Manager: Dr. Janos Kriston-Vizi
The Bioinformatics Image Core provides services for researchers from various areas in cell biology and analysis of large data sets from high-throughput screening experiments.
The Bioinformatics Image Core (BIONIC) facility supports:
- the high-throughput screening activities at the Translational Research Resource Centre
- the image processing and data analysis throughout the LMCB
Advanced microscopic technology is allowing imaging approaches to detect cellular dynamics with increasing spatial and temporal resolution. New approaches are required to allow effective analysis and quantitation of the data, requiring individuals with advanced skills in image analysis and computing.
The core facility has experience in image analysis, statistical methods for analyzing large data sets and experience in automation processes. as well as a good understanding of genomic technologies and the drug discovery process. We have experience in operation of PerkinElmer Opera high-content screening microscope. The core's activity is based on image analysis softwares ImageJ and Acapella as well as the statistical program R with Bioconductor packages such as CellHTS2. Furthermore, we have some experience in tissue culture and basic laboratory techniques.
Equipment of the Bioinformatics Image Core
Tyan FT48-B8812 High Performance Barebone System
256 GB memory
4x 12-Core AMD® Opteron® 6174 CPUs (48 cores)
NVidia GeForce GTX 580
Acer GN245HQ 3D monitor with NVidia 3D Vision
Ultima 9550i Tyrannosaur MKIII
Intel Core i7 3770K 3.50GHz @ 4.60GHz 8-Core
DDR3 32GB Ivybridge
Nvidia GTX 680
Asus VG278H 3D monitor with NVidia 3D Vision 2
UCL Drug Design - Phenotypic Screening course supporting files
ImageJ/Fiji Cell Outliner plugin compiled files.
- Download Cell_Outliner.png and copy into Imagej/Fiji "Plugins" folder.
- Rename the file extension from .png to .jar
- Start ImageJ/Fiji.
Prak, K., Kriston-Vizi, J., Chan, A. W., Luft, C., Costa, J. R., Pengo, N., & Ketteler, R. (2015). Benzobisthiazoles Represent a Novel Scaffold for Kinase Inhibitors of CLK Family Members. Biochemistry.
Prak, K., Naka, M., Tandang-Silvas, M. R. G., Kriston-Vizi, J., Maruyama, N., & Utsumi, S. (2015). Polypeptide modification: An improved proglycinin design to stabilise oil-in-water emulsions. Protein Engineering, Design and Selection, 28 (9), 281-291. doi:10.1093/protein/gzv031
Rodrigues, N. T., Lekomtsev, S., Jananji, S., Kriston-Vizi, J., Hickson, G. R., & Baum, B. (2015). Kinetochore-localized PP1-Sds22 couples chromosome segregation to polar relaxation. Nature, 524 (7566), 489-492. doi:10.1038/nature14496
Ferraro F, Kriston-Vizi J, Metcalf DJ, Martin-Martin B, Freeman J, Burden JJ, Westmoreland D, Dyer CE, Knight AE, Ketteler R, Cutler DF (2014) A Two-Tier Golgi-Based Control of Organelle Size Underpins the Functional Plasticity of Endothelial Cells. Developmental Cell. 29 (3), 292-304.
Stevenson NL, Martin-Martin B, Freeman J, Kriston-Vizi J, Ketteler R, Cutler DF (2014) G protein-coupled receptor kinase 2 moderates recruitment of THP-1 cells to the endothelium by limiting histamine-invoked Weibel-Palade body exocytosis. Journal of Thrombosis and Haemostasis. 12 (2), 261-272.
Foldes G, Matsa E, Kriston-Vizi J, Leja T, Amisten S, Kolker L, Mioulane M, Vauchez K, Aranyi T, Ketteler R, Schneider M, Denning C, Harding S (2013) Key differences in hypertrophic signalling in hESC-and hIPSC-derived cardiomyocytes. Human Gene Therapy. 24: A9-A9.
Kriston-Vizi, J. (2013, June 28). High Content Imaging at LMCB. Computational Life and Medical Sciences (CLMS) Annual Symposium. University College London, UK. http://www.clms.ucl.ac.uk/node/254
Kriston-Vizi, J. (2013, June 6-7). 3D and 2D cellular high-content screens identify novel autophagy regulator kinase inhibitors. European Lab Automation 2013, Drug Discovery Automation: High-content Screening & Cell Based Assays. Hamburg, Germany. http://selectbiosciences.com/conferences/index.aspx?conf=DDAHCS2013
Kriston-Vizi, J., Ketteler, R. (2012, February 4). Single-Cell Volume Determination for 3D Segmentation of Human Cancer and Nontumourigenic Cells to Identify Autophagic Phenotypes in High-Content Screening. SLAS2012 Society for Laboratory Automation & Screening. San Diego, USA.
Evans, R., Coussens, A. K., Kriston-Vizi, J., Chain, B. J., & Noursadeghi, M. (2011). The effect of vitamin D on monocyte biology: a physiological perspective. In IMMUNOLOGY Vol. 135 (pp. 88). Retrieved from http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000297507100254&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=f41074198c063036414efcbc916f8956
Freeman, J., Kriston-Vizi, J., Lindenschmidt, I., Haider, M., & Ketteler, R. (2011). Autophagy protease ATG4B as a target in drug discovery. INT J MOL MED, 28, S72.
Kriston-Vizi, J. (2011, November 17). From image processing to hits: a high-content analysis pipeline using free software. In 16th Academic Screening Group Meeting. Sutton, UK.
Kriston-Vizi, J., Wee Thong, N., Leong Poh, C., Chia Yee, K., Poh Ling, J. S., Kraut, R., Wasser, M. (2011). Gebiss: An ImageJ Plugin for the Specification of Ground Truth and the Performance Evaluation of 3D Segmentation Algorithms. BMC Bioinformatics, 12, 232. doi:10.1186/1471-2105-12-232
Kriston-Vizi, J., Ching Aeng, L., Condron, P., Chua, K., Wasser, M., & Flotow, H. (2010). An Automated High-Content Screening Image Analysis Pipeline for the Identification of Selective Autophagic Inducers in Human Cancer Cell Lines. Journal of Biomolecular Screening, 15(7), 869-881. doi:10.1177/1087057110373393