TY - JOUR AU - Grexa, István AU - Iván, Zsanett Zsófia AU - Migh, Ede AU - Kovács, Ferenc AU - Bolck, Hella A AU - Zheng, Xiang AU - Mund, Andreas AU - Moshkov, Nikita AU - Csapóné Miczán, Vivien AU - Koós, Krisztián AU - Horváth, Péter TI - SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy JF - BRIEFINGS IN BIOINFORMATICS J2 - BRIEF BIOINFORM VL - 25 PY - 2024 IS - 2 PG - 11 SN - 1467-5463 DO - 10.1093/bib/bbae029 UR - https://m2.mtmt.hu/api/publication/34786803 ID - 34786803 AB - Numerous imaging techniques are available for observing and interrogating biological samples, and several of them can be used consecutively to enable correlative analysis of different image modalities with varying resolutions and the inclusion of structural or molecular information. Achieving accurate registration of multimodal images is essential for the correlative analysis process, but it remains a challenging computer vision task with no widely accepted solution. Moreover, supervised registration methods require annotated data produced by experts, which is limited. To address this challenge, we propose a general unsupervised pipeline for multimodal image registration using deep learning. We provide a comprehensive evaluation of the proposed pipeline versus the current state-of-the-art image registration and style transfer methods on four types of biological problems utilizing different microscopy modalities. We found that style transfer of modality domains paired with fully unsupervised training leads to comparable image registration accuracy to supervised methods and, most importantly, does not require human intervention. LA - English DB - MTMT ER - TY - JOUR AU - Moshkov, Nikita AU - Bornholdt, Michael AU - Benoit, Santiago AU - Smith, Matthew AU - Mcquin, Claire AU - Goodman, Allen AU - Senft, Rebecca A. AU - Han, Yu AU - Babadi, Mehrtash AU - Horváth, Péter AU - Cimini, Beth A. AU - Carpenter, Anne E. AU - Singh, Shantanu AU - Caicedo, Juan C. TI - Learning representations for image-based profiling of perturbations JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 15 PY - 2024 IS - 1 PG - 17 SN - 2041-1723 DO - 10.1038/s41467-024-45999-1 UR - https://m2.mtmt.hu/api/publication/34782532 ID - 34782532 N1 - Funding Agency and Grant Number: ECOST action [CA15124]; LENDULET-BIOMAG Grant [2018-342]; European Regional Development Funds [GINOP-2.2.1-15-2017-00072]; H2020; EU-Horizont (ERAPERMED-COMPASS, ERAPERMED-SYMMETRY, DiscovAIR, FAIR-CHARM, SWEEPICS); ELKH-Excellence grants; Cooperative Doctoral Programme (2020-2021) of the Ministry for Innovation and Technology; OTKA-SNN [P41 GM135019]; NIH [2018-192059]; Chan Zuckerberg Initiative DAF [2348683]; Silicon Valley Community Foundation; Schmidt Fellowship program of the Broad Institute; NSF DBI Award [R35 GM122547]; [TKP2021-EGA09]; [139455/ARRS]; [2020-225720] Funding text: We thank Salil Bhate for the valuable discussions and feedback provided to improve the clarity of this manuscript. NM acknowledges the short-term scientific mission grants provided by eCOST action CA15124 (NEUBIAS) in 2019 and 2020. NM and PH acknowledge support from the LENDULET-BIOMAG Grant (2018-342), from the European Regional Development Funds (GINOP-2.2.1-15-2017-00072), from the H2020 and EU-Horizont (ERAPERMED-COMPASS, ERAPERMED-SYMMETRY, DiscovAIR, FAIR-CHARM, SWEEPICS), from TKP2021-EGA09, from the ELKH-Excellence grants and from the Cooperative Doctoral Programme (2020-2021) of the Ministry for Innovation and Technology, from OTKA-SNN no.139455/ARRS. Researchers in the Carpenter-Singh lab were supported by NIH R35 GM122547 to AEC. Researchers in the Cimini lab were supported by NIH P41 GM135019. AG was supported by grant number 2018-192059 