Reconceptualizing Fluorescence Correlation Spectroscopy for Monitoring and Analyzing Periodically Passing Objects.

Abstract

Fluorescence correlation spectroscopy (FCS) is a sensitive technique commonly applied for studying the dynamics of nanoscale-labeled objects in solution. Current analysis of FCS data is largely based on the assumption that the labeled objects are stochastically displaced due to Brownian motion. However, this assumption is often invalid for microscale objects, since the motion of these objects is dominated by Stokes drag and settling or rising effects, rather than stochastic Brownian motion. To utilize the power of FCS for systems with nonstochastic displacements of objects, the collection and analysis of FCS data have to be reconceptualized. Here, we extended the applicability of FCS for the detection and analysis of periodically passing objects. Toward this end, we implemented droplet-based microfluidics, in which monodispersed droplets containing fluorescent marker are flowing equally spaced within microchannels. We show by simulations and experiments that FCS can sensitively quantify the flow-rates, variability, and content of rapidly passing droplets. This information can be derived at high temporal resolution, based on the intensity fluctuations generated by only 5–10 passing droplets. Moreover, by utilizing the periodicity of the flowing droplets for noise reduction by averaging, FCS can monitor accurately the droplets flow even if their fluorescence intensity is negligible. Hence, extending FCS for periodically passing objects converts it into a powerful analytical tool for high-throughput droplet-based microfluidics. Moreover, based on the principles described here, FCS can be straightforwardly applied for a variety of systems in which the passing of objects is periodic rather than stochastic.