ArticleJournal of proteome research2024
LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems.
Article in Journal of proteome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Characterising complex metabolic responses in an engineered, cross-feeding microbial co-culture using quantitative proteomics.New biotechnology · 2026Article
- Mono-mix strategy enables comparative proteomics of a cross-kingdom microbial symbiosis.PloS one · 2026Article
- Library-free data-independent acquisition mass spectrometry enables comprehensive coverage of the cyanobacterial proteome.Plant physiology · 2025Article
- Peptide abundance correlations in metaproteomics enhance taxonomic and functional analysis of the human gut microbiome.NPJ biofilms and microbiomes · 2025Article
- Applying LFQRatio Normalization in Quantitative Proteomic Analysis of Microbial Co-culture Systems.Bio-protocol · 2025Article
- Article
- Integrating the milk microbiome signatures in mastitis: milk-omics and functional implications.World journal of microbiology & biotechnology · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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,
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Registered trials
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