and BAC was additionally supported by grant number 2020-225720 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. JCC was supported by the Schmidt Fellowship program of the Broad Institute and by the NSF DBI Award 2348683. AB - Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient. Assessing cell phenotypes in image-based assays requires solid computational methods for transforming images into quantitative data. Here, the authors present a strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. LA - English DB - MTMT ER - TY - JOUR AU - Dinh, Hoa AU - Kovács, Zsuzsanna AU - Kis, Merse AU - Kupecz, Klaudia AU - Sejben, Anita AU - Szűcs, Gergő AU - Márványkövi, Fanni AU - Siska, Andrea AU - Freiwan, Marah AU - Pósa, Szonja Polett AU - Galla, Zsolt AU - Ibos, Katalin Eszter AU - Bodnár, Éva AU - Lauber, Gülsüm Yilmaz AU - Goncalves, Ana Isabel Antunes AU - Acar, Eylem AU - Kriston, András AU - Kovács, Ferenc AU - Horváth, Péter AU - Bozsó, Zsolt AU - Tóth, Gábor AU - Földesi, Imre AU - Monostori, Péter AU - Cserni, Gábor AU - Podesser, Bruno K. AU - Lehoczki, Andrea Marianna AU - Pokreisz, Peter AU - Kiss, Attila AU - Dux, László AU - Csabafi, Krisztina AU - Sárközy, Márta TI - Role of the kisspeptin-KISS1R axis in the pathogenesis of chronic kidney disease and uremic cardiomyopathy JF - GEROSCIENCE: OFFICIAL JOURNAL OF THE AMERICAN AGING ASSOCIATION (AGE) J2 - GEROSCIENCE VL - 46 PY - 2024 IS - 2 SP - 2463 EP - 2488 PG - 26 SN - 2509-2715 DO - 10.1007/s11357-023-01017-8 UR - https://m2.mtmt.hu/api/publication/34395398 ID - 34395398 N1 - Department of Biochemistry and Interdisciplinary Centre of Excellence, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Department of Biochemistry, Bach Mai Hospital, Hanoi, 100000, Viet Nam Department of Pathophysiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Department of Laboratory Medicine, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Metabolic and Newborn Screening Laboratory, Department of Pediatrics, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Ludwig Boltzmann Institute for Cardiovascular Research at Center for Biomedical Research and Translational Surgery, Medical University of Vienna, Vienna, 1090, Austria Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged, 6726, Hungary Single-Cell Technologies Ltd, Szeged, 6726, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00014, Finland Department of Medical Chemistry, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Departments of Hematology and Stem Cell Transplantation, South Pest Central Hospital, National Institute of Hematology and Infectious Diseases, Saint Ladislaus Campus, Budapest, Hungary Export Date: 16 April 2024 Correspondence Address: Dux, L.; Department of Biochemistry and Interdisciplinary Centre of Excellence, Hungary; email: dux.laszlo@med.u-szeged.hu Correspondence Address: Sárközy, M.; Department of Biochemistry and Interdisciplinary Centre of Excellence, Hungary; email: martasarkozy@gmail.com AB - The prevalence of chronic kidney disease (CKD) is increasing globally, especially in elderly patients. Uremic cardiomyopathy is a common cardiovascular complication of CKD, characterized by left ventricular hypertrophy (LVH), diastolic dysfunction, and fibrosis. Kisspeptins and their receptor, KISS1R, exert a pivotal influence on kidney pathophysiology and modulate age-related pathologies across various organ systems. KISS1R agonists, including kisspeptin-13 (KP-13), hold promise as novel therapeutic agents within age-related biological processes and kidney-related disorders. Our investigation aimed to elucidate the impact of KP-13 on the trajectory of CKD and uremic cardiomyopathy. Male Wistar rats (300–350 g) were