Quantifying the invisible: Bayesian approaches in fluorescence microscopy

Image datasets capturing the dynamics of large populations of moving objects are routinely acquired in fluorescent microscopy. Newest datasets contain quantitative and reliable observations with single molecule accuracy; however, despite of being obtained under state-of-the-art experimental procedures, their analysis remains a challenging task. Major difficulties are caused by: the governing photophysics which allow only limited fluorescent signals leading to very low signal-to-noise ratios; the optics of the experimental apparatus which limit the image resolution leading to significant blur even in the absence of other noise sources. Because of these characteristics, existing analysis methods, that are primarily developed for applications outside biophysics, can be used only sub-optimally as they typically rely on ergodic averages, thresholding, or otherwise artificial reduction of the available measurements. Instead, for biophysical applications, optimal analysis of the experimental datasets needs to utilize every data piece. This may be achieved only when the analysis is combined with simultaneous interpretation of the underlying physical system over the entire fluorescent population and over the entire time course available. In this presentation, I will describe a general framework that may be used for this task and I will walk through a number of example cases involving single molecule tracking.