Fluorescence has emerged as a widely used signal in life science research. In particular, fluorescence microscopy techniques have enabled researchers to characterize molecular interactions in natural cell environments. These techniques have been enhanced by electron multiplying charge coupled device (EMCCD) cameras providing sub-electron read noise and enabling visualization of everything from single molecules and sub-cellular structures to whole animals in vivo.
Fluorescence microscopy has advanced all stages of the drug discovery process, including target discovery, candidate screening with cell-based assays, toxicology evaluations, and mode-of-action studies. However, analyzing the wealth of knowledge from imaging experiments can be difficult. For years, life science researchers have struggled with the irreproducibility of images from CCD and EMCCD cameras. The analog-to-digital units (ADUs) in which those cameras report imaging data are known by many names, including gray-scale units and fluorescence units.
The problem is that ADUs are merely electronic representations of the number of photons hitting the sensor. The way in which cameras make this representation varies, even between the same make and model of camera. So an ADU doesn’t directly correlate with real incident light.
While technological advances have brought quantitative elements to imaging—such as cell counting and structural measurement—solutions for quantitation of fluorescence intensity, independent of users and equipment, is far from standard.
This is a hurdle for scientific data sharing, especially for multisite collaborations. The variable nature of the raw ADUs that cameras produce limit the labs’ ability to control for experimental variability and directly compare imaging data. Thus, knowledge produced by imaging experiments can be difficult to interpret.