randomized into four groups: (I) sham-operated, (II) 5/6 nephrectomy-induced CKD, (III) CKD subjected to a low dose of KP-13 (intraperitoneal 13 µg/day), and (IV) CKD treated with a higher KP-13 dose (intraperitoneal 26 µg/day). Treatments were administered daily from week 3 for 10 days. After 13 weeks, KP-13 increased systemic blood pressure, accentuating diastolic dysfunction’s echocardiographic indicators and intensifying CKD-associated markers such as serum urea levels, glomerular hypertrophy, and tubular dilation. Notably, KP-13 did not exacerbate circulatory uremic toxin levels, renal inflammation, or fibrosis markers. In contrast, the higher KP-13 dose correlated with reduced posterior and anterior wall thickness, coupled with diminished cardiomyocyte cross-sectional areas and concurrent elevation of inflammatory ( Il6, Tnf ), fibrosis ( Col1 ), and apoptosis markers ( Bax/Bcl2 ) relative to the CKD group. In summary, KP-13’s influence on CKD and uremic cardiomyopathy encompassed heightened blood pressure and potentially activated inflammatory and apoptotic pathways in the left ventricle. LA - English DB - MTMT ER - TY - JOUR AU - Hirling, Dominik AU - Tasnádi, Ervin Áron AU - Caicedo, Juan AU - Caroprese, Maria V. AU - Sjögren, Rickard AU - Aubreville, Marc AU - Koós, Krisztián AU - Horváth, Péter TI - Segmentation metric misinterpretations in bioimage analysis JF - NATURE METHODS J2 - NAT METHODS VL - 21 PY - 2024 SP - 213 EP - 216 PG - 4 SN - 1548-7091 DO - 10.1038/s41592-023-01942-8 UR - https://m2.mtmt.hu/api/publication/34080269 ID - 34080269 N1 - Funding Agency and Grant Number: Lenduelet BIOMAG grant [2018342, TKP2021-EGA09]; H2020-COMPASS-ERAPerMed; CZI Deep Visual Proteomics; H2020-DiscovAIR; H2020-Fair-CHARM; HAS-NAP3; Horizon Europe BIALYMP; ELKH-Excellence grant from OTKA-SNN [139455/ARRS]; FIMM High Content Imaging and Analysis Unit (FIMM-HCA; HiLIFE-HELMI); Finnish Cancer Society; Doctoral Student Scholarship Program of the Co-operative Doctoral Program of the Ministry of Innovation and Technology - National Research, Development and Innovation Fund Funding text: D.H., K.K., E.T. and P.H. acknowledge support from the Lenduelet BIOMAG grant (no. 2018-342), TKP2021-EGA09, H2020-COMPASS-ERAPerMed, CZI Deep Visual Proteomics, H2020-DiscovAIR, H2020-Fair-CHARM, HAS-NAP3, Horizon Europe BIALYMP, the ELKH-Excellence grant from OTKA-SNN no. 139455/ARRS, the FIMM High Content Imaging and Analysis Unit (FIMM-HCA; HiLIFE-HELMI), and Finnish Cancer Society. D.H. and P.H. acknowledge the professional support of the Doctoral Student Scholarship Program of the Co-operative Doctoral Program of the Ministry of Innovation and Technology financed from the National Research, Development and Innovation Fund. We acknowledge support from A. Carpenter for the help in sharing the DSB2018 dataset. AB - Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled. LA - English DB - MTMT ER - TY - JOUR AU - Szadai, Leticia AU - Guedes, Jéssica de Siqueira AU - Woldmar, Nicole AU - de Almeida, Natália Pinto AU - Jánosi, Ágnes Judit AU - Rajeh, Ahmad AU - Kovács, Ferenc AU - Kriston, András AU - Migh, Ede AU - Wan, Guihong AU - Nguyen, Nga AU - Oskolás, Henriett AU - Appelqvist, Roger AU - Nogueira, Fábio Cn AU - Domont, Gilberto B AU - Yu, Kun-Hsing AU - Semenov, Eugene R AU - Malm, Johan AU - Rezeli, Melinda AU - Wieslander, Elisabet AU - Fenyö, David AU - Kemény, Lajos AU - Horváth, Péter AU - Németh, István Balázs AU - Marko-Varga, György AU - Gil, Jeovanis TI - Mitochondrial and immune response dysregulation in melanoma recurrence JF - CLINICAL AND TRANSLATIONAL MEDICINE J2 - CLIN TRANSL MED VL - 13 PY - 2023 IS - 11 PG - 7 SN - 2001-1326 DO - 10.1002/ctm2.1495 UR - https://m2.mtmt.hu/api/publication/34436978 ID - 34436978 N1 - Besorolása szakcikk a központi MTMT adminisztrátorokkal történt egyeztetés és az MTA V. Osztályának képviselője által végzett tartalmi vizsgálat alapján. (BCS, SZTE admin4, 2024-01-03) LA - English DB - MTMT ER - TY - JOUR AU - Bukva, Mátyás AU - Dobra, Gabriella AU - Gyukity-Sebestyén, Edina AU - Böröczky, Timea AU - Korsós, Marietta Margaréta AU - David G, Meckes AU - Horváth, Péter AU - Buzás, Krisztina AU - Harmati, Mária TI - Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis. JF - CELL COMMUNICATION AND SIGNALING J2 - CELL COMM SIGN VL - 21 PY - 2023 IS - 1 PG - 17 SN - 1478-811X DO - 10.1186/s12964-023-01344-5 UR - https://m2.mtmt.hu/api/publication/34417027 ID - 34417027 N1 - Video-Audio Media; Meta-Analysis; Journal Article AB - Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods.On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas.Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract. LA - English DB - MTMT ER - TY - JOUR AU - Tasnádi, Ervin Áron AU - Sliz-Nagy, Alex AU - Horváth, Péter TI - Structure preserving adversarial generation of labeled training samples for single-cell segmentation JF - CELL REPORTS METHODS J2 - CELL REP METH VL - 3 PY - 2023 IS - 9 PG - 15 SN - 2667-2375 DO - 10.1016/j.crmeth.2023.100592 UR - https://m2.mtmt.hu/api/publication/34334191 ID - 34334191 N1 - Funding Agency and Grant Number: LENDULET-BIOMAG grant [2018-342]; European Regional Development Funds [GINOP-2.2.1-15-2017-00072]; Chan-Zuckerberg Initiative Deep Visual Proteomincs grant; Cooperative Doctoral Programme (KDP) (2020-2021) of the Ministry for Innovation and Technology; CZI Napari grant Funding text: The authors thank Andreas Mund and Reka Hollandi for providing help with describing and annotating the datasets. The authors acknowledge support from a LENDULET-BIOMAG grant (2018-342) , the European Regional Development Funds (GINOP-2.2.1-15-2017-00072) , the H2020 and EU-Horizont (ERAPERMED-COMPASS, ERAPERMED-SYMMETRY, DiscovAIR, FAIR-CHARM, TRANSSCAN-BIALYMP) , HAS-NAP3, OTKA-SNN, TKP2021- EGA09, and ELKH-Excellence grants, from a Chan-Zuckerberg Initiative Deep Visual Proteomincs grant. E.T. and P.H. acknowledge support from the Cooperative Doctoral Programme (KDP) (2020-2021) of the Ministry for Innovation and Technology and from a CZI Napari grant. AB - We introduce a generative data augmentation strategy to improve the accuracy of instance segmentation of microscopy data for complex tissue structures. Our pipeline uses regular and conditional generative adver-sarial networks (GANs) for image-to-image translation to construct synthetic microscopy images along with their corresponding masks to simulate the distribution and shape of the objects and their appearance. The synthetic samples are then used for training an instance segmentation network (for example, StarDist or Cell -pose). We show on two single-cell-resolution tissue datasets that our method improves the accuracy of downstream instance segmentation tasks compared with traditional training strategies using either the raw data or basic augmentations. We also compare the quality of the object masks with those generated by a traditional cell population simulation method, finding that our synthesized masks are closer to the ground truth considering Fre ' chet inception distances. LA - English DB - MTMT ER - TY - JOUR AU - Dinh, Hoa AU - Kovács, Zsuzsanna AU - Márványkövi, Fanni AU - Kis, Merse AU - Kupecz, Klaudia AU - Szűcs, Gergő AU - Freiwan, Marah AU - Lauber, Gülsüm Yilmaz AU - Acar, Eylem AU - Siska, Andrea AU - Ibos, Katalin Eszter AU - Bodnár, Éva AU - Kriston, András AU - Kovács, Ferenc AU - Horváth, Péter AU - Földesi, Imre AU - Cserni, Gábor AU - Podesser, Bruno K. AU - Pokreisz, Peter AU - Kiss, Attila AU - Dux, László AU - Csabafi, Krisztina AU - Sárközy, Márta TI - The kisspeptin-1 receptor antagonist peptide-234 aggravates uremic cardiomyopathy in a rat model JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 13 PY - 2023 IS - 1 PG - 16 SN - 2045-2322 DO - 10.1038/s41598-023-41037-0 UR - https://m2.mtmt.hu/api/publication/34123594 ID - 34123594 N1 - Department of Biochemistry and Interdisciplinary Centre of Excellence, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Department of Biochemistry, Bach Mai Hospital, Hanoi, 100000, Viet Nam Ludwig Boltzmann Institute for Cardiovascular Research at Center for Biomedical Research and Translational Surgery, Medical University of Vienna, Vienna, A1090, Austria Department of Laboratory Medicine, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Department of Pathophysiology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged, 6726, Hungary Single-Cell Technologies Ltd, Szeged, 6726, Hungary Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00014, Finland Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, 6720, Hungary Export Date: 7 September 2023 Correspondence Address: Dux, L.; Department of Biochemistry and Interdisciplinary Centre of Excellence, Hungary; email: dux.laszlo@med.u-szeged.hu Correspondence Address: Sárközy, M.; Department of Biochemistry and Interdisciplinary Centre of Excellence, Hungary; email: sarkozy.marta@med.u-szeged.hu Funding details: BO/00532/23/5, UNKP-19-3-SZTE-160, UNKP-20-5-SZTE-166 Funding details: TSZ:34232-3/2016/INTFIN Funding details: 6177 Funding details: Magyar Tudományos Akadémia, MTA Funding details: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFIH, EFOP-3.6.2-16-2017-00006, GINOP-2.3.2-15-2016-00040 Funding details: Tempus Közalapítvány, TPF Funding details: Szegedi Tudományegyetem, SZTE Funding text 1: Open access funding provided by University of Szeged. This research was funded by the projects NKFIH FK129094 (to M.S., funder: National Research, Development and Innovation Office), GINOP-2.3.2-15-2016-00040 (to L.D., funder: National Research, Development and Innovation Office), EFOP-3.6.2-16-2017-00006 (to K.C., funder: National Research, Development and Innovation Office), Stipendium Hungaricum Program (to M.S. and L.D., funder: Tempus Public Foundation), and Ludwig Boltzmann Institute for Cardiovascular Research, Vienna, Austria. D.H. and M.F. were supported by the Stipendium Hungaricum Scholarship (funder: Tempus Public Foundation). H. D. was supported by the Albert Szent-Györgyi Scholarship for Ph.D. students (funder: University of Szeged, Albert Szent-Györgyi Medical School, Szeged, Hungary) and Bach Mai Hospital, Hanoi, Vietnam. M.S. and Z.Z.A.K. were supported by the New National Excellence Program of the Ministry of Human Capacities, Hungary (UNKP-20-5-SZTE-166 and UNKP-19-3-SZTE-160). M.S. was supported by the János Bolyai Research Scholarship (BO/00532/23/5) of the Hungarian Academy of Sciences. Z.Z.A.K. was supported by the EFOP 3.6.3-VEKOP-16-2017-00009 (funder: National Research, Development and Innovation Office). A.K. was supported by Theodor Körner Founds, Austria. F.M. was supported by the Szeged Scientists Academy Program (TSZ:34232-3/2016/INTFIN, Hungary). The publication was supported by the University of Szeged Open Access Found (6177). AB - Uremic cardiomyopathy is characterized by diastolic dysfunction, left ventricular hypertrophy (LVH), and fibrosis. Dysregulation of the kisspeptin receptor (KISS1R)-mediated pathways are associated with the development of fibrosis in cancerous diseases. Here, we investigated the effects of the KISS1R