TY - THES AU - Varga, Dániel TI - Lokalizációs mikroszkópiás mérések kvantitatív elemzése [Quantitative analysis of superresolution localization microscopy measurements] PB - Szegedi Tudományegyetem PY - 2023 SP - 113 DO - 10.14232/phd.11283 UR - https://m2.mtmt.hu/api/publication/34131654 ID - 34131654 AB - Célom volt, hogy értelmezzem és kvantitatív információkat nyerjek ki az SMLM technikával kapott pontfelhőkből, különös tekintettel a megjelölt molekulák számára, a klasztereződés kimutatására valamint a klaszterek és a minta geometriai paramétereinek jellemzésére. Ter- veim között szerepelt, hogy az eredményeket összevessem a biológiai kutatásokban széles körben elterjedt konfokális mikroszkópiás (CLSM) felvételek elemzéséből kapott eredményekkel, feltérképezve a korrelatív mérések lehetőségeit. A mikroszkópiás mérések elvégzése szintén a feladataim között szerepelt. Célul tűztem ki, hogy az alkalmazott analitikai módszerek ne bonyolítsák a mérési protokollt, valamint ne 4 növeljék meg a mérési időt. Célkitűzéseimet olyan fontos biológiai problémák motiválták, mint a DNS kettős szálú töréseket jelező γH2AX hisztonok számának kvan- titatív megbecslése, a γH2AX klaszterek időbontott vizsgálata röntgen besugárzás után, az aktin szálak szerkezetében bekövetkező változások, vagy RNC klaszterek kvantitatív jellemzése genotoxikus stressz hatás után. A kísérletek elvégzéséhez az AdOptIm kutatócsoport dSTORM és CLSM rendszerét használtam. Az adatok számítógépes elemzése so- rán MATLAB-ot, illetve Python programozási nyelvet használtam. LA - Hungarian DB - MTMT ER - TY - JOUR AU - Szatmári, Orsolya AU - Nagy-Mikó, Bence AU - Györkei, Ádám AU - Varga, Dániel AU - H. Kovács, Bálint Barna AU - Igaz, Nóra AU - Bognár, Bence AU - Rázga, Zsolt AU - Nagy, Gábor AU - Zsindely, Nóra AU - Bodai, László AU - Papp, Balázs AU - Erdélyi, Miklós AU - Csontné Kiricsi, Mónika AU - Blastyák, András AU - Collart, Martine A AU - Boros, Imre Miklós AU - Villanyi, Zoltan TI - Phase-separated ribosome-nascent chain complexes in genotoxic stress response JF - RNA-A PUBLICATION OF THE RNA SOCIETY J2 - RNA VL - 29 PY - 2023 IS - 10 SP - 1557 EP - 1574 PG - 18 SN - 1355-8382 DO - 10.1261/rna.079755.123 UR - https://m2.mtmt.hu/api/publication/34067381 ID - 34067381 N1 - Department of Biochemistry and Molecular Biology, University of Szeged, Szeged, 6726, Hungary Institute of Biochemistry, Biological Research Centre, Szeged, 6726, Hungary Section for Physiology and Cell Biology, Department of Biosciences, University of Oslo, Oslo, 0316, Norway Department of Optics and Quantum Electronics, University of Szeged, Szeged, 6720, Hungary Department of Pathology, Faculty of Medicine, University of Szeged, Szeged, 6720, Hungary Institute of Genetics, Biological Research Centre, Szeged, 6726, Hungary Department of Microbiology and Molecular Medicine, Institute of Genetics and Genomics Geneva, Faculty of Medicine, University of Geneva, Geneva 4, 1211, Switzerland Cited By :1 Export Date: 12 December 2023 CODEN: RNARF Correspondence Address: Villányi, Z.; Department of Biochemistry and Molecular Biology, Hungary; email: villanyi.zoltan@bio.u-szeged.hu Chemicals/CAS: 1,6 hexanediol, 629-11-8; DNA helicase; edetic acid, 150-43-6, 60-00-4; ribonuclease, 59794-03-5, 9001-99-4; transcriptional regulator ATRX; Werner syndrome ATP dependent helicase; RNA, 63231-63-0; Edetic Acid; RecQ Helicases; Ribonucleoproteins; RNA; Saccharomyces cerevisiae Proteins; SGS1 protein, S cerevisiae Funding details: 31003A_172999, NTP-NFTÖ-20-B-0354 Funding details: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF Funding details: Magyar Tudományos Akadémia, MTA, BO/00878/19/8, BO/902/19, TKP2021-NVA-19 Funding details: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFI Funding text 1: We are grateful to Dr. Balázs Vedelek and Dr. Zsuzsa Sarkadi for valuable discussions. We thank Jawad Iqbal, Elvira Czvik, Zita Kóra, and Edina Pataki for technical assistance. We are grateful to Blanka Léhy for the graphical abstract. This work was supported by grants GINOP-2.3.2-15-2016-00020 and GINOP-2.3.2-15-2016-00038, as well as by NKFI-K 142961 (Z.V.), ÚNKP-21-5-595-SZTE (Z.V.), and ÚNKP-20-5-SZTE-655 (M.K.) from the Hungarian National Research, Development and Innovation Office. Further support was provided by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences (BO/902/19 for Z.V. and BO/00878/19/8 for M.K.). Superresolu-tion dSTORM experiments and their evaluation were funded by the Hungarian National Research, Development and Innovation Office (TKP2021-NVA-19), Hungarian Brain Research Program (2017-1.2.1-NKP-2017-00002) awarded to M.E., and NTP-NFTÖ-20-B-0354 awarded to D.V., as well as grant 31003A_172999 from the Swiss National Science Foundation awarded to M.A.C. AB - Assemblysomes are EDTA- and RNase-resistant ribonucleoprotein (RNP) complexes of paused ribosomes with protruding nascent polypeptide chains. They have been described in yeast and human cells for the proteasome subunit Rpt1, and the disordered N-terminal part of the nascent chain was found to be indispensable for the accumulation of the Rpt1-RNP into assemblysomes. Motivated by this, to find other assemblysome-associated RNPs we used bioinformatics to rank subunits of Saccharomyces cerevisiae protein complexes according to their N-terminal disorder propensity. The results revealed that gene products involved in DNA repair are enriched among the top candidates. The Sgs1 DNA helicase was chosen for experimental validation. We found that indeed nascent chains of Sgs1 form EDTA-resistant RNP condensates, assemblysomes by definition. Moreover, upon exposure to UV, SGS1 mRNA shifted from assemblysomes to polysomes, suggesting that external stimuli are regulators of assemblysome dynamics. We extended our studies to human cell lines. The BLM helicase, ortholog of yeast Sgs1, was identified upon sequencing assemblysome-associated RNAs from the MCF7 human breast cancer cell line, and mRNAs encoding DNA repair proteins were overall enriched. Using the radiation-resistant A549 cell line, we observed by transmission electron microscopy that 1,6-hexanediol, an agent known to disrupt phase-separated condensates, depletes ring ribosome structures compatible with assemblysomes from the cytoplasm of cells and makes the cells more sensitive to X-ray treatment. Taken together these findings suggest that assemblysomes may be a component of the DNA damage response from yeast to human. LA - English DB - MTMT ER - TY - JOUR AU - Varga, Dániel AU - Szikora, Szilárd AU - Novák, Tibor AU - Pap, Gergely AU - Lékó, Gábor AU - Mihály, József AU - Erdélyi, Miklós TI - Machine learning framework to segment sarcomeric structures in SMLM data JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 13 PY - 2023 IS - 1 PG - 10 SN - 2045-2322 DO - 10.1038/s41598-023-28539-7 UR - https://m2.mtmt.hu/api/publication/33708203 ID - 33708203 N1 - Funding Agency and Grant Number: University of Szeged Funding text: Open access funding provided by University of Szeged. AB - Object detection is an image analysis task with a wide range of applications, which is difficult to accomplish with traditional programming. Recent breakthroughs in machine learning have made significant progress in this area. However, these algorithms are generally compatible with traditional pixelated images and cannot be directly applied for pointillist datasets generated by single molecule localization microscopy (SMLM) methods. Here, we have improved the averaging method developed for the analysis of SMLM images of sarcomere structures based on a machine learning object detection algorithm. The ordered structure of sarcomeres allows us to determine the location of the proteins more accurately by superimposing SMLM images of identically assembled proteins. However, the area segmentation process required for averaging can be extremely time-consuming and tedious. In this work, we have automated this process. The developed algorithm not only finds the regions of interest, but also classifies the localizations and identifies the true positive ones. For training, we used simulations to generate large amounts of labelled data. After tuning the neural network’s internal parameters, it could find the localizations associated with the structures we were looking for with high accuracy. We validated our results by comparing them with previous manual evaluations. It has also been proven that the simulations can generate data of sufficient quality for training. Our method is suitable for the identification of other types of structures in SMLM data. LA - English DB - MTMT ER - TY - JOUR AU - Szegedi, Viktor AU - Bakos , Emőke AU - Furdan, Szabina AU - H. Kovács, Bálint Barna AU - Varga, Dániel AU - Erdélyi, Miklós AU - Barzó, Pál AU - Szűcs, Attila AU - Tamás, Gábor AU - Lamsa, Karri TI - HCN channels at the cell soma ensure the rapid electrical reactivity of fast-spiking interneurons in human neocortex JF - PLOS BIOLOGY J2 - PLOS BIOL VL - 21 PY - 2023 IS - 2 PG - 33 SN - 1544-9173 DO - 10.1371/journal.pbio.3002001 UR - https://m2.mtmt.hu/api/publication/33644822 