Justin A. Bosch

Characterizing the proteome composition of organelles and subcellular regions of living cells can facilitate the understanding of cellular organization as well as protein interactome networks. Proximity labeling-based methods coupled with mass spectrometry (MS) offer a high-throughput approach for systematic analysis of spatially restricted proteomes. Proximity labeling utilizes enzymes that generate reactive radicals to covalently tag neighboring proteins. The tagged endogenous proteins can then be isolated for further analysis by MS. To analyze protein-protein interactions or identify components that localize to discrete subcellular compartments, spatial expression is achieved by fusing the enzyme to specific proteins or signal peptides that target to particular subcellular regions. Although these technologies have only been introduced recently, they have already provided deep insights into a wide range of biological processes. Here, we provide an updated description and comparison of proximity labeling methods, as well as their applications and improvements. As each method has its own unique features, the goal of this review is to describe how different proximity labeling methods can be used to answer different biological questions. This article is categorized under: Technologies > Analysis of Proteins.

Conventional approaches to identify secreted factors that regulate homeostasis are limited in their abilities to identify the tissues/cells of origin and destination. We established a platform to identify secreted protein trafficking between organs using an engineered biotin ligase (BirA*G3) that biotinylates, promiscuously, proteins in a subcellular compartment of one tissue. Subsequently, biotinylated proteins are affinity-enriched and identified from distal organs using quantitative mass spectrometry. Applying this approach in Drosophila, we identify 51 muscle-secreted proteins from heads and 269 fat body-secreted proteins from legs/muscles, including CG2145 (human ortholog ENDOU) that binds directly to muscles and promotes activity. In addition, in mice, we identify 291 serum proteins secreted from conditional BirA*G3 embryo stem cell-derived teratomas, including low-abundance proteins with hormonal properties. Our findings indicate that the communication network of secreted proteins is vast. This approach has broad potential across different model systems to identify cell-specific secretomes and mediators of interorgan communication in health or disease.

Screens in mosaic Drosophila tissues that use chemical mutagenesis have identified many regulators of growth and patterning. Many of the mutant phenotypes observed were contingent upon the presence of both wild-type and mutant cells in the same tissue. More recently, large collections of RNAi lines or cDNAs expressed under Gal4/UAS control have been used to alter gene expression uniformly in specific tissues. However, these newer approaches are not easily combined with the efficient generation of genetic mosaics. The CoinFLP system described here enables mosaic screens in the context of gene knockdown or overexpression by automatically generating a reliable ratio of mutant to wild-type tissue in a developmentally controlled manner. CoinFLP-Gal4 generates mosaic tissues composed of clones of which only a subset expresses Gal4. CoinFLP-LexGAD/Gal4 generates tissues composed of clones that express either Gal4 or LexGAD, thus allowing the study of interactions between different types of genetically manipulated cells. By combining CoinFLP-LexGAD/Gal4 with the split-GFP system GRASP, boundaries between genetically distinct cell populations can be visualized at high resolution.