postdoc HHMI/Stanford University - Department of Biology
Defining specific protein interactions and spatially or temporally restricted local proteomes can greatly improve our understanding of all cellular processes, but obtaining such datasets can be challenging, especially when the protein, cell type or event of interest is rare. In mammalian systems, proximity labeling methods like BioID and APEX have become popular tools for identifying functional protein complexes and local proteomes. Proteins in close proximity to a protein or cellular domain of interest are biotinylated and can then be affinity purified and identified by mass spectrometry. Due to technical challenges, the use of these techniques has been extremely limited in plants. However, recent technological innovations, in the form of two highly active biotin ligases (TurboID and miniTurbo), is promising to make proximity labeling more accessible to plants. We tested TurboID and miniTurbo in Arabidopsis and N. benthamiana and found that, when broadly expressed, both ligases work well in different tissues and under a range of experimental conditions. To further test the applicability of TurboID for identification of putative protein interactors and local proteomes in individual and rare cell types, we used the Arabidopsis stomatal lineage as a test case. By fusing TurboID to stage-specific key transcription factors of stomatal development, we identified both known and novel putative interaction partners of these transcriptional master regulators that could give new insight into their functional mechanisms and specificity. By targeting TurboID to the nucleus of late-stage stomatal lineage cells, we further obtained information on their nuclear proteome and identified proteins that are enriched in or even specific for this cell type. These include well-known, but also new potential regulators of stomatal differentiation. Overall, our data suggest that TurboID and miniTurbo work well in plants and will be applicable for a wide range of questions.
Coauthors: Shou-Ling Xu – Carnegie Institution for Science;Tess Branon – UC Berkeley;Alice Ting – Stanford University;Dominique Bergmann – Stanford University