TY - JOUR AU - Hetényi, Anasztázia AU - Imre, Norbert AU - Szabó, Enikő AU - Bodnár, Brigitta AU - Szkalisity, Ábel AU - Gróf, Ilona AU - Bocsik, Alexandra AU - Deli, Mária Anna AU - Horváth, Péter AU - Czibula, Ágnes AU - Monostori, Éva AU - Martinek, Tamás TI - Fehérje méretű molekulák humán sejtekbe juttatása lipid-raft mediált endocitózissal JF - BIOKÉMIA: A MAGYAR BIOKÉMIAI EGYESÜLET FOLYÓIRATA J2 - BIOKÉMIA VL - 45 PY - 2021 IS - 4 SP - 67 EP - 83 PG - 17 SN - 0133-8455 UR - https://m2.mtmt.hu/api/publication/32570862 ID - 32570862 N1 - Nincs jelölve levelező szerzőség a közleményen. (BÉ SZTE admin5) LA - Hungarian DB - MTMT ER - TY - JOUR AU - Szkalisity, Ábel AU - Piccinini, Filippo AU - Beleon, Attila AU - Balassa, Tamás AU - Varga, Gergely István AU - Migh, Ede AU - Molnár, Csaba AU - Paavolainen, Lassi AU - Timonen, Sanna AU - Banerjee, Indranil AU - Ikonen, Elina AU - Yamauchi, Yohei AU - Andó, István AU - Peltonen, Jaakko AU - Pietiäinen, Vilja AU - Honti, Viktor AU - Horváth, Péter TI - Regression plane concept for analysing continuous cellular processes with machine learning JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 12 PY - 2021 IS - 1 PG - 9 SN - 2041-1723 DO - 10.1038/s41467-021-22866-x UR - https://m2.mtmt.hu/api/publication/32023128 ID - 32023128 N1 - Cited By :2 Export Date: 14 June 2022 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 - Imre, Norbert AU - Hetényi, Anasztázia AU - Szabó, Enikő AU - Bodnár, Brigitta AU - Szkalisity, Ábel AU - Gróf, Ilona AU - Bocsik, Alexandra AU - Deli, Mária Anna AU - Horváth, Péter AU - Czibula, Ágnes AU - Monostori, Éva AU - Martinek, Tamás TI - Routing Nanomolar Protein Cargoes to Lipid Raft‐Mediated/Caveolar Endocytosis through a Ganglioside GM1‐Specific Recognition Tag JF - ADVANCED SCIENCE J2 - ADV SCI VL - 7 PY - 2020 IS - 4 PG - 10 SN - 2198-3844 DO - 10.1002/advs.201902621 UR - https://m2.mtmt.hu/api/publication/31126947 ID - 31126947 N1 - Funding text: This research was funded by the National Research, Development and Innovation Office of Hungary, grant number GINOP-2.2.1-15-2016-00007, the Hungarian Ministry of Innovation and Technology, TUDFO/47138-1/2019-ITM, and Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG). T.A.M. acknowledges support from the Hungarian Academy of Sciences LENDULET-Foldamer. P.H. acknowledges support from the Finnish TEKES FiDiPro Fellow Grant 40294/13, the Hungarian Academy of Sciences LENDULET-Biomag. LA - English DB - MTMT ER - TY - JOUR AU - Gyukity-Sebestyén, Edina AU - Harmati, Mária AU - Dobra, Gabriella AU - Németh, István Balázs AU - Mihály, Johanna AU - Zvara, Ágnes AU - Hunyadi-Gulyás Éva, Csilla AU - Katona, Róbert László AU - Nagy, István AU - Horváth, Péter AU - Bálind, Árpád AU - Szkalisity, Ábel AU - Kovács, Mária AU - Pankotai, Tibor AU - Borsos, Barbara Nikolett AU - Erdélyi, Miklós AU - Szegletes, Zsolt AU - Veréb, Zoltán AU - Buzás, Edit Irén AU - Kemény, Lajos AU - Bíró, Tamás AU - Buzás, Krisztina TI - Melanoma-Derived Exosomes Induce PD-1 Overexpression and Tumor Progression via Mesenchymal Stem Cell Oncogenic Reprogramming JF - FRONTIERS IN IMMUNOLOGY J2 - FRONT IMMUNOL VL - 10 PY - 2019 PG - 22 SN - 1664-3224 DO - 10.3389/fimmu.2019.02459 UR - https://m2.mtmt.hu/api/publication/30898978 ID - 30898978 N1 - Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Doctoral School of Interdisciplinary Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary Department of Immunology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomics Research, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Artificial Chromosome and Stem Cell Research Laboratory, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Sequencing Platform, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Advanced Optical Imaging Group, Department of Optics and Quantum Electronics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Atomic Force Microscope Laboratory, Institute of Biophysics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary MTA-SE Immuno-proteogenomics Extracellular Vesicle Research Group, Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Szeged, Hungary Export Date: 14 January 2020 Correspondence Address: Buzás, K.; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of SciencesHungary; email: kr.buzas@gmail.com Chemicals/CAS: aggrecanase 1, 147172-61-0; alpha