When less is more - A fast TurboID KI approach for high sensitivity endogenous interactome mapping

Abstract

In recent years, proximity labelling has established itself as an unbiased and powerful approach to map the interactome of specific proteins. While physiological expression of the labelling enzyme is beneficial for the mapping of interactors, generation of the desired cell lines remains time-consuming and challenging. Using our established pipeline for the rapid generation of C- and N-terminal CRISPR-Cas9 knock-ins (KIs) based on antibiotic selection, we were able to compare the performance of commonly used labelling enzymes when endogenously expressed. Endogenous tagging of the μ subunit of the AP-1 complex with TurboID allowed identification of known interactors and cargo proteins that simple overexpression of a labelling enzyme fusion protein could not reveal. We used the KI-strategy to compare the interactome of the different adaptor protein (AP) complexes and clathrin and were able to assemble lists of potential interactors and cargo proteins that are specific for each sorting pathway. Our approach greatly simplifies the execution of proximity labelling experiments for proteins in their native cellular environment and allows going from CRISPR transfection to mass spectrometry analysis and interactome data in just over a month.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* 1) We changed the tone of the manuscript and focussed more on the benefits of the presented KI strategy over other strategies used in the field. By doing so, we restructured the manuscripts and moved the comparison of the biotin ligases (Former Fig. 2, now supplementary Fig. 1) into the supplementary information. At the same time, we pointed out differences and advantages compared to other strategies in both the introduction and discussion. 2) We expanded the manuscript with an additional MS dataset derived from N-terminal tagging of clathrin light chain to show that both C- and N-terminal KI strategy can be used to obtain high quality MS data. 3) To evaluate the MS data quality, we added GO-term analysis to characterize the protein hits (potential interactors) and show the high accuracy of the presented approach. 4) We carried out validation of some top hits from the AP-1 dataset (Fig. 3).