Controlling the unknowns introduced by the imaging system itself has been key to the emergence of high-content, high-throughput screening platforms, which use sophisticated hardware calibration and software to report data in a standardized manner.
While secondary screening and lead optimization can be enhanced by automated microscopy methods, not all experiments are so amenable. These experiments can be hard to integrate into the discovery process because imaging data is reported in units of ambiguous meaning and internal controls must be set up to account for such potential issues.
Even when using such controls, data represented in ADUs leaves researchers without the means to verify that controls between experiments actually behaved equivalently, unless performed in the exact same system and, in the case of systems containing aging EMCCD sensors, around the same time. Without such affirmation, integrating data from these experiments and confidently reaching statistically significant conclusions is difficult.
For example, antibodies can introduce experimental variability. They can degrade during the course of multiple imaging experiments. This results in a reduction of their binding efficacy, changing the behavior of the fluorescent reporting system. The slow degradation of an antibody would gradually change the property of the experiment over time.
Cells transfected with fluorescent proteins are also useful for many types of studies. However, transfection efficiencies often vary from cell to cell. Under- or overexpression of fluorescent proteins can introduce experimental artifacts, change molecular behaviors, and modify intracellular distribution. Likewise, fluorescent dye uptake can also vary from cell to cell.
Without accounting for variation between the controls of different experiments, conclusions drawn from comparing such experiments have the potential for introducing additional variables and resulting in further errors. Such errors could potentially influence outcomes of the study such as the selection of candidate drug compounds.
Reporting imaging data in absolute, quantitative units lays the foundation for individual researchers and collaborators across the globe to more completely control for experimental variation. This makes imaging experiments reproducible and more easily comparable between different imaging systems, personnel, and laboratories.
Of course, researchers can manually calibrate their current cameras to align reported ADUs with actual incident photons. However, the calibration and back-calculation process requires a high level of knowledge, specialized equipment, and staff time. Since the process must be repeated regularly to account for sensor and gain aging of some EMCCD devices, it can be extremely cumbersome for researchers or technicians to keep every microscope camera characterized.
Implementing standards for reporting imaging data, starting with an absolute unit of measurement, will increase the potential for reporting statistically significant results and boost confidence in the reliability of those results.
Photons that hit CCD and EMCCD sensors generate photoelectrons, the fundamental unit of measurement for these devices. The way those photoelectrons are processed into ADUs is done in a variable manner, dependent on the camera electronics and the sensor.