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The value of synthetic microbial communities in biotechnology is gaining traction due to their ability to undertake more complex metabolic tasks than monocultures. However, a thorough understanding of strain interactions, productivity, and stability is often required to optimize growth and scale up cultivation. Quantitative proteomics can provide valuable insights into how microbial strains adapt to changing conditions in biomanufacturing. However, current workflows and methodologies are not suitable for simple artificial coculture systems where strain ratios are dynamic. Here, we established a workflow for coculture proteomics using an exemplar system containing two members, Azotobacter vinelandii and Synechococcus elongatus. Factors affecting the quantitative accuracy of coculture proteomics were investigated, including peptide physicochemical characteristics such as molecular weight, isoelectric point, hydrophobicity, and dynamic range as well as factors relating to protein identification such as varying proteome size and shared peptides between species. Different quantification methods based on spectral counts and intensity were evaluated at the protein and cell level. We propose a new normalization method, named "LFQRatio", to reflect the relative contributions of two distinct cell types emerging from cell ratio changes during cocultivation. LFQRatio can be applied to real coculture proteomics experiments, providing accurate insights into quantitative proteome changes in each strain.
Shi et al. (Wed,) studied this question.