@article{MTMT:31334623,
	title = {nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer},
	url = {https://m2.mtmt.hu/api/publication/31334623},
	author = {Hollandi, Réka and Szkalisity, Ábel and Tóth, Tímea and Tasnádi, Ervin Áron and Molnár, Csaba and Mathe, Botond and Grexa, István and Molnár, József and Bálind, Árpád and Gorbe, Mate and Kovács, Mária and Migh, Ede and Goodman, Allen and Balassa, Tamás and Koós, Krisztián and Wang, Wenyu and Caicedo, Juan Carlos and Bara, Norbert and Kovács, Ferenc and Paavolainen, Lassi and Danka, Tivadar and Kriston, András and Carpenter, Anne Elizabeth and Smith, Kevin and Horváth, Péter},
	doi = {10.1016/j.cels.2020.04.003},
	journal-iso = {CELL SYST},
	journal = {CELL SYSTEMS},
	volume = {10},
	unique-id = {31334623},
	issn = {2405-4712},
	abstract = {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.},
	year = {2020},
	eissn = {2405-4720},
	pages = {453-458},
	orcid-numbers = {Molnár, Csaba/0000-0002-6124-1209; Molnár, József/0000-0002-9185-9376}
}

@article{MTMT:3325810,
	title = {Signaling Lymphocyte Activation Molecule Family 5 Enhances Autophagy and Fine-Tunes Cytokine Response in Monocyte-Derived Dendritic Cells via Stabilization of Interferon Regulatory Factor 8},
	url = {https://m2.mtmt.hu/api/publication/3325810},
	author = {Agod, Zsófia and Pázmándi, Kitti Linda and Bencze, Dóra and Vereb, György and Bíró, Tamás and Szabó, Attila and Rajnavölgyi, Éva and Bácsi, Attila and Engel, Pablo and Lányi, Árpád},
	doi = {10.3389/fimmu.2018.00062},
	journal-iso = {FRONT IMMUNOL},
	journal = {FRONTIERS IN IMMUNOLOGY},
	volume = {9},
	unique-id = {3325810},
	issn = {1664-3224},
	year = {2018},
	eissn = {1664-3224}
}

@article{MTMT:3258443,
	title = {Pooled CRISPR screening with single-cell transcriptome readout.},
	url = {https://m2.mtmt.hu/api/publication/3258443},
	author = {Datlinger, P and Rendeiro, AF and Schmidl, C and Krausgruber, T and Traxler, P and Klughammer, J and Schuster, LC and Kuchler, A and Alpár, Donát and Bock, C},
	doi = {10.1038/nmeth.4177},
	journal-iso = {NAT METHODS},
	journal = {NATURE METHODS},
	volume = {14},
	unique-id = {3258443},
	issn = {1548-7091},
	abstract = {CRISPR-based genetic screens are accelerating biological discovery, but current methods have inherent limitations. Widely used pooled screens are restricted to simple readouts including cell proliferation and sortable marker proteins. Arrayed screens allow for comprehensive molecular readouts such as transcriptome profiling, but at much lower throughput. Here we combine pooled CRISPR screening with single-cell RNA sequencing into a broadly applicable workflow, directly linking guide RNA expression to transcriptome responses in thousands of individual cells. Our method for CRISPR droplet sequencing (CROP-seq) enables pooled CRISPR screens with single-cell transcriptome resolution, which will facilitate high-throughput functional dissection of complex regulatory mechanisms and heterogeneous cell populations.},
	keywords = {Humans; cell proliferation; Cell Line; Gene Expression Profiling/*methods; HEK293 Cells; High-Throughput Nucleotide Sequencing/*methods; Single-Cell Analysis/methods; Transcriptome/*genetics; Sequence Analysis, RNA/*methods; RNA, Guide/genetics; Clustered Regularly Interspaced Short Palindromic Repeats/*genetics},
	year = {2017},
	eissn = {1548-7105},
	pages = {297-301}
}