tubulin, 78769-62-7; carbonate dehydratase, 9001-03-0; cathepsin B, 9047-22-7; cathepsin D, 9025-26-7; lipocortin 5, 111237-10-6; mitogen activated protein kinase kinase, 142805-58-1; protein bcl 2, 219306-68-0; protein kinase B, 148640-14-6 Funding details: GINOP-2.2.1-15-2017-00052, 11493, GINOP-2.3.2-15-2016-00015 Funding details: NKP-19-4 Funding details: GINOP-2.3.2-15-2016-00036, KTIA_13_NAP-A-I/14 Funding text 1: The authors wish to thank G?bor Braunitzer and Csaba Vizler, for scientific discussion, Annam?ria Marton for technical issues and adaptation of MSC isolation protocol, Lilla Pint?r for technical support. Funding. This work was in part supported by the National Research, Development and Innovation Fund of Hungary, financed under the NKFI-6-K funding scheme (11493 project), GINOP-2.3.2-15-2016-00015, GINOP-2.2.1-15-2017-00052, J?nos Bolyai Research Scholarship of the Hungarian Academy of Sciences ?NKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology and University of Szeged Open Access Fund. The dSTORM experiments were funded by the Hungarian Brain Research Program (KTIA_13_NAP-A-I/14) and the GINOP-2.3.2-15-2016-00036. Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Doctoral School of Interdisciplinary Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary Department of Immunology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomics Research, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Artificial Chromosome and Stem Cell Research Laboratory, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Sequencing Platform, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Advanced Optical Imaging Group, Department of Optics and Quantum Electronics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Atomic Force Microscope Laboratory, Institute of Biophysics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary MTA-SE Immuno-proteogenomics Extracellular Vesicle Research Group, Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Szeged, Hungary Export Date: 17 January 2020 Correspondence Address: Buzás, K.; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of SciencesHungary; email: kr.buzas@gmail.com Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Doctoral School of Interdisciplinary Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary Department of Immunology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomics Research, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Artificial Chromosome and Stem Cell Research Laboratory, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Sequencing Platform, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Advanced Optical Imaging Group, Department of Optics and Quantum Electronics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Atomic Force Microscope Laboratory, Institute of Biophysics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary MTA-SE Immuno-proteogenomics Extracellular Vesicle Research Group, Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Szeged, Hungary Export Date: 28 January 2020 Correspondence Address: Buzás, K.; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of SciencesHungary; email: kr.buzas@gmail.com Chemicals/CAS: aggrecanase 1, 147172-61-0; alpha tubulin, 78769-62-7; carbonate dehydratase, 9001-03-0; cathepsin B, 9047-22-7; cathepsin D, 9025-26-7; lipocortin 5, 111237-10-6; mitogen activated protein kinase kinase, 142805-58-1; protein bcl 2, 219306-68-0; protein kinase B, 148640-14-6 Funding details: GINOP-2.2.1-15-2017-00052, 11493, GINOP-2.3.2-15-2016-00015 Funding details: NKP-19-4 Funding details: GINOP-2.3.2-15-2016-00036, KTIA_13_NAP-A-I/14 Funding text 1: The authors wish to thank G?bor Braunitzer and Csaba Vizler, for scientific discussion, Annam?ria Marton for technical issues and adaptation of MSC isolation protocol, Lilla Pint?r for technical support. Funding. This work was in part supported by the National Research, Development and Innovation Fund of Hungary, financed under the NKFI-6-K funding scheme (11493 project), GINOP-2.3.2-15-2016-00015, GINOP-2.2.1-15-2017-00052, J?nos Bolyai Research Scholarship of the Hungarian Academy of Sciences ?NKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology and University of Szeged Open Access Fund. The dSTORM experiments were funded by the Hungarian Brain Research Program (KTIA_13_NAP-A-I/14) and the GINOP-2.3.2-15-2016-00036. Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Doctoral School of Interdisciplinary Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary Department of Immunology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomics Research, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Artificial Chromosome and Stem Cell Research Laboratory, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Sequencing Platform, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Advanced Optical Imaging Group, Department of Optics and Quantum Electronics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Atomic Force Microscope Laboratory, Institute of Biophysics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary MTA-SE Immuno-proteogenomics Extracellular Vesicle Research Group, Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Szeged, Hungary Export Date: 30 January 2020 Correspondence Address: Buzás, K.; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of SciencesHungary; email: kr.buzas@gmail.com Funding Agency and Grant Number: National Research, Development and Innovation Fund of Hungary under the NKFI-6-K funding scheme [11493, GINOP-2.3.2-15-2016-00015, GINOP-2.2.1-152017-00052]; Janos Bolyai Research Scholarship of the Hungarian Academy of SciencesHungarian Academy of Sciences [UNKP-19-4]; University of Szeged; Hungarian Brain Research Program [KTIA_13_NAP-A-I/14]; [GINOP-2.3.2-15-2016-00036] Funding text: This work was in part supported by the National Research, Development and Innovation Fund of Hungary, financed under the NKFI-6-K funding scheme (11493 project), GINOP-2.3.2-15-2016-00015, GINOP-2.2.1-152017-00052, Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences UNKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology and University of Szeged Open Access Fund. The dSTORM experiments were funded by the Hungarian Brain Research Program (KTIA_13_NAP-A-I/14) and the GINOP-2.3.2-15-2016-00036. Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Doctoral School of Interdisciplinary Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary Department of Immunology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomics Research, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Artificial Chromosome and Stem Cell Research Laboratory, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Sequencing Platform, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Advanced Optical Imaging Group, Department of Optics and Quantum Electronics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Atomic Force Microscope Laboratory, Institute of Biophysics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary MTA-SE Immuno-proteogenomics Extracellular Vesicle Research Group, Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Szeged, Hungary Cited By :2 Export Date: 3 March 2020 Correspondence Address: Buzás, K.; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of SciencesHungary; email: kr.buzas@gmail.com Chemicals/CAS: aggrecanase 1, 147172-61-0; alpha tubulin, 78769-62-7; carbonate dehydratase, 9001-03-0; cathepsin B, 9047-22-7; cathepsin D, 9025-26-7; lipocortin 5, 111237-10-6; mitogen activated protein kinase kinase, 142805-58-1; protein bcl 2, 219306-68-0; protein kinase B, 148640-14-6 Funding details: GINOP-2.2.1-15-2017-00052, 11493, GINOP-2.3.2-15-2016-00015 Funding details: NKP-19-4 Funding details: GINOP-2.3.2-15-2016-00036, KTIA_13_NAP-A-I/14 Funding text 1: The authors wish to thank G?bor Braunitzer and Csaba Vizler, for scientific discussion, Annam?ria Marton for technical issues and adaptation of MSC isolation protocol, Lilla Pint?r for technical support. Funding. This work was in part supported by the National Research, Development and Innovation Fund of Hungary, financed under the NKFI-6-K funding scheme (11493 project), GINOP-2.3.2-15-2016-00015, GINOP-2.2.1-15-2017-00052, J?nos Bolyai Research Scholarship of the Hungarian Academy of Sciences ?NKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology and University of Szeged Open Access Fund. The dSTORM experiments were funded by the Hungarian Brain Research Program (KTIA_13_NAP-A-I/14) and the GINOP-2.3.2-15-2016-00036. Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Doctoral School of Interdisciplinary Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary Department of Immunology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomics Research, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Artificial Chromosome and Stem Cell Research Laboratory, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Sequencing Platform, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Advanced Optical Imaging Group, Department of Optics and Quantum Electronics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Atomic Force Microscope Laboratory, Institute of Biophysics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary MTA-SE Immuno-proteogenomics Extracellular Vesicle Research Group, Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Szeged, Hungary Cited By :9 Export Date: 10 January 2021 Correspondence Address: Buzás, K.; Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of SciencesHungary; email: kr.buzas@gmail.com Chemicals/CAS: aggrecanase 1, 147172-61-0; alpha tubulin, 78769-62-7; carbonate dehydratase, 9001-03-0; cathepsin B, 9047-22-7; cathepsin D, 9025-26-7; lipocortin 5, 111237-10-6; mitogen activated protein kinase kinase, 142805-58-1; protein bcl 2, 219306-68-0; protein kinase B, 148640-14-6; Programmed Cell Death 1 Receptor Funding details: Magyar Tudományos Akadémia, MTA Funding details: GINOP-2.2.1-15-2017-00052, 11493, GINOP-2.3.2-15-2016-00015, NKFI-6-K Funding details: GINOP-2.3.2-15-2016-00036, KTIA_13_NAP-A-I/14 Funding text 1: This work was in part supported by the National Research, Development and Innovation Fund of Hungary, financed under the NKFI-6-K funding scheme (11493 project), GINOP-2.3.2-15-2016-00015, GINOP-2.2.1-15-2017-00052, János Bolyai Research Scholarship of the Hungarian Academy of Sciences ÚNKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology and University of Szeged Open Access Fund. The dSTORM experiments were funded by the Hungarian Brain Research Program (KTIA_13_NAP-A-I/14) and the GINOP-2.3.2-15-2016-00036. Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Doctoral School of Interdisciplinary Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary Department of Immunology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomics Research, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Artificial Chromosome and Stem Cell Research Laboratory, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Sequencing Platform, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Advanced Optical Imaging Group, Department of Optics and Quantum Electronics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Atomic Force Microscope Laboratory, Institute of Biophysics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary MTA-SE Immuno-proteogenomics Extracellular Vesicle Research Group, Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Szeged, Hungary Cited By :9 Export Date: 10 February 2021 Correspondence Address: Buzás, K.; Laboratory of Microscopic Image Analysis and Machine Learning, Hungary; email: kr.buzas@gmail.com Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Doctoral School of Interdisciplinary Sciences, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary Department of Immunology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary Laboratory of Functional Genomics, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Laboratory of Proteomics Research, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Artificial Chromosome and Stem Cell Research Laboratory, Institute of Genetics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Sequencing Platform, Institute of Biochemistry, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary Department of Pharmacology and Pharmacotherapy, Faculty of Medicine, University of Szeged, Szeged, Hungary Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Advanced Optical Imaging Group, Department of Optics and Quantum Electronics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary Atomic Force Microscope Laboratory, Institute of Biophysics, Biological Research Centre of Hungarian Academy of Sciences, Szeged, Hungary MTA-SE Immuno-proteogenomics Extracellular Vesicle Research Group, Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Budapest, Hungary Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, Szeged, Hungary Cited By :9 Export Date: 10 March 2021 Correspondence Address: Buzás, K.; Laboratory of