TY - JOUR AU - Daly, James L. AU - Simonetti, Boris AU - Klein, Katja AU - Chen, Kai-En AU - Williamson, Maia Kavanagh AU - Anton-Plagaro, Carlos AU - Shoemark, Deborah K. AU - Simon-Gracia, Lorena AU - Bauer, Michael AU - Hollandi, Réka AU - Greber, Urs F. AU - Horváth, Péter AU - Sessions, Richard B. AU - Helenius, Ari AU - Hiscox, Julian A. AU - Teesalu, Tambet AU - Matthews, David A. AU - Davidson, Andrew D. AU - Collins, Brett M. AU - Cullen, Peter J. AU - Yamauchi, Yohei TI - Neuropilin-1 is a host factor for SARS-CoV-2 infection JF - SCIENCE J2 - SCIENCE VL - 370 PY - 2020 IS - 6518 SP - 861 EP - 865 PG - 5 SN - 0036-8075 DO - 10.1126/science.abd3072 UR - https://m2.mtmt.hu/api/publication/31794636 ID - 31794636 AB - Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), uses the viral spike (S) protein for host cell attachment and entry. The host protease furin cleaves the full-length precursor S glycoprotein into two associated polypeptides: S1 and S2. Cleavage of S generates a polybasic Arg-Arg-Ala-Arg carboxyl-terminal sequence on S1, which conforms to a C-end rule (CendR) motif that binds to cell surface neuropilin-1 (NRP1) and NRP2 receptors. We used x-ray crystallography and biochemical approaches to show that the S1 CendR motif directly bound NRP1. Blocking this interaction by RNA interference or selective inhibitors reduced SARS-CoV-2 entry and infectivity in cell culture. NRP1 thus serves as a host factor for SARS-CoV-2 infection and may potentially provide a therapeutic target for COVID-19. LA - English DB - MTMT ER - TY - JOUR AU - Hollandi, Réka AU - Szkalisity, Ábel AU - Tóth, Tímea AU - Tasnádi, Ervin Áron AU - Molnár, Csaba AU - Mathe, Botond AU - Grexa, István AU - Molnár, József AU - Bálind, Árpád AU - Gorbe, Mate AU - Kovács, Mária AU - Migh, Ede AU - Goodman, Allen AU - Balassa, Tamás AU - Koós, Krisztián AU - Wang, Wenyu AU - Caicedo, Juan Carlos AU - Bara, Norbert AU - Kovács, Ferenc AU - Paavolainen, Lassi AU - Danka, Tivadar AU - Kriston, András AU - Carpenter, Anne Elizabeth AU - Smith, Kevin AU - Horváth, Péter TI - nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer JF - CELL SYSTEMS J2 - CELL SYST VL - 10 PY - 2020 IS - 5 SP - 453 EP - 458 PG - 6 SN - 2405-4712 DO - 10.1016/j.cels.2020.04.003 UR - https://m2.mtmt.hu/api/publication/31334623 ID - 31334623 AB - Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information. LA - English DB - MTMT ER - TY - JOUR AU - Piccinini, F AU - Balassa, Tamás AU - Szkalisity, Ábel AU - Molnár, Csaba AU - Paavolainen, L AU - Kujala, K AU - Buzás, Krisztina AU - Sarazova, M AU - Pietiainen, V AU - Kutay, U AU - Smith, K AU - Horváth, Péter TI - Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data JF - CELL SYSTEMS J2 - CELL SYST VL - 4 PY - 2017 IS - 6 SP - 651 EP - 655 PG - 5 SN - 2405-4712 DO - 10.1016/j.cels.2017.05.012 UR - https://m2.mtmt.hu/api/publication/3247398 ID - 3247398 N1 - Short communication! AB - High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. LA - English DB - MTMT ER - TY - JOUR AU - Smith, K AU - Li, YP AU - Piccinini, F AU - Csucs, G AU - Balazs, C AU - Bevilacqua, A AU - Horváth, Péter TI - CIDRE: an illumination-correction method for optical microscopy JF - NATURE METHODS J2 - NAT METHODS VL - 12 PY - 2015 IS - 5 SP - 404 EP - 406 PG - 3 SN - 1548-7091 DO - 10.1038/NMETH.3323 UR - https://m2.mtmt.hu/api/publication/2896546 ID - 2896546 AB - Uneven illumination affects every image acquired by a microscope. It is often overlooked, but it can introduce considerable bias to image measurements. The most reliable correction methods require special reference images, and retrospective alternatives do not fully model the correction process. Our approach overcomes these issues for most optical microscopy applications without the need for reference images. LA - English DB - MTMT ER - TY - JOUR AU - Smith, K AU - Horváth, Péter TI - Active Learning Strategies for Phenotypic Profiling of High-Content Screens JF - JOURNAL OF BIOMOLECULAR SCREENING J2 - J BIOMOL SCREEN VL - 19 PY - 2014 IS - 5 SP - 685 EP - 695 PG - 11 SN - 1087-0571 DO - 10.1177/1087057114527313 UR - https://m2.mtmt.hu/api/publication/2724011 ID - 2724011 AB - High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. LA - English DB - MTMT ER -