Protocol Citation: Emilio Cirri, Hannah Knaudt, Domenico Di Fraia, Nadine Pömpner, Norman Rahnis, Ivonne Heinze, Alessandro Ori, Therese Dau 2024. Automated BioID sample preparation. protocols.io https://dx.doi.org/10.17504/protocols.io.kxygxymdwl8j/v2Version created by Emilio Cirri
Manuscript citation:
Cirri, E., Knaudt, H., Fraia, D. Di, Pömpner, N., Rahnis, N., Heinze, I., Ori, A., & Dau, T. (2023). Automated workflow for BioID improves reproducibility and identification of protein-protein interactions. BioRxiv, 2023.09.08.556804. https://doi.org/10.1101/2023.09.08.556804
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
We introduce an automated workflow for proximity dependent biotinylation on a liquid handler to process up to 96 samples at a time,combined with shorter liquid chromatography gradients and data-independent acquisition to increase analysis throughput and enable reproducible protein quantitation
- Lysis buffer: 50 mM Tris, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% Triton, 0.1% SDS, 1.5 µM
aprotinin, 10 µM leupeptin, 250U Turbonuclease
- Acetylation buffer: 10 mM sulfo-NHS acetate
- Wash buffer: 50 mM AmBic, pH 8.3
- Digest buffer: 0.5 µg LysC in 50 mM AmBic
- Elution buffer: 10% TFA in ACN
- Maintenance buffers: 20% ACN
General concept
General concept
Proximity dependent biotinylation (BioID) 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 does not only allow 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 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.