Microscopic Image Analysis and Machine Learning, Hungary; email: kr.buzas@gmail.com Chemicals/CAS: aggrecanase 1, 147172-61-0; alpha tubulin, 78769-62-7; carbonate dehydratase, 9001-03-0; cathepsin B, 9047-22-7; cathepsin D, 9025-26-7; lipocortin 5, 111237-10-6; mitogen activated protein kinase kinase, 142805-58-1; protein bcl 2, 219306-68-0; protein kinase B, 148640-14-6; Programmed Cell Death 1 Receptor Funding details: NKP-19-4 Funding details: GINOP-2.3.2-15-2016-00036, KTIA_13_NAP-A-I/14 Funding details: 11493, GINOP-2.2.1-15-2017-00052, GINOP-2.3.2-15-2016-00015, NKFI-6-K Funding details: Magyar Tudományos Akadémia, MTA Funding text 1: The authors wish to thank G?bor Braunitzer and Csaba Vizler, for scientific discussion, Annam?ria Marton for technical issues and adaptation of MSC isolation protocol, Lilla Pint?r for technical support. Funding. This work was in part supported by the National Research, Development and Innovation Fund of Hungary, financed under the NKFI-6-K funding scheme (11493 project), GINOP-2.3.2-15-2016-00015, GINOP-2.2.1-15-2017-00052, J?nos Bolyai Research Scholarship of the Hungarian Academy of Sciences ?NKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology and University of Szeged Open Access Fund. The dSTORM experiments were funded by the Hungarian Brain Research Program (KTIA_13_NAP-A-I/14) and the GINOP-2.3.2-15-2016-00036. Funding text 2: This work was in part supported by the National Research, Development and Innovation Fund of Hungary, financed under the NKFI-6-K funding scheme (11493 project), GINOP-2.3.2-15-2016-00015, GINOP-2.2.1-15-2017-00052, János Bolyai Research Scholarship of the Hungarian Academy of Sciences ÚNKP-19-4 New National Excellence Program of the Ministry for Innovation and Technology and University of Szeged Open Access Fund. The dSTORM experiments were funded by the Hungarian Brain Research Program (KTIA_13_NAP-A-I/14) and the GINOP-2.3.2-15-2016-00036. AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Salo, Veijo T. AU - Li, Shiqian AU - Vihinen, Helena AU - Holtta-Vuori, Maarit AU - Szkalisity, Ábel AU - Horváth, Péter AU - Belevich, Ilya AU - Peranen, Johan AU - Thiele, Christoph AU - Somerharju, Pentti AU - Zhao, Hongxia AU - Santinho, Alexandre AU - Thiam, Abdou Rachid AU - Jokitalo, Eija AU - Ikonen, Elina TI - Seipin Facilitates Triglyceride Flow to Lipid Droplet and Counteracts Droplet Ripening via Endoplasmic Reticulum Contact JF - DEVELOPMENTAL CELL J2 - DEV CELL VL - 50 PY - 2019 IS - 4 SP - 478 EP - 493 PG - 16 SN - 1534-5807 DO - 10.1016/j.devcel.2019.05.016 UR - https://m2.mtmt.hu/api/publication/30793391 ID - 30793391 N1 - Funding Agency and Grant Number: Academy of Finland [282192, 307415, l312491, 1287975]; LENDULET-BIOMAG [2018342]; European Regional Development Funds [GINOP-2.3.2-15-2016-00006]; QLife program; Paris Sciences et Lettres installation grant; ANR-NanoDrop project [ANR-17-CE11-0003]; Mobil grant [ANR-18-CE11-0012-01]; Finnish Medical Foundation; Paulo Foundation; Alfred Kordelin Foundation; Maud Kuistila Foundation; Biomedicum Helsinki Foundation; Emil Aaltonen Foundation Funding text: We thank Mervi Lindman, Anna Uro and Katharina Ven for excellent technical assistance. We thank Biocenter Finland and HiLIFE Light and Electron microscopy, Functional genomics and Flow cytometry units. This study was supported by the Academy of Finland (grants 282192, 307415, and l312491 to E.I. and 1287975 to E.J.); LENDULET-BIOMAG grant (2018342 to P.H. and A. Szkalisity); European Regional Development Funds (GINOP-2.3.2-15-2016-00006 to P.H. and A. Szkalisity), QLife program (to A. Santinho); Paris Sciences et Lettres installation grant (to A.R.T.); ANR-NanoDrop project (ANR-17-CE11-0003 to A.R.T.); and Mobil grant (ANR-18-CE11-0012-01 to A.R.T.). V.T.S. acknowledges support from the Finnish Medical, Paulo, Alfred Kordelin, Maud Kuistila, Biomedicum Helsinki, and Emil Aaltonen Foundation. AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Braskó, Csilla AU - Smith, K AU - Molnár, Csaba AU - Faragó, Nóra AU - Hegedűs, Lili AU - Bálind, Árpád AU - Balassa, Tamás AU - Szkalisity, Ábel AU - Sükösd, Farkas AU - Kocsis, Ágnes Katalin AU - Bálint, Balázs AU - Paavolainen, L AU - Enyedi, Márton Zsolt AU - Nagy, István AU - Puskás, László AU - Haracska, Lajos AU - Tamás, Gábor AU - Horváth, Péter TI - Intelligent image-based in situ single-cell isolation JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 9 PY - 2018 IS - 1 PG - 7 SN - 2041-1723 DO - 10.1038/s41467-017-02628-4 UR - https://m2.mtmt.hu/api/publication/3318793 ID - 3318793 N1 - University of Szeged, Hungary Közép fasor 52, Szeged, 6726, Hungary School of Computer Science and Communication, KTH Royal Institute of Technology, Lindstedtsvägen 3-5, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17121, Sweden Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62., Szeged, 6726, Hungary Avidin Biotechnology Ltd, Alsó Kiköto sor 11, Szeged, 6726, Hungary SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom, 6782, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland Cited By :17 Export Date: 26 August 2019 Correspondence Address: Horvath, P.; Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62., Hungary; email: horvath.peter@brc.mta.hu Funding details: VKSZ-14-1-2015-0155 Funding details: Magyar Tudományos Akadémia, MTA Funding details: Tekes, 40294/13 Funding text 1: A.B., C.B., T.B. and P.H. acknowledge the Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG). P.H. and L.P. acknowledge support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. N.F. and G.T. were supported by the National Research, Development and Innovation Office of Hungary (VKSZ-14-1-2015-0155), the Hungarian Academy of Sciences and the ERC INTERIMPACT project. P.H. and I.N. were supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. L.H. and P.H. acknowledge the European Union and the European Regional Development Funds (GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00020). University of Szeged, Hungary Közép fasor 52, Szeged, 6726, Hungary School of Computer Science and Communication, KTH Royal Institute of Technology, Lindstedtsvägen 3-5, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17121, Sweden Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62., Szeged, 6726, Hungary Avidin Biotechnology Ltd, Alsó Kiköto sor 11, Szeged, 6726, Hungary SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom, 6782, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland Cited By :19 Export Date: 28 January 2020 Correspondence Address: Horvath, P.; Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62., Hungary; email: horvath.peter@brc.mta.hu Funding details: VKSZ-14-1-2015-0155 Funding details: Magyar Tudományos Akadémia, MTA Funding details: Tekes, 40294/13 Funding text 1: A.B., C.B., T.B. and P.H. acknowledge the Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG). P.H. and L.P. acknowledge support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. N.F. and G.T. were supported by the National Research, Development and Innovation Office of Hungary (VKSZ-14-1-2015-0155), the Hungarian Academy of Sciences and the ERC INTERIMPACT project. P.H. and I.N. were supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. L.H. and P.H. acknowledge the European Union and the European Regional Development Funds (GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00020). Funding Agency and Grant Number: Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG); Finnish TEKES FiDiPro [40294/13]; National Research, Development and Innovation Office of Hungary [VKSZ-14-1-2015-0155]; Hungarian Academy of SciencesHungarian Academy of Sciences; ERC INTERIMPACT; European UnionEuropean Union (EU); European Regional Development FundsEuropean Union (EU) [GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00020] Funding text: A.B., C.B., T.B. and P.H. acknowledge the Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG). P.H. and L.P. acknowledge support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. N.F. and G.T. were supported by the National Research, Development and Innovation Office of Hungary (VKSZ-14-1-2015-0155), the Hungarian Academy of Sciences and the ERC INTERIMPACT project. P.H. and I.N. were supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences. L.H. and P. H. acknowledge the European Union and the European Regional Development Funds (GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00020). University of Szeged, Hungary Közép fasor 52, Szeged, 6726, Hungary School of Computer Science and Communication, KTH Royal Institute of Technology, Lindstedtsvägen 