@article{MTMT:32570862,
	title = {Fehérje méretű molekulák humán sejtekbe juttatása lipid-raft mediált endocitózissal},
	url = {https://m2.mtmt.hu/api/publication/32570862},
	author = {Hetényi, Anasztázia and Imre, Norbert and Szabó, Enikő and Bodnár, Brigitta and Szkalisity, Ábel and Gróf, Ilona and Bocsik, Alexandra and Deli, Mária Anna and Horváth, Péter and Czibula, Ágnes and Monostori, Éva and Martinek, Tamás},
	journal-iso = {BIOKÉMIA},
	journal = {BIOKÉMIA: A MAGYAR BIOKÉMIAI EGYESÜLET FOLYÓIRATA},
	volume = {45},
	unique-id = {32570862},
	issn = {0133-8455},
	year = {2021},
	eissn = {2060-8152},
	pages = {67-83},
	orcid-numbers = {Hetényi, Anasztázia/0000-0001-8080-6992; Deli, Mária Anna/0000-0001-6084-6524; Czibula, Ágnes/0000-0003-4461-2773; Monostori, Éva/0000-0002-7442-3562; Martinek, Tamás/0000-0003-3168-8066}
}

@article{MTMT:32023128,
	title = {Regression plane concept for analysing continuous cellular processes with machine learning},
	url = {https://m2.mtmt.hu/api/publication/32023128},
	author = {Szkalisity, Ábel and Piccinini, Filippo and Beleon, Attila and Balassa, Tamás and Varga, Gergely István and Migh, Ede and Molnár, Csaba and Paavolainen, Lassi and Timonen, Sanna and Banerjee, Indranil and Ikonen, Elina and Yamauchi, Yohei and Andó, István and Peltonen, Jaakko and Pietiäinen, Vilja and Honti, Viktor and Horváth, Péter},
	doi = {10.1038/s41467-021-22866-x},
	journal-iso = {NAT COMMUN},
	journal = {NATURE COMMUNICATIONS},
	volume = {12},
	unique-id = {32023128},
	issn = {2041-1723},
	year = {2021},
	eissn = {2041-1723},
	orcid-numbers = {Piccinini, Filippo/0000-0002-0371-7782; Varga, Gergely István/0000-0001-9073-5788; Molnár, Csaba/0000-0002-6124-1209; Ikonen, Elina/0000-0001-8382-1135; Yamauchi, Yohei/0000-0002-8233-9133; Andó, István/0000-0002-4648-9396; Pietiäinen, Vilja/0000-0003-3125-2406}
}

@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:31126947,
	title = {Routing Nanomolar Protein Cargoes to Lipid Raft‐Mediated/Caveolar Endocytosis through a Ganglioside GM1‐Specific Recognition Tag},
	url = {https://m2.mtmt.hu/api/publication/31126947},
	author = {Imre, Norbert and Hetényi, Anasztázia and Szabó, Enikő and Bodnár, Brigitta and Szkalisity, Ábel and Gróf, Ilona and Bocsik, Alexandra and Deli, Mária Anna and Horváth, Péter and Czibula, Ágnes and Monostori, Éva and Martinek, Tamás},
	doi = {10.1002/advs.201902621},
	journal-iso = {ADV SCI},
	journal = {ADVANCED SCIENCE},
	volume = {7},
	unique-id = {31126947},
	year = {2020},
	eissn = {2198-3844},
	orcid-numbers = {Hetényi, Anasztázia/0000-0001-8080-6992; Deli, Mária Anna/0000-0001-6084-6524; Czibula, Ágnes/0000-0003-4461-2773; Monostori, Éva/0000-0002-7442-3562; Martinek, Tamás/0000-0003-3168-8066}
}

@article{MTMT:30898978,
	title = {Melanoma-Derived Exosomes Induce PD-1 Overexpression and Tumor Progression via Mesenchymal Stem Cell Oncogenic Reprogramming},
	url = {https://m2.mtmt.hu/api/publication/30898978},
	author = {Gyukity-Sebestyén, Edina and Harmati, Mária and Dobra, Gabriella and Németh, István Balázs and Mihály, Johanna and Zvara, Ágnes and Hunyadi-Gulyás Éva, Csilla and Katona, Róbert László and Nagy, István and Horváth, Péter and Bálind, Árpád and Szkalisity, Ábel and Kovács, Mária and Pankotai, Tibor and Borsos, Barbara Nikolett and Erdélyi, Miklós and Szegletes, Zsolt and Veréb, Zoltán and Buzás, Edit Irén and Kemény, Lajos and Bíró, Tamás and Buzás, Krisztina},
	doi = {10.3389/fimmu.2019.02459},
	journal-iso = {FRONT IMMUNOL},
	journal = {FRONTIERS IN IMMUNOLOGY},
	volume = {10},
	unique-id = {30898978},
	issn = {1664-3224},
	abstract = {Recently, it has been described that programmed cell death protein 1 (PD-1) overexpressing melanoma cells are highly aggressive. However, until now it has not been defined which factors lead to the generation of PD-1 overexpressing subpopulations. Here, we present that melanoma-derived exosomes, conveying oncogenic molecular reprogramming, induce the formation of a melanoma-like, PD-1 overexpressing cell population (mMSCPD-1+) from naïve mesenchymal stem cells (MSCs). Exosomes and mMSCPD-1+ cells induce tumor progression and expression of oncogenic factors in vivo. Finally, we revealed a characteristic, tumorigenic signaling network combining the upregulated molecules (e.g., PD-1, MET, RAF1, BCL2, MTOR) and their upstream exosomal regulating proteins and miRNAs. Our study highlights the complexity of exosomal communication during tumor progression and contributes to the detailed understanding of metastatic processes.},
	keywords = {METASTASIS; stem cell; exosome; Reprogramming; PD-1; melanoma/tumor progression; signalization pattern},
	year = {2019},
	eissn = {1664-3224},
	orcid-numbers = {Gyukity-Sebestyén, Edina/0000-0003-1383-6301; Harmati, Mária/0000-0002-4875-5723; Dobra, Gabriella/0000-0002-2814-7720; Pankotai, Tibor/0000-0001-9810-5465; Erdélyi, Miklós/0000-0002-9501-5752; Szegletes, Zsolt/0000-0003-2202-6933; Veréb, Zoltán/0000-0002-9518-2155; Buzás, Edit Irén/0000-0002-3744-206X; Kemény, Lajos/0000-0002-2119-9501; Buzás, Krisztina/0000-0001-8933-2033}
}

