Scientists from Arizona State University are leveraging the tools of data science to study molecular activity more quickly than is possible through traditional fluorescence correlation spectroscopy (FCS). While FCS provides estimates of dynamical quantities, it requires high signal-to-noise ratios and time traces that are typically in the minute range. The researchers at ASU are using Bayesian analysis to overcome the limitations of fluorescent correlative methods in using short and noisy time traces to deduce molecular properties such as diffusion coefficients. A molecule, whose path traced out in time is shown by the blue line, occasionally wanders into a brightly lit green region. Within this region, the molecule is excited and begins emitting light of a different wavelength that can be distinguished from the green light. This emitted light reports back on the behavior of the molecule. Courtesy of Steve Pressé. Using Bayesian nonparametrics for the direct analysis of the observed photon counts emitted by single molecules, the researchers were able to analyze time traces that are too short to be analyzed by existing methods, including FCS. The team’s new analysis approach could extend the capability of single molecule fluorescence confocal microscopy techniques to probe molecular processes at a speed that is several orders of magnitude faster. Also, the new approach could potentially reduce the phototoxic effects on living samples that can occur when samples are exposed to light for long periods of time. ASU professors Steve Pressé and Marcia Levitus. Courtesy of ASU School of Molecular Sciences. “New mathematical tools make it possible to think about old but powerful experiments in a new light,” professor Steve Pressé said. “Single-molecule fluorescence techniques have revolutionized our understanding of the dynamics of many critical molecular processes, but signals are inherently noisy and experiments require long acquisition times,” professor Marcia Levitus said. “Old strategies limited our ability to probe anything but slow processes, leaving a vast number of interesting biological questions involving faster chemical reactions out of reach. Now we can begin asking questions on processes resolved in short order.” The research was published in Nature Communications (https://doi.org/10.1038/s41467-019-11574-2).