Autocorrelation function of finite-length data in fluorescence correlation spectroscopy

Area of Science:

• Biophysics
• Spectroscopy
• Data Analysis

Background:

• Experimental autocorrelation functions in fluorescence correlation spectroscopy (FCS) are biased due to finite data length.
• Conventional FCS analysis struggles with data from living cells, often affected by signal instabilities.

Purpose of the Study:

• To develop a theoretical framework for unbiased analysis of experimental autocorrelation functions in FCS.
• To extend FCS applicability to live-cell measurements with intensity variations and instabilities.

Main Methods:

• Formulated a new theoretical framework accounting for data length in FCS analysis.
• Validated the theory using experiments and simulations of diffusion.
• Applied short data segmentation techniques to analyze unstable fluorescence signals.

Main Results:

• The new framework provides unbiased estimates of theoretical correlation functions.
• Accuracy and precision of parameter estimates were characterized for diffusion processes.
• Reformulated theory allows analysis with segment times approaching diffusion time, enhancing robustness.

Conclusions:

• The developed theoretical framework significantly improves FCS analysis for finite-length data.
• This approach enhances the robustness and applicability of FCS, particularly for challenging live-cell experiments.
• The study expands the range of experimental systems accessible to fluorescence correlation spectroscopy.