Shining a Light on the Dark-Field of the Metaproteome - Duan, Haonan (Université d'Ottawa | University of Ottawa, 2024)
Metaproteomics has emerged as a powerful tool for studying human gut microbiomes. However, the intricate nature of these microbial communities often surpasses the analytical depth of current metaproteomic methodologies, particularly those relying on traditional Data-Dependent Acquisition (DDA) techniques. In this thesis, we first quantitatively assess the limitations of DDA-based metaproteomics, revealing a significant underrepresentation of proteins from low-abundance species. This finding underscores the necessity for analytical approaches capable of capturing the full spectrum of microbiome samples. Further investigation into the capabilities of mass spectrometry to discern the complexity of human gut microbiomes revealed that the complexity of MS1 spectra from microbiome samples significantly exceeds that of individual species. This suggests that many species are potentially detectable at the MS1 level but are systematically overlooked by conventional shotgun metaproteomic strategies, which prioritize peptides for MS2 fragmentation based on abundance. In response to these challenges, we explored the application of Data-Independent Acquisition (DIA) in metaproteomics. We developed and optimized MetaDIA, a novel workflow for the analysis of microbiome DIA data. MetaDIA operates independently of DDA-derived spectral libraries, offering a robust alternative that enhances the detection and characterization of microbial proteins. Our comparative analyses demonstrate that MetaDIA achieves strong consistency with the result from traditional DDA-derived spectral libraries, both in terms of protein identification and functional annotation. This work not only highlights the limitations of current metaproteomic methodologies in capturing the full complexity of microbiomes but also introduces MetaDIA as a promising strategy for advancing the depth and accuracy of microbial community analysis.
Keywords: Metaproteomics; Human gut microbiomes; Data-Dependent Acquisition (DDA); Data-Independent Acquisition (DIA)