ARGOLIGHT
Arnaud ROYON
On Stand Workshop - Stand 41
One of the core facilities’ duties is to provide end-users, usually researchers in life sciences, a fleet of microscopes at a level of performance compatible with their experiments. This is not an easy task because microscopes intrinsically introduce biases in the images and because their performance tends to fluctuate or deteriorate over time for many reasons: misusing, aging, environment fluctuations, etc. This is especially true for high end imaging systems such as confocal or super-resolution microscopes.
To get quantitative and reproducible data, assessing the performance of fluorescence microscopes is a prerequisite before any imaging campaign, to know, measure and eventually correct the different biases they can introduce. For example, system co-registration accuracy should be evaluated before any co-localization study; System field uniformity and intensity response before any study where intensity in the image matters; Spatial resolution before any study aiming at counting objects close to each other, etc.
At the age of big data, artificial intelligence, machine learning, and predictive models, it is essential to perform quality control and quality assurance at any step of a bio-imaging experiment: at the sample preparation level, at the imaging system level, and at the image analysis level, to extract the sought biological information. Feeding the image analysis algorithms with corrupted image data gives rise to the well-known adage: “garbage in, garbage out”.
The workshop aims to show how the quality control and quality assurance of fluorescence imaging systems can be performed and standardized with Argolight solutions, and how the generated quality data can be managed and centralized for later reporting.
