LAUSR

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization. Description Tracking proteins Improved understanding of how proteins are organized within human cells should enhance our systems-level understanding of how cells function. Cho et al. used CRISPR technology to express more than 1000 different proteins at near endogenous amounts with labels that allowed both fluorescent imaging of their location and immunoprecipitation and mass spectrometry analysis of interacting protein partners (see the Perspective by Michnick and Levy). The large-scale data are made available on an interactive website, with clustering and analysis performed by machine learning. The studies emphasize the unusual properties of RNA-binding proteins and indicate that protein localization is very specific and may allow predictions of function. —LBR Combining genome engineering, live-cell imaging, mass spectrometry, and data science are used to map the localization and interactions of human proteins. INTRODUCTION Proteins are the product of gene expression and the molecular building blocks of cells. Examples include enzymes that orchestrate the cell’s chemistry, filaments that shape the cell’s structure, or the pharmacological targets of drugs. The genome sequence provides us with the complete set of proteins that give rise to the human cell. However, systematically characterizing how proteins organize within the cell to sustain its operation remains an important goal of modern cell biology. A comprehensive map of the human proteome’s organization will serve as a reference to explore gene function in health and disease. RATIONALE Subcellular localization and physical interactions are key aspects tightly related to the function of any given protein. Proteins localize to different subcellular compartments, which enables a spatial separation of cellular functions. Proteins also physically interact with one another, forming molecular networks that connect proteins involved in the same processes. Therefore, mapping the cell’s molecular organization requires a comprehensive description of where different proteins localize and how they interact. Among other strategies, a powerful approach to map cellular architecture is to visualize individual proteins using fusions with fluorescent protein “tags.” These tags allow us not only to image protein localization in live cells, but also to measure protein interactions by serving as handles for immunopurification–mass spectrometry (IP-MS). Recent advances in genome engineering facilitate tagging of endogenous human genes, so that the corresponding proteins can be characterized in their native cellular environment. RESULTS Using high-throughput CRISPR-mediated genome editing, we constructed a library of 1310 fluorescently tagged cell lines. By performing paired IP-MS and live-cell imaging using this library, we generated a large dataset that maps the cellular localization and physical interactions of the corresponding 1310 proteins. Applying a combination of unsupervised clustering and machine learning for image analysis allowed us to objectively identify proteins that share spatial or interaction signatures. Our data provide insights into the function of individual proteins, but also enable us to derive some general principles of human cellular organization. In particular, we show that proteins that bind RNA form a separate subgroup defined by specific localization and interaction signatures. We also show that the precise spatial distribution of a given protein is very strongly correlated with its cellular function, such that fine-grained molecular insights can be derived from the analysis of imaging data. Our open-source dataset can be explored through an interactive web interface at opencell.czbiohub.org. CONCLUSION Our results show that endogenous tagging coupled with interactome and microscopy analysis provides new systems-level insights about the organization of the human proteome. The information contained within the subcellular distribution of each protein is highly specific and can be paired with advances in machine learning to extrapolate fine-grained functional information using microscopy alone. This opens exciting avenues for the characterization of understudied proteins, high-throughput screening, and modeling of complex cellular states during differentiation and disease. OpenCell: Combining endogenous tagging, live-cell imaging, and interaction proteomics to map the architecture of the human proteome. We created a library of engineered cell lines by using CRISPR to introduce fluorescent tags into 1310 individual human proteins. This allowed us to image the localization of each protein in live cells, as well as the interactions between a given target and other proteins within the cell. This large dataset enables a systems-level description of the organization of the human proteome.