Abstract
Functional imaging can provide a level of quantication that is not possible inwhat might be termed traditional high-content screening. This is due to the fact
that the current state-of-the-art high-content screening systems take the approach of scaling-up single cell assays, and are therefore based on essentially pictorial measures as assay indicators. Such phenotypic analyses have become extremely sophisticated, advancing screening enormously, but this approach can still be somewhat subjective. Recent advances in high-content screening with functional read-outs such as FRET (by FLIM or anisotropy imaging) have enabled screening of compound libraries of inhibitors and siRNA against a known protein interaction readout but this is still relatively slow in comparison to true high throughput methodologies. In order to further increase the predictive and statistical power of functional FRET assays, we have developed a compact lifetime-based ow cytometer, utilising a commercial micro
uidic chip, to screen large non-adherent cell population. Fluorescent signals from cells are detected using time correlated single photon counting (TCSPC) in the burst integrated uorescence lifetime (BIFL) mode and used to determine the
uorescence lifetime of each cell. Initially, the system was tested using 2 m and 10 m highly uorescent beads to determine optical throughput and detection eciency. The system was validated with a number of cell lines transiently transfected with FRET standards, consisting of eGFP and mRFP1
uorescent proteins linked by 7, 19, and 32 amino acid chains. Analysis software was developed to process detected signals in BIFL mode and chronologically save the transient burst data for each cell in a multidimensional image le. Furthermore, the system was validated using an EGFR
phosphorylation assay in MCF7 cells to ascertain the sensitivity of the system
for protein-protein interaction screening with a transfected protein and a labelled antibody.
| Date of Award | 2013 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Simon Ameer-Beg (Supervisor) & Klaus Suhling (Supervisor) |