Cellular Phenotyping
Cellular Phenotyping for High Throughput Screening
The BHF Centre for Cardiovascular Target Discovery currently has two high-throughput phenotyping approaches - high content imaging using a Perkin Elmer Operetta System, and a multimodal plate reader (Perkin Elmer Envision, provided through the Target Discovery Institute).
Perkin Elmer Envision Multimodal Plate Reader
The Perkin Elmer Envision multimodal plate reader allows reporter assays (luciferase, renilla, GFP), calcium measurements, FRET and BRET assays, among others.
Perkin Elmer Operetta High Content Screening Microscope
The Perkin Elmer Operetta is a 96-384 well spinning disk confocal microscopy system, that has live cell imaging capacity, automated quantitative image analysis (Harmony) and machine learning (PhenoLOGIC) software. It can perform many types of assay followed by automated image analysis -including cell cycle, apoptosis, cell shape, G-proteins, cytoskeletal reorganisation, texture, membrane texture, nuclear foci - nucleoli, neurite outgrowth, angiogenic tube formation, mitochondria mass, cytoplasmic foci - e.g. autophagosomes, colony formation, cell migration, 3D cell invasion, and protein localisation and quantification.
If you have an assay that you can perform using ordinary or confocal microscopy, we should be able to port it to the Operetta system.
Multiparametric Imaging
The image (provided by Dr Ayman Zen, Bhattacharya Group) shows HL1 cardiomyocytes stained using DAPI (nuclei), phalloidin (actin, red), and anti-tubulin antibody (green). A major goal is to develop multiparametric imaging to maximise phenotype information retrieval from high-content images such as this.
Key references for multiparametric imaging
1. Caie, P.D., Walls, R.E., Ingleston-Orme, A., Daya, S., Houslay, T., Eagle, R., Roberts, M.E. & Carragher, N.O. High-content phenotypic profiling of drug response signatures across distinct cancer cells. Mol Cancer Ther 9, 1913-26 (2010).
2. Fuchs, F., Pau, G., Kranz, D., Sklyar, O., Budjan, C., Steinbrink, S., Horn, T., Pedal, A., Huber, W. & Boutros, M. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Mol Syst Biol 6, 370 (2010).
3. Jones, T.R., Carpenter, A.E., Lamprecht, M.R., Moffat, J., Silver, S.J., Grenier, J.K., Castoreno, A.B., Eggert, U.S., Root, D.E., Golland, P. & Sabatini, D.M. Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proceedings of the National Academy of Sciences of the United States of America 106, 1826-31 (2009).
4. Collinet, C., Stoter, M., Bradshaw, C.R., Samusik, N., Rink, J.C., Kenski, D., Habermann, B., Buchholz, F., Henschel, R., Mueller, M.S., Nagel, W.E., Fava, E., Kalaidzidis, Y. & Zerial, M. Systems survey of endocytosis by multiparametric image analysis. Nature 464, 243-9 (2010).
5. Shamir, L., Delaney, J.D., Orlov, N., Eckley, D.M. & Goldberg, I.G. Pattern recognition software and techniques for biological image analysis. PLoS computational biology 6, e1000974 (2010).