Measurement of live-cell binding interactions is vital for understanding the biochemical reactions that drive cellular processes. Here, we develop, characterize, and apply a new procedure to extract information about binding to an immobile substrate from fluorescence correlation spectroscopy (FCS) autocorrelation data. We show that existing methods for analyzing such data by two-component diffusion fits can produce inaccurate estimates of diffusion constants and bound fractions, or even fail altogether to fit FCS binding data. By analyzing live-cell FCS measurements, we show that our new model can satisfactorily account for the binding interactions introduced by attaching a DNA binding domain to the dimerization domain derived from a site-specific transcription factor (the vitellogenin binding protein (VBP)). We find that our FCS estimates are quantitatively consistent with our fluorescence recovery after photobleaching (FRAP) measurements on the same VBP domains. However, due to the fast binding interactions introduced by the DNA binding domain, FCS generates independent estimates for the diffusion constant (6.7 +/- 2.4 microm2/s) and the association (2 +/- 1.2 s(-1)) and dissociation (19 +/- 7 s(-1)) rates, whereas FRAP produces only a single, but a consistent, estimate, the effective-diffusion constant (4.4 +/- 1.4 microm2/s), which depends on all three parameters. We apply this new FCS method to evaluate the efficacy of a potential anticancer drug that inhibits DNA binding of VBP in vitro and find that in vivo the drug inhibits DNA binding in only a subset of cells. In sum, we provide a straightforward approach to directly measure binding rates from FCS data.

Controlled diffusion and release of soluble molecules is one of the key challenges in developing three-dimensional (3D) scaffolds for tissue engineering and drug delivery applications in part because current methods to measure dynamic transport properties are difficult to perform directly, are strongly affected by the experimental setup, and therefore can be a subject to various artifacts. In this work we present a method for direct measurement of translational diffusion of solutes, namely Fluorescence Correlation Spectroscopy (FCS), by characterizing the diffusion of model proteins through a 3D cross-linked poly(ethylene glycol) (PEG) hydrogel scaffold. We examined both the dynamics of hydrogel structure (e.g., cross-linking and swelling) as well as protein size and their effect on protein diffusivity. For example, we demonstrated that protein diffusivity was closely related to protein size as smaller proteins (e.g., lysozyme) diffused faster than larger proteins (e.g., γ-globulin or Ig). We validated the FCS protein diffusivity results by comparison to standard bulk diffusion assays. Additionally, due to the nature of FCS measurements, we were able to probe for hydrogel–protein interactions during cross-linking that may contribute to the obstructed protein diffusion in the 3D scaffold. We determined that such interactions in this system were not covalent (i.e., were independent of the cross-linking chemistry) but may be due to weaker hydrogen bonding or ionic interactions. Also, these interactions were protein specific and contributed up to 25% of the total decrease in protein diffusivity in the hydrogel as compared to diffusivity in water. Though interactions between various proteins and PEG have been reported, this is the first study that has explored these effects in detail in cross-linked PEG hydrogels using FCS; our findings question the assumption that PEG hydrogels are completely inert to protein interactions when applied as drug delivery matrices and tissue engineering scaffolds.

Many techniques rely on the binding activity of surface-immobilized proteins, including antibody-based affinity biosensors for the detection of analytes, immunoassays, protein arrays, and surface plasmon resonance biosensors for the study of thermodynamic and kinetic aspects of protein interactions. To study the functional homogeneity of the surface sites and to characterize their binding properties, we have recently proposed a computational tool to determine the distribution of affinity and kinetic rate constants from surface binding progress curves. It is based on modeling the experimentally measured binding signal as a superposition of signals from binding to sites spanning a range of rate and equilibrium constants, with regularization providing the most parsimonious distribution consistent with the data. In the present work, we have expanded the scope of this approach to include a compartment-like transport step, which can describe competitive binding to different surface sites in a zone of depleted analyte close to the sensor surface. This approach addresses a major difficulty in the analysis of surface binding where both transport limitation as well as unknown surface site heterogeneity may be present. In addition to the kinetic binding parameters of the ensemble of surface sites, it can provide estimates for effective transport rate constants. Using antibody-antigen interactions as experimental model systems, we studied the effects of the immobilization matrix and of the analyte flow-rate on the effective transport rate constant. Both were experimentally observed to influence mass transport. The approximate description of mass transport by a compartment model becomes critical when applied to strongly transport-controlled data, and we examined the limitations of this model. In the presence of only moderate mass transport limitation the compartment model provides a good description, but this approximation breaks down for strongly transport-limited surface binding. In the latter regime, we report experimental evidence for the formation of gradients within the sensing volume of the evanescent field biosensor used.