3-5, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17121, Sweden Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62., Szeged, 6726, Hungary Avidin Biotechnology Ltd, Alsó Kiköto sor 11, Szeged, 6726, Hungary SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom, 6782, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland Cited By :26 Export Date: 28 August 2020 Correspondence Address: Horvath, P.; Biological Research Centre, Hungarian Academy of Sciences, Temesvári krt. 62., Hungary; email: horvath.peter@brc.mta.hu Funding details: VKSZ-14-1-2015-0155 Funding details: Magyar Tudományos Akadémia, MTA Funding details: European Commission, EC Funding details: European Research Council, ERC Funding details: GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00020 Funding details: 40294/13 Funding text 1: A.B., C.B., T.B. and P.H. acknowledge the Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG). P.H. and L.P. acknowledge support from the Finnish TEKES FiDiPro Fellow Grant 40294/13. N.F. and G.T. were supported by the National Research, Development and Innovation Office of Hungary (VKSZ-14-1-2015-0155), the Hungarian Academy of Sciences and the ERC INTERIMPACT project. P.H. and I.N. were supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. L.H. and P.H. acknowledge the European Union and the European Regional Development Funds (GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00020). AB - 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. 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 - Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) S.r.l., IRCCS, Via Piero Maroncelli 40, Meldola (FC), 47014, Italy Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári körút 62, Szeged, 6726, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland University of Szeged, Faculty of Dentistry, Tisza Lajos körút 64, Szeged, 6720, Hungary Insitute of Biochemistry, ETH Zurich, Otto-Stern-Weg 3, Zurich, 8093, Switzerland KTH Royal Institute of Technology, School of Computer Science and Communication, Lindstedtsvägen 3, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden Cited By :12 Export Date: 14 January 2020 Correspondence Address: Horvath, P.; Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári körút 62, Hungary; email: horvath.peter@brc.mta.hu Funding details: Federation of European Biochemical Societies, FEBS Funding details: MTA-SE-NAP B-BIOMAG Funding details: Federation of European Biochemical Societies, FEBS Funding details: European Commission, EC Funding details: GINOP-2.3.2-15-2016-00037, GINOP-2.3.2-15-2016-00026 Funding details: Tekes, 40294/13 Funding text 1: The authors wish to thank Beverley Isherwood, Neil Carragher, and Anne E. Carpenter for the information they provided about the HCS image dataset; ETH Zurich Microscopy Center (ScopeM) for HCS imaging; Arpad Balind for suggestions and feedback; Gabriella Tick and Máté Görbe for their help with the documentation; Dóra Bokor for proofreading the manuscript. F.P. was supported by a Short-Term Fellowship awarded by the Federation of European Biochemical Societies ( FEBS ). F.P., B.T., CS.M., A.SZ., and P.H. acknowledge support from the Hungarian National Brain Research Program (MTA-SE-NAP B-BIOMAG). U.K. received support from the SNF ( 313003A_166565 ). L.P., V.P., and P.H. acknowledge support from the Finnish TEKES FiDiPro Fellow Grant 40294/13 and European Union and the European Regional Development Funds ( GINOP-2.3.2-15-2016-00026 , GINOP-2.3.2-15-2016-00037 ). Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) S.r.l., IRCCS, Via Piero Maroncelli 40, Meldola (FC), 47014, Italy Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári körút 62, Szeged, 6726, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, Helsinki, 00014, Finland University of Szeged, Faculty of Dentistry, Tisza Lajos körút 64, Szeged, 6720, Hungary Insitute of Biochemistry, ETH Zurich, Otto-Stern-Weg 3, Zurich, 8093, Switzerland KTH Royal Institute of Technology, School of Computer Science and Communication, Lindstedtsvägen 3, Stockholm, 10044, Sweden Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden Cited By :12 Export Date: 17 February 2020 Correspondence Address: Horvath, P.; Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári körút 62, Hungary; email: horvath.peter@brc.mta.hu 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 -