Applying LFQRatio Normalization in Quantitative Proteomic Analysis of Microbial Co-culture Systems

Quantitative proteomic analysis plays a crucial role in understanding microbial co-culture systems. Traditional techniques, such as label-free quantification (LFQ) and label-based proteomics, provide valuable insights into the interactions and metabolic exchanges of microbial species. However, the complexity of microbial co-culture systems often leads to challenges in data normalization, especially when dealing with comparative LFQ data where ratios of different organisms can vary across experiments. This protocol describes the application of LFQRatio normalization, a novel normalization method designed to improve the reliability and accuracy of quantitative proteomics data obtained from microbial co-cultures. The method was developed following the analysis of factors that affect both the identification of proteins and the quantitative accuracy of co-culture proteomics. These include peptide physicochemical characteristics such as isoelectric point (pI), molecular weight (MW), hydrophobicity, dynamic range, and proteome size, as well as shared peptides between species. We then created a normalization method based on LFQ intensity values named LFQRatio normalization. This approach was demonstrated by analysis of a synthetic co-culture of two bacteria, <i>Synechococcus elongatus</i> cscB/SPS and <i>Azotobacter vinelandii</i> ΔnifL. Results showed enhanced accuracy of differentially expressed proteins, allowing for more reliable biological interpretation. This protocol provides a reliable and effective tool with wider application to analyze other co-culture systems to study microbial interactions. Key features • Assessment of factors affecting the quantitative accuracy of co-culture proteomics. • Provides a LFQRatio normalization method for label-free quantification of microbial co-cultures. • Recommendations for co-culture proteomics for mixed microbial populations.