@article{MTMT:30793391,
	title = {Seipin Facilitates Triglyceride Flow to Lipid Droplet and Counteracts Droplet Ripening via Endoplasmic Reticulum Contact},
	url = {https://m2.mtmt.hu/api/publication/30793391},
	author = {Salo, Veijo T. and Li, Shiqian and Vihinen, Helena and Holtta-Vuori, Maarit and Szkalisity, Ábel and Horváth, Péter and Belevich, Ilya and Peranen, Johan and Thiele, Christoph and Somerharju, Pentti and Zhao, Hongxia and Santinho, Alexandre and Thiam, Abdou Rachid and Jokitalo, Eija and Ikonen, Elina},
	doi = {10.1016/j.devcel.2019.05.016},
	journal-iso = {DEV CELL},
	journal = {DEVELOPMENTAL CELL},
	volume = {50},
	unique-id = {30793391},
	issn = {1534-5807},
	abstract = {Seipin is an oligomeric integral endoplasmic reticulum (ER) protein involved in lipid droplet (LD) biogenesis. To study the role of seipin in LD formation, we relocalized it to the nuclear envelope and found that LDs formed at these new seipin-defined sites. The sites were characterized by uniform seipin-mediated ER-LD necks. At low seipin content, LDs only grew at seipin sites, and tiny, growth-incompetent LDs appeared in a Rab18-dependent manner. When seipin was removed from ER-LD contacts within 1 h, no lipid metabolic defects were observed, but LDs became heterogeneous in size. Studies in seipin-ablated cells and model membranes revealed that this heterogeneity arises via a biophysical ripening process, with triglycerides partitioning from smaller to larger LDs through droplet-bilayer contacts. These results suggest that seipin supports the formation of structurally uniform ER-LD contacts and facilitates the delivery of triglycerides from ER to LDs. This counteracts ripening-induced shrinkage of small LDs.},
	year = {2019},
	eissn = {1878-1551},
	pages = {478-493},
	orcid-numbers = {Salo, Veijo T./0000-0002-6991-2142}
}

@article{MTMT:3318793,
	title = {Intelligent image-based in situ single-cell isolation},
	url = {https://m2.mtmt.hu/api/publication/3318793},
	author = {Braskó, Csilla and Smith, K and Molnár, Csaba and Faragó, Nóra and Hegedűs, Lili and Bálind, Árpád and Balassa, Tamás and Szkalisity, Ábel and Sükösd, Farkas and Kocsis, Ágnes Katalin and Bálint, Balázs and Paavolainen, L and Enyedi, Márton Zsolt and Nagy, István and Puskás, László and Haracska, Lajos and Tamás, Gábor and Horváth, Péter},
	doi = {10.1038/s41467-017-02628-4},
	journal-iso = {NAT COMMUN},
	journal = {NATURE COMMUNICATIONS},
	volume = {9},
	unique-id = {3318793},
	issn = {2041-1723},
	abstract = {Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.},
	year = {2018},
	eissn = {2041-1723},
	orcid-numbers = {Molnár, Csaba/0000-0002-6124-1209; Tamás, Gábor/0000-0002-7905-6001}
}

@article{MTMT:3247398,
	title = {Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data},
	url = {https://m2.mtmt.hu/api/publication/3247398},
	author = {Piccinini, F and Balassa, Tamás and Szkalisity, Ábel and Molnár, Csaba and Paavolainen, L and Kujala, K and Buzás, Krisztina and Sarazova, M and Pietiainen, V and Kutay, U and Smith, K and Horváth, Péter},
	doi = {10.1016/j.cels.2017.05.012},
	journal-iso = {CELL SYST},
	journal = {CELL SYSTEMS},
	volume = {4},
	unique-id = {3247398},
	issn = {2405-4712},
	abstract = {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.},
	year = {2017},
	eissn = {2405-4720},
	pages = {651-655},
	orcid-numbers = {Molnár, Csaba/0000-0002-6124-1209; Buzás, Krisztina/0000-0001-8933-2033}
}