ID - 33644822 N1 - Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary Hungarian Centre of Excellence for Molecular Medicine Research Group for Human neuron physiology and therapy, Szeged, Hungary Department of Optics and Quantum Electronics, University of Szeged, Szeged, Hungary Department of Neurosurgery, University of Szeged, Szeged, Hungary Neuronal Cell Biology Research Group, Eötvös Loránd University, Budapest, Budapest, Hungary MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy and Neuroscience, University of Szeged, Szeged, Hungary Export Date: 26 May 2023 CODEN: PBLIB Correspondence Address: Lamsa, K.; Department of Physiology, Hungary; email: klamsa@bio.u-szeged.hu AB - Accumulating evidence indicates that there are substantial species differences in the properties of mammalian neurons, yet theories on circuit activity and information processing in the human brain are based heavily on results obtained from rodents and other experimental animals. This knowledge gap may be particularly important for understanding the neocortex, the brain area responsible for the most complex neuronal operations and showing the greatest evolutionary divergence. Here, we examined differences in the electrophysiological properties of human and mouse fast-spiking GABAergic basket cells, among the most abundant inhibitory interneurons in cortex. Analyses of membrane potential responses to current input, pharmacologically isolated somatic leak currents, isolated soma outside-out patch recordings, and immunohistochemical staining revealed that human neocortical basket cells abundantly express hyperpolarization-activated cyclic nucleotide-gated cation (HCN) channel isoforms HCN1 and HCN2 at the cell soma membrane, whereas these channels are sparse at the rodent basket cell soma membrane. Antagonist experiments showed that HCN channels in human neurons contribute to the resting membrane potential and cell excitability at the cell soma, accelerate somatic membrane potential kinetics, and shorten the lag between excitatory postsynaptic potentials and action potential generation. These effects are important because the soma of human fast-spiking neurons without HCN channels exhibit low persistent ion leak and slow membrane potential kinetics, compared with mouse fast-spiking neurons. HCN channels speed up human cell membrane potential kinetics and help attain an input–output rate close to that of rodent cells. Computational modeling demonstrated that HCN channel activity at the human fast-spiking cell soma membrane is sufficient to accelerate the input–output function as observed in cell recordings. Thus, human and mouse fast-spiking neurons exhibit functionally significant differences in ion channel composition at the cell soma membrane to set the speed and fidelity of their input–output function. These HCN channels ensure fast electrical reactivity of fast-spiking cells in human neocortex. LA - English DB - MTMT ER - TY - JOUR AU - Novák, Tibor AU - Varga, Dániel AU - Bíró, Péter AU - H. Kovács, Bálint Barna AU - Majoros, Hajnalka AU - Pankotai, Tibor AU - Szikora, Szilárd AU - Mihály, József AU - Erdélyi, Miklós TI - Quantitative dSTORM superresolution microscopy JF - RESOLUTION AND DISCOVERY J2 - RESOL DISCOVERY VL - 6 PY - 2022 IS - 1 SP - 25 EP - 31 PG - 7 SN - 2498-8707 DO - 10.1556/2051.2022.00093 UR - https://m2.mtmt.hu/api/publication/33260144 ID - 33260144 AB - Localization based superresolution technique provides the highest spatial resolution in optical microscopy. The final image is formed by the precise localization of individual fluorescent dyes, therefore the quantification of the collected data requires special protocols, algorithms and validation processes. The effects of labelling density and structured background on the final image quality were studied theoretically using the TestSTORM simulator. It was shown that system parameters affect the morphology of the final reconstructed image in different ways and the accuracy of the imaging can be determined. Although theoretical studies help in the optimization procedure, the quantification of experimental data raises additional issues, since the ground truth data is unknown. Localization precision, linker length, sample drift and labelling density are the major factors that make quantitative data analysis difficult. Two examples (geometrical evaluation of sarcomere structures and counting the γH2AX molecules in DNA damage induced repair foci) have been presented to demonstrate the efficiency of quantitative evaluation