Subcellular proteome niche discovery using semi-supervised functional clustering

TL;DR

This paper introduces FSPmix, a semi-supervised clustering tool that improves protein localization predictions in subcellular proteomics, especially for non-model organisms with noisy data.

Contribution

FSPmix is a novel semi-supervised clustering method that leverages partial annotations to enhance subcellular localization predictions in proteomics data.

Findings

FSPmix successfully assigned probabilistic localizations to proteins in a marine diatom dataset.

The method uncovered potentially new protein functions based on localization.

FSPmix demonstrated robustness in low signal-to-noise data regimes.

Abstract

Intracellular compartmentalization of proteins underpins their function and the metabolic processes they sustain. Various mass spectrometry-based proteomics methods (subcellular spatial proteomics) now allow high throughput subcellular protein localization. Yet, the curation, analysis and interpretation of these data remain challenging, particularly in non-model organisms where establishing reliable marker proteins is difficult, and in contexts where experimental replication and subcellular fractionation are constrained. Here, we develop FSPmix, a semi-supervised functional clustering method implemented as an open-source R package, which leverages partial annotations from a subset of marker proteins to predict protein subcellular localization from subcellular spatial proteomics data. This method explicitly assumes that protein signatures vary smoothly across subcellular fractions, enabling more robust inference under low signal-to-noise data regimes. We applied FSPmix to a subcellular proteomics dataset from a marine diatom, allowing us to assign probabilistic localizations to proteins and uncover potentially new protein functions. Altogether, this work lays the foundation for more robust statistical analysis and interpretation of subcellular proteomics datasets, particularly in understudied organisms.