@mastersthesis{MTMT:34131654,
	title = {Lokalizációs mikroszkópiás mérések kvantitatív elemzése [Quantitative analysis of superresolution localization microscopy measurements]},
	url = {https://m2.mtmt.hu/api/publication/34131654},
	author = {Varga, Dániel},
	doi = {10.14232/phd.11283},
	publisher = {Universití of Szeged},
	unique-id = {34131654},
	abstract = {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.},
	year = {2023},
	orcid-numbers = {Varga, Dániel/0000-0003-0391-5057}
}

@article{MTMT:34067381,
	title = {Phase-separated ribosome-nascent chain complexes in genotoxic stress response},
	url = {https://m2.mtmt.hu/api/publication/34067381},
	author = {Szatmári, Orsolya and Nagy-Mikó, Bence and Györkei, Ádám and Varga, Dániel and H. Kovács, Bálint Barna and Igaz, Nóra and Bognár, Bence and Rázga, Zsolt and Nagy, Gábor and Zsindely, Nóra and Bodai, László and Papp, Balázs and Erdélyi, Miklós and Csontné Kiricsi, Mónika and Blastyák, András and Collart, Martine A and Boros, Imre Miklós and Villanyi, Zoltan},
	doi = {10.1261/rna.079755.123},
	journal-iso = {RNA},
	journal = {RNA-A PUBLICATION OF THE RNA SOCIETY},
	volume = {29},
	unique-id = {34067381},
	issn = {1355-8382},
	abstract = {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.},
	year = {2023},
	eissn = {1469-9001},
	pages = {1557-1574},
	orcid-numbers = {Varga, Dániel/0000-0003-0391-5057; Igaz, Nóra/0000-0003-1580-4397; Rázga, Zsolt/0000-0003-4717-8482; Nagy, Gábor/0000-0001-5464-1135; Zsindely, Nóra/0000-0002-6189-3100; Bodai, László/0000-0001-8411-626X; Erdélyi, Miklós/0000-0002-9501-5752; Csontné Kiricsi, Mónika/0000-0002-8416-2052; Boros, Imre Miklós/0000-0001-8504-9687}
}

@article{MTMT:33708203,
	title = {Machine learning framework to segment sarcomeric structures in SMLM data},
	url = {https://m2.mtmt.hu/api/publication/33708203},
	author = {Varga, Dániel and Szikora, Szilárd and Novák, Tibor and Pap, Gergely and Lékó, Gábor and Mihály, József and Erdélyi, Miklós},
	doi = {10.1038/s41598-023-28539-7},
	journal-iso = {SCI REP},
	journal = {SCIENTIFIC REPORTS},
	volume = {13},
	unique-id = {33708203},
	issn = {2045-2322},
	abstract = {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.},
	year = {2023},
	eissn = {2045-2322},
	orcid-numbers = {Varga, Dániel/0000-0003-0391-5057; Novák, Tibor/0000-0003-0756-6851; Lékó, Gábor/0000-0001-8679-9156; Erdélyi, Miklós/0000-0002-9501-5752}
}

@article{MTMT:33644822,
	title = {HCN channels at the cell soma ensure the rapid electrical reactivity of fast-spiking interneurons in human neocortex},
	url = {https://m2.mtmt.hu/api/publication/33644822},
	author = {Szegedi, Viktor and Bakos , Emőke and Furdan, Szabina and H. Kovács, Bálint Barna and Varga, Dániel and Erdélyi, Miklós and Barzó, Pál and Szűcs, Attila and Tamás, Gábor and Lamsa, Karri},
	doi = {10.1371/journal.pbio.3002001},
	journal-iso = {PLOS BIOL},
	journal = {PLOS BIOLOGY},
	volume = {21},
	unique-id = {33644822},
	issn = {1544-9173},
	abstract = {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.},
	year = {2023},
	eissn = {1545-7885},
	orcid-numbers = {Szegedi, Viktor/0000-0003-4191-379X; Varga, Dániel/0000-0003-0391-5057; Erdélyi, Miklós/0000-0002-9501-5752; Barzó, Pál/0000-0001-8717-748X; Szűcs, Attila/0000-0001-9733-4135; Tamás, Gábor/0000-0002-7905-6001; Lamsa, Karri/0000-0002-4609-1337}
}

