RESUMO
Imaging-based methods are widely used for studying the subcellular localization of proteins in living cells. While routine for individual proteins, global monitoring of protein dynamics following perturbation typically relies on arrayed panels of fluorescently tagged cell lines, limiting throughput and scalability. Here, we describe a strategy that combines high-throughput microscopy, computer vision and machine learning to detect perturbation-induced changes in multicolour tagged visual proteomics cell (vpCell) pools. We use genome-wide and cancer-focused intron-targeting sgRNA libraries to generate vpCell pools and a large, arrayed collection of clones each expressing two different endogenously tagged fluorescent proteins. Individual clones can be identified in vpCell pools by image analysis using the localization patterns and expression level of the tagged proteins as visual barcodes, enabling simultaneous live-cell monitoring of large sets of proteins. To demonstrate broad applicability and scale, we test the effects of antiproliferative compounds on a pool with cancer-related proteins, on which we identify widespread protein localization changes and new inhibitors of the nuclear import/export machinery. The time-resolved characterization of changes in subcellular localization and abundance of proteins upon perturbation in a pooled format highlights the power of the vpCell approach for drug discovery and mechanism-of-action studies.
Assuntos
Proteômica , Humanos , Proteômica/métodos , Aprendizado de Máquina , Microscopia de Fluorescência/métodos , Linhagem Celular TumoralRESUMO
Despite being considered druggable and attractive therapeutic targets, most of the solute carrier (SLC) membrane transporters remain pharmacologically underexploited. One of the reasons for this is a lack of reliable chemical screening assays, made difficult by functional redundancies among SLCs. In this study we leveraged synthetic lethality between the lactate transporters SLC16A1 and SLC16A3 in a screening strategy that we call paralog-dependent isogenic cell assay (PARADISO). The system involves five isogenic cell lines, each dependent on various paralog genes for survival/fitness, arranged in a screening cascade tuned for the identification of SLC16A3 inhibitors. We screened a diversity-oriented library of â¼90,000 compounds and further developed our hits into slCeMM1, a paralog-selective and potent SLC16A3 inhibitor. By implementing chemoproteomics, we showed that slCeMM1 is selective also at the proteome-wide level, thus fulfilling an important criterion for chemical probes. This study represents a framework for the development of specific cell-based drug discovery assays.
Assuntos
Proteínas de Transporte , Descoberta de Drogas , Proteínas de Membrana Transportadoras/genéticaRESUMO
SMNDC1 is a Tudor domain protein that recognizes di-methylated arginines and controls gene expression as an essential splicing factor. Here, we study the specific contributions of the SMNDC1 Tudor domain to protein-protein interactions, subcellular localization, and molecular function. To perturb the protein function in cells, we develop small molecule inhibitors targeting the dimethylarginine binding pocket of the SMNDC1 Tudor domain. We find that SMNDC1 localizes to phase-separated membraneless organelles that partially overlap with nuclear speckles. This condensation behavior is driven by the unstructured C-terminal region of SMNDC1, depends on RNA interaction and can be recapitulated in vitro. Inhibitors of the protein's Tudor domain drastically alter protein-protein interactions and subcellular localization, causing splicing changes for SMNDC1-dependent genes. These compounds will enable further pharmacological studies on the role of SMNDC1 in the regulation of nuclear condensates, gene regulation and cell identity.