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:
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.
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
ImageJ/Fiji Cell Outliner plugin compiled files.
- Using the link at the bottom of the page download Cell_Outliner.txt and copy into Imagej/Fiji "Plugins" folder.
- Rename the file extension from .txt to .zip
- Uncompress the Cell_Outliner.zip file.
- Start Imagej/Fiji.
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
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.
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
J., Kriston-Vizi, J., Lindenschmidt, I., Haider, M., & Ketteler,
R. (2011). Autophagy protease ATG4B as a target in drug discovery.
J MOL MED,
J. (2011, November 17). From image processing to hits: a
high-content analysis pipeline using free software. In 16th
Academic Screening Group Meeting.
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
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,
C., Choo, P. W., Kriston-Vizi, J., & Waser, M. (2009). 3D
segmentation for the study of cell cycle progression in live
drosophila embryos. Proceedings
of the 1st International Workshop on Medical Image Analysis and
Description for Diagnosis Systems, MIAD 2009 in Conjunction with
J., Umeda, M., & Miyamoto, K. (2008). Assessment of the water
status of mandarin and peach canopies using visible multispectral