antagonist peptide-234 (P234) on the development of uremic cardiomyopathy. Male Wistar rats (300–350 g) were randomized into four groups: (i) Sham, (ii) chronic kidney disease (CKD) induced by 5/6 nephrectomy, (iii) CKD treated with a lower dose of P234 ( ip. 13 µg/day), (iv) CKD treated with a higher dose of P234 ( ip. 26 µg/day). Treatments were administered daily from week 3 for 10 days. At week 13, the P234 administration did not influence the creatinine clearance and urinary protein excretion. However, the higher dose of P234 led to reduced anterior and posterior wall thicknesses, more severe interstitial fibrosis, and overexpression of genes associated with left ventricular remodeling ( Ctgf, Tgfb, Col3a1, Mmp9 ), stretch ( Nppa ), and apoptosis ( Bax, Bcl2, Casp7 ) compared to the CKD group. In contrast, no significant differences were found in the expressions of apoptosis-associated proteins between the groups. Our results suggest that the higher dose of P234 hastens the development and pathophysiology of uremic cardiomyopathy by activating the fibrotic TGF-β-mediated pathways. LA - English DB - MTMT ER - TY - JOUR AU - Pietiäinen, Vilja AU - Polso, Minttu AU - Migh, Ede AU - Guckelsberger, Christian AU - Harmati, Mária AU - Diósdi, Ákos AU - Turunen, Laura AU - Hassinen, Antti AU - Potdar, Swapnil AU - Koponen, Annika AU - Gyukity-Sebestyén, Edina AU - Kovács, Ferenc AU - Kriston, András AU - Hollandi, Réka AU - Burián, Katalin AU - Terhes, Gabriella AU - Visnyovszki, Ádám AU - Fodor, Eszter AU - Lacza, Zsombor AU - Kantele, Anu AU - Kolehmainen, Pekka AU - Kakkola, Laura AU - Strandin, Tomas AU - Levanov, Lev AU - Kallioniemi, Olli AU - Kemény, Lajos AU - Julkunen, Ilkka AU - Vapalahti, Olli AU - Buzás, Krisztina AU - Paavolainen, Lassi AU - Horváth, Péter AU - Hepojoki, Jussi TI - Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2 JF - CELL REPORTS METHODS J2 - CELL REP METH VL - 3 PY - 2023 IS - 8 PG - 19 SN - 2667-2375 DO - 10.1016/j.crmeth.2023.100565 UR - https://m2.mtmt.hu/api/publication/34107493 ID - 34107493 N1 - HCEMM-USZ Skin Research Group Funding Agency and Grant Number: LENDULET-BIOMAG grant; European Regional Development Funds; H2020-discovAIR; H2020 ATTRACT-Spheroid -Picker; Chan Zuckerberg Initiative, Seed Networks for the HCA-DVP [2018-342, GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00026]; Finnish TEKES/BusinessFinland FiDiPro [GINOP-2.3.2-15-2016-00037]; Academy of Finland; EU H2020 VEO project; Minerva Foundation for COVID-19 Research project grant [874656]; Academy of Finland Flagship program, Finnish Center for Artificial Intelligence [40294/13, iCOIN-336496, 308613, 321809, 310552, 337530]; NKFIH grants; [FIRI2020-337036]; [2020-1.1.6-JOVO-2021-00010]; [TKP2020-NKA-17] Funding text: The authors thank the Minerva Institute (Helsinki, Finland) for providing utilities for the project, Prof. Perttu Hamalainen (Aalto University, Finland) for providing the expertise of his group for the project, the FIMM High Throughput Biomedicine Unit for providing access to high-throughput robotics, the FIMM High Content Imaging and Analysis Unit for HC imaging and analysis (HiLIFE, University of Helsinki and Biocenter Finland; EuroBioImaging, ISIDORe partner), and the CSC - IT Center for Science, Finland, for computational resources. We acknowledge support from the LENDULET-BIOMAG grant (2018-342), from the European Regional Development Funds (GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00026, and GINOP-2.3.2-15-2016-00037), from the H2020-discovAIR (874656), from the H2020 ATTRACT-Spheroid -Picker, and from the Chan Zuckerberg Initiative, Seed Networks for the HCA-DVP. The Finnish TEKES/BusinessFinland FiDiPro Fellow