High Content Analysis
Optical microscopy provides many important readouts for cell-based studies. Increasingly for drug discovery and for systems biology there is an increasing desire to translate microscopy-based studies of cell biology to automated higher throughput measurements or assays. Conventional high throughput screening (HTS) approaches, e.g. using flow cytometry or multiwell plate readers, can enable the rapid measurement of fluorescence from of tens of thousands of cells but such techniques typically provide no sub-cellular image data. Increasingly, there is interest in fluorescence and other assays implemented in multiwell plate readers or imaging cytometers to facilitate high content analysis (HCA), imaging fixed or live samples with subcellular resolution with high throughput. Such HCA instrumentation must enable hundreds to thousands of microscopy images to be obtained with minimum user intervention and combine automated acquisition with sophisticated image analysis routines. This presents significant challenges in terms of data acquisition and analysis, but enables scientific questions to be addressed on an unprecedented scale and HCA is being increasingly utilised in academic research as well as in the pharmaceutical industry. The power of HCA is illustrated by the assays based on automated time lapse microscopy of live cells systematically modified by siRNA to silence specific genes and screen for candidates associated with particular phenotypes. This was exemplified at a high level of sophistication by the “Mitocheck” assay1 to identify the genes involved in mitosis, and today screens are envisaged based on automated imaging whole organisms such as zebrafish2. We are particularly interested in developing FLIM-based HCA technology to screen for protein interactions and other cell signalling processes using FRET-based readouts and in HCA applied to 3-D cell cultures.
1 Neumann, B., Held, M., Liebel, U., Erfle, H., Rogers, P., Pepperkok, R. and Ellenberg, J., Nature Methods, 3 (2006) 385
2 Pardo-Martin, C., Chang, T.-Y., Koo, B. K., Gilleland, C. L., Wasserman, S. C. and Yanik, M. F., Nature Methods, 7 (2010) 634