, linear mixed-effects model framework for assessing differential abundance in protein mass spectrometry experiments. Combined with methods to identify communities of proteins in biological networks, I extend this framework to perform inference at the level of protein groups or modules. Using these software tools, I demonstrate how module-level insight in proximity and spatial proteomics generates hypotheses that identify foci of biological function and dysfunction which may underlie the neuropathology of disease.