Grant 40294/13 (to V.P., O.K., L.P., and P.H.), grants awarded by the Academy of Finland (iCOIN-336496 to O.K., V.P., and O.V.; 308613 to J.H.; 321809 to T.S.; 310552 to L.P.; 337530 to I.J.; and FIRI2020-337036 to FIMM-HCA, A.H., L.P., V.P., and P.H.), the EU H2020 VEO project (O.V.), and a Minerva Foundation for COVID-19 Research project grant (to V.P.) are also acknowledged. C.G. is funded by the Academy of Finland Flagship program, Finnish Center for Artificial Intelligence. OrthoSera Ltd. was funded by NKFIH grants (2020-1.1.6-JOVO-2021-00010 and TKP2020-NKA-17). The authors thank Dora Bokor, PharmD, for proofreading the manuscript. LA - English DB - MTMT ER - TY - JOUR AU - Sikkema, L. AU - Ramírez-Suástegui, C. AU - Strobl, D.C. AU - Gillett, T.E. AU - Zappia, L. AU - Madissoon, E. AU - Markov, N.S. AU - Zaragosi, L.-E. AU - Ji, Y. AU - Ansari, M. AU - Arguel, M.-J. AU - Apperloo, L. AU - Banchero, M. AU - Bécavin, C. AU - Berg, M. AU - Chichelnitskiy, E. AU - Chung, M.-I. AU - Collin, A. AU - Gay, A.C.A. AU - Gote-Schniering, J. AU - Hooshiar, Kashani B. AU - Inecik, K. AU - Jain, M. AU - Kapellos, T.S. AU - Kole, T.M. AU - Leroy, S. AU - Mayr, C.H. AU - Oliver, A.J. AU - von, Papen M. AU - Peter, L. AU - Taylor, C.J. AU - Walzthoeni, T. AU - Xu, C. AU - Bui, L.T. AU - De, Donno C. AU - Dony, L. AU - Faiz, A. AU - Guo, M. AU - Gutierrez, A.J. AU - Heumos, L. AU - Huang, N. AU - Ibarra, I.L. AU - Jackson, N.D. AU - Kadur, Lakshminarasimha Murthy P. AU - Lotfollahi, M. AU - Tabib, T. AU - Talavera-López, C. AU - Travaglini, K.J. 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AU - Lung, Biological Network Consortium TI - An integrated cell atlas of the lung in health and disease JF - NATURE MEDICINE J2 - NAT MED VL - 29 PY - 2023 IS - 6 SP - 1563 EP - 1577 PG - 15 SN - 1078-8956 DO - 10.1038/s41591-023-02327-2 UR - https://m2.mtmt.hu/api/publication/34024053 ID - 34024053 N1 - Department of Computational Health, Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany TUM School of Life Sciences, Technical University of Munich, Munich, Germany La Jolla Institute for Allergy and Immunology, La JollaCA, United States Institute of Clinical Chemistry and Pathobiochemistry, TUM School of Medicine, Technical University of Munich, Munich, Germany Experimental Pulmonary and Inflammatory Research, Department of Pathology and Medical Biology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands Groningen Research Institute for Asthma and COPD, University Medical Center Groningen, University of Groningen, Groningen, Netherlands Department of Mathematics, Technical University of Munich, Garching, Germany Wellcome Sanger Institute, Cambridge, Hinxton, United Kingdom Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States Institut de Pharmacologie Moléculaire et Cellulaire, Université Côte d’Azur and Centre National de la Recherche Scientifique, Valbonne, France Institute of Lung Health and Immunity (a member of the German Center for Lung Research) and Comprehensive Pneumology Center with the CPC-M bioArchive, Helmholtz Center Munich, Munich, Germany Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands Institute for Transplant Immunology, Hannover Medical School, Hannover, Germany Translational Genomics Research Institute, Phoenix, AZ, United States 3IA Côte d’Azur, Nice, France Department of Genomics and Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany Department of Pulmonary Diseases, University Medical Center Groningen, University of Groningen, Groningen, Netherlands Pulmonology Department, Fédération Hospitalo-Universitaire OncoAge, Centre Hospitalier Universitaire de Nice, Université Côte d’Azur, Nice, France Research, Development and