ArticleJournal of proteome research2024
Optimized Automated Workflow for BioID Improves Reproducibility and Identification of Protein-Protein Interactions.
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.
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Who cites it
7 citing papers in PubMed.
- Proteomic investigation of signaling dynamics: from static maps to network rewiring.Bioscience reports · 2026Review
- The Regulatory Network of FOXM1: Orchestrating Cancer Progression and Resistance to Therapy.International journal of molecular sciences · 2026Review
- Oxidative stress causes a reversible decrease of deubiquitylases activity in old vertebrate brains.Nature communications · 2026Article
- Multi-omic mapping of Drosophila protein secretomes reveals tissue-specific origins and inter-organ trafficking.Nature communications · 2026Article
- Integrating endogenous TurboID and data-independent acquisition mass spectrometry for in vivo proximity labeling.The EMBO journal · 2026Article
- Overcoming Analytical Challenges in Proximity Labeling Proteomics.Journal of mass spectrometry : JMS · 2025Review
- Benchmarking and Automating the Biotinylation Proteomics Workflow.Research square · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Proximity-dependent biotinylation is an important method to study protein-protein interactions in cells, for which an expanding number of applications has been proposed. The laborious and time-consuming sample processing has limited project sizes so far. Here, we introduce an automated workflow on a liquid handler to process up to 96 samples at a time. The automation not only allows higher sample numbers to be processed in parallel but also improves reproducibility and lowers the minimal sample input. Furthermore, we combined automated sample processing with shorter liquid chromatography gradients and data-independent acquisition to increase the analysis throughput and enable reproducible protein quantitation across a large number of samples. We successfully applied this workflow to optimize the detection of proteasome substrates by proximity-dependent labeling.
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Registered trials
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