ArticleCell systems2020
SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles.
Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.Nature methods · 2021Pooled it
- Complex assembly and activity states as multifaceted protein attributes explaining phenotypic variability.Molecular systems biology · 2026Article
- Quantitative interactome mapping of skeletal muscle insulin resistance.Molecular systems biology · 2026Article
- The multidimensional significance of virus-host protein interactions and their implications in the antiviral defense of plants.Stress biology · 2026Review
- Complex Assembly and Activity States as Multifaceted Protein Attributes Explaining Phenotypic Variability.bioRxiv : the preprint server for biology · 2025Article
- Global profiling of protein complex dynamics with an experimental library of protein interaction markers.Nature biotechnology · 2025Article
- Article
- SEC-MX: an approach to systematically study the interplay between protein assembly states and phosphorylation.Nature communications · 2025Article
- Large-scale map of RNA-binding protein interactomes across the mRNA life cycle.Molecular cell · 2024Article
- PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals.Cell reports methods · 2024Article
- Progress in mass spectrometry approaches to profiling protein-protein interactions in the studies of the innate immune system.Journal of proteins and proteomics · 2024Article
- [Applications of high performance liquid chromatography-mass spectrometry in proteomics].Se pu = Chinese journal of chromatography · 2024Review
- Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.Nature communications · 2024Article
- Large-scale map of RNA binding protein interactomes across the mRNA life-cycle.bioRxiv : the preprint server for biology · 2023Article
- Rapid Profiling of Protein Complex Reorganization in Perturbed Systems.Journal of proteome research · 2023Article
- Protein complexes in cells by AI-assisted structural proteomics.Molecular systems biology · 2023Article
- Unveiling the complexity of transcription factor networks in hematopoietic stem cells: implications for cell therapy and hematological malignancies.Frontiers in oncology · 2023Review
- The Second Asia-Oceania Human Proteome Organization (AOHUPO) Online Education Series on the Renaissance of Clinical Proteomics: Biomarkers, Imaging and Therapeutics.Molecular & cellular proteomics : MCP · 2022Article
- PCfun: a hybrid computational framework for systematic characterization of protein complex function.Briefings in bioinformatics · 2022Article
- A peptidoform based proteomic strategy for studying functions of post-translational modifications.Proteomics · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Protein-protein interactions (PPIs) play critical functional and regulatory roles in cellular processes. They are essential for macromolecular complex formation, which in turn constitutes the basis for protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we extend co-fractionation and mass spectrometric data analysis to perform quantitative, network-based studies of proteome organization, via the size-exclusion chromatography algorithmic toolkit (SECAT). This framework explicitly accounts for the dynamic nature and rewiring of protein complexes across multiple cell states and samples, thus, elucidating molecular mechanisms that are differentially implemented across different experimental settings. Systematic analysis of multiple datasets shows that SECAT represents a highly scalable and effective methodology to assess condition/state-specific protein-network state. A record of this paper's transparent peer review process is included in the Supplemental Information.
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