Innovation, Comma Soft, Bonn, Germany Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States Core Facility Genomics, Helmholtz Center Munich, Munich, Germany Department of Translational Psychiatry, Max Planck Institute of Psychiatry and International Max Planck Research School for Translational Psychiatry, Munich, Germany School of Life Sciences, Respiratory Bioinformatics and Molecular Biology, University of Technology Sydney, Sydney, Australia Division of Neonatology and Pulmonary Biology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States Center for Genes, Environment, and Health, National Jewish Health, Denver, CO, United States Department of Cell Biology, Duke University School of Medicine, Durham, NC, United States Department of Pharmacology and Regenerative Medicine, University of Illinois Chicago, Chicago, IL, United States Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States Division of Infectious Diseases and Tropical Medicine, Klinikum der Lüdwig-Maximilians-Universität, Munich, Germany Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, United States Howard Hughes Medical Institute, Chevy Chase, MD, United States Allen Institute for Brain Science, Seattle, WA, United States Department of Respiratory Medicine, Division of Medicine, University College London, London, United Kingdom Institut Universitaire de Cardiologie et de Pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec City, QC, Canada Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, United States Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, United States Garvan Institute of Medical Research, Sydney, NSW, Australia Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia Center for Regenerative Medicine, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA, United States Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, The Ohio State University, Columbus, OH, United States Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, United States Cellular and Tissue Genomics, Genentech, South San Francisco, CA, United States Department of Pediatrics, National Jewish Health, Denver, CO, United States Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado School of Medicine, Aurora, CO, United States Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, San Francisco, CA, United States Department of Physics and Center for Biological Physics, Arizona State University, Tempe, AZ, United States Centre for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, Vancouver, BC, Canada Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States Department of Pediatrics, Division of Respiratory Medicine, University of California, San Diego, La JollaCA, United States Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, United States Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, United States PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen and University of Bonn, Bonn, Germany Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom Department of Pediatrics (Pulmonology), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States Biological Research Centre, Szeged, Hungary Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States Lab and Department of Molecular Bioscience, Stockholm University, Stockholm, Sweden University of California, San Diego, La JollaCA, United States Export Date: 19 June 2023 CODEN: NAMEF Correspondence Address: Luecken, M.D.; Department of Computational Health, Germany; email: malte.luecken@helmholtz-munich.de Correspondence Address: Theis, F.J.; Department of Computational Health, Germany; email: fabian.theis@helmholtz-munich.de AB - Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. © 2023, The Author(s). LA - English DB - MTMT ER -