@article{MTMT:33260144,
	title = {Quantitative dSTORM superresolution microscopy},
	url = {https://m2.mtmt.hu/api/publication/33260144},
	author = {Novák, Tibor and Varga, Dániel and Bíró, Péter and H. Kovács, Bálint Barna and Majoros, Hajnalka and Pankotai, Tibor and Szikora, Szilárd and Mihály, József and Erdélyi, Miklós},
	doi = {10.1556/2051.2022.00093},
	journal-iso = {RESOL DISCOVERY},
	journal = {RESOLUTION AND DISCOVERY},
	volume = {6},
	unique-id = {33260144},
	abstract = {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.},
	year = {2022},
	eissn = {2498-8707},
	pages = {25-31},
	orcid-numbers = {Novák, Tibor/0000-0003-0756-6851; Varga, Dániel/0000-0003-0391-5057; Majoros, Hajnalka/0000-0003-2020-971X; Pankotai, Tibor/0000-0001-9810-5465; Erdélyi, Miklós/0000-0002-9501-5752}
}

@article{MTMT:33134572,
	title = {Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images},
	url = {https://m2.mtmt.hu/api/publication/33134572},
	author = {H. Kovács, Bálint Barna and Varga, Dániel and Sebők, Dániel and Majoros, Hajnalka and Polanek, Róbert and Pankotai, Tibor and Hideghéty, Katalin and Kukovecz, Ákos and Erdélyi, Miklós},
	doi = {10.3390/cells11193105},
	journal-iso = {CELLS-BASEL},
	journal = {CELLS},
	volume = {11},
	unique-id = {33134572},
	abstract = {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.},
	year = {2022},
	eissn = {2073-4409},
	orcid-numbers = {Varga, Dániel/0000-0003-0391-5057; Sebők, Dániel/0000-0001-9769-5598; Majoros, Hajnalka/0000-0003-2020-971X; Polanek, Róbert/0000-0003-3645-8331; Pankotai, Tibor/0000-0001-9810-5465; Hideghéty, Katalin/0000-0001-7080-2365; Kukovecz, Ákos/0000-0003-0716-9557; Erdélyi, Miklós/0000-0002-9501-5752}
}

@misc{MTMT:32783378,
	title = {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},
	url = {https://m2.mtmt.hu/api/publication/32783378},
	author = {Szatmári, Orsolya and Györkei, Á and Varga, Dániel and H. Kovács, Bálint Barna and Igaz, Nóra and Német, K and Bagi, N and Nagy-Mikó, B and Balogh, D and Rázga, Zsolt and Erdélyi, Miklós and Papp, B and Csontné Kiricsi, Mónika and Blastyák, A and Collart, MA and Boros, Imre Miklós and Villanyi, Zoltan},
	doi = {10.1101/2022.03.16.484567},
	unique-id = {32783378},
	year = {2022},
	orcid-numbers = {Varga, Dániel/0000-0003-0391-5057; Igaz, Nóra/0000-0003-1580-4397; Rázga, Zsolt/0000-0003-4717-8482; Erdélyi, Miklós/0000-0002-9501-5752; Csontné Kiricsi, Mónika/0000-0002-8416-2052; Boros, Imre Miklós/0000-0001-8504-9687}
}

@misc{MTMT:32783294,
	title = {Segmentation of sarcomeric structures in SMLM with machine learning},
	url = {https://m2.mtmt.hu/api/publication/32783294},
	author = {Varga, Dániel and Novák, Tibor and P., Bíró and S., Szikora and J., Mihály and Erdélyi, Miklós},
	unique-id = {32783294},
	year = {2022},
	orcid-numbers = {Varga, Dániel/0000-0003-0391-5057; Erdélyi, Miklós/0000-0002-9501-5752}
}

@misc{MTMT:32783128,
	title = {SMLM adatok kvantitatív elemzése},
	url = {https://m2.mtmt.hu/api/publication/32783128},
	author = {Varga, Dániel},
	unique-id = {32783128},
	year = {2022},
	orcid-numbers = {Varga, Dániel/0000-0003-0391-5057}
}

@misc{MTMT:32755344,
	title = {Quantification of DNA Damage Induced Repair Focus Formation via dSTORM Localization Microscopy},
	url = {https://m2.mtmt.hu/api/publication/32755344},
	author = {Varga, Dániel},
	unique-id = {32755344},
	year = {2022},
	orcid-numbers = {Varga, Dániel/0000-0003-0391-5057}
}