experimentally. LA - English DB - MTMT ER - TY - JOUR AU - H. Kovács, Bálint Barna AU - Varga, Dániel AU - Sebők, Dániel AU - Majoros, Hajnalka AU - Polanek, Róbert AU - Pankotai, Tibor AU - Hideghéty, Katalin AU - Kukovecz, Ákos AU - Erdélyi, Miklós TI - Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images JF - CELLS J2 - CELLS-BASEL VL - 11 PY - 2022 IS - 19 PG - 14 SN - 2073-4409 DO - 10.3390/cells11193105 UR - https://m2.mtmt.hu/api/publication/33134572 ID - 33134572 N1 - Department of Optics and Quantum Electronics, University of Szeged, Szeged, 6720, Hungary Department of Applied and Environmental Chemistry, University of Szeged, Szeged, 6720, Hungary Institute of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6725, Hungary Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, Szeged, 6723, Hungary Biomedical Applications Group, ELI-ALPS Research Institute, ELI-HU Non-Profit Ltd., Szeged, 6728, Hungary Department of Oncotherapy, University of Szeged, Szeged, 6720, Hungary Genome Integrity and DNA Repair Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), University of Szeged, Szeged, 6728, Hungary Export Date: 31 March 2023 Correspondence Address: Kovács, B.B.H.; Department of Optics and Quantum Electronics, Hungary; email: hkbalint@gmail.com Correspondence Address: Erdélyi, M.; Department of Optics and Quantum Electronics, Hungary; email: erdelyim@physx.u-szeged.hu Funding details: 739593 Funding details: National Renewable Energy Laboratory, NREL, RRF-2.3.1-21-2022-00009 Funding details: European Commission, EC Funding details: Magyar Tudományos Akadémia, MTA, BO/27/20, UNKP-21-5-SZTE-563, UNKP-22-5-SZTE-318 Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA, TKP2021-NVA-19 Funding text 1: This research was partially supported by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme (no. TKP2021-NVA-19); the Bolyai János Research Scholarship of the Hungarian Academy of Sciences (BO/27/20); the UNKP-22-5-SZTE-318 and UNKP-21-5-SZTE-563 grants; the Recovery and Resilience Facility of the European Union within the framework of the Széchenyi Plan Plus programme (National Laboratory for Renewable Energy project, no. RRF-2.3.1-21-2022-00009). The project has received funding from the EU’s Horizon 2020 research and innovation program under grant agreement No. 739593. AB - The quantitative analysis of datasets achieved by single molecule localization microscopy is vital for studying the structure of subcellular organizations. Cluster analysis has emerged as a multi-faceted tool in the structural analysis of localization datasets. However, the results it produces greatly depend on the set parameters, and the process can be computationally intensive. Here we present a new approach for structural analysis using lacunarity. Unlike cluster analysis, lacunarity can be calculated quickly while providing definitive information about the structure of the localizations. Using simulated data, we demonstrate how lacunarity results can be interpreted. We use these interpretations to compare our lacunarity analysis with our previous cluster analysis-based results in the field of DNA repair, showing the new algorithm’s efficiency. LA - English DB - MTMT ER - TY - GEN AU - Szatmári, Orsolya AU - Györkei, Á AU - Varga, Dániel AU - H. Kovács, Bálint Barna AU - Igaz, Nóra AU - Német, K AU - Bagi, N AU - Nagy-Mikó, B AU - Balogh, D AU - Rázga, Zsolt AU - Erdélyi, Miklós AU - Papp, B AU - Csontné Kiricsi, Mónika AU - Blastyák, A AU - Collart, MA AU - Boros, Imre Miklós AU - Villanyi, Zoltan TI - Phase separated ribosome nascent chain complexes paused in translation are capable to continue expression of proteins playing role in genotoxic stress response upon DNA damage PY - 2022 DO - 10.1101/2022.03.16.484567 UR - https://m2.mtmt.hu/api/publication/32783378 ID - 32783378 LA - English DB - MTMT ER - TY - GEN AU - Varga, Dániel AU - Novák, Tibor AU - P., Bíró AU - S., Szikora AU - J., Mihály AU - Erdélyi, Miklós TI - Segmentation of sarcomeric structures in SMLM with machine learning PY - 2022 UR - https://m2.mtmt.hu/api/publication/32783294 ID - 32783294 LA - English DB - MTMT ER - TY - GEN AU - Varga, Dániel TI - SMLM adatok kvantitatív elemzése PY - 2022 UR - https://m2.mtmt.hu/api/publication/32783128 ID - 32783128 N1 - Előadás LA - Hungarian DB - MTMT ER - TY - GEN AU - Varga, Dániel TI - Quantification of DNA Damage Induced Repair Focus Formation via dSTORM Localization Microscopy PY - 2022 UR - https://m2.mtmt.hu/api/publication/32755344 ID - 32755344 LA - English DB - MTMT ER -