K-space Image Correlation Spectroscopy Research Articles

K-Space Image Correlation Spectroscopy of Quantum dot Labeled T Cell Receptors Characterizes their Nanoscale Clustering in Living Cells

Changes in distribution of membrane receptor organization are used by cells to modulate the dynamic range of their responses to environmental cues. Consequently, it is important to develop experimental methods that can accurately measure receptor transport and aggregation. We used quantum dot (QD) labeling of T cell receptors (TCR) and a recently developed technique, k-space image correlation spectroscopy (kICS) to characterize TCR state as a function of cell differentiation. We developed kICS to measure transport coefficients of fluorescently labeled membrane proteins while taking into account nanoparticle emission blinking. We use kICS to measure T cell receptor (TCR) aggregation in live cells by characterizing quantum dot (QD) blinking and distribution on the cell surface. 2C TCR transgenic cells in culture were observed from the naive state to 12 days after activation by antigen. Cells were labeled with biotin-MHC/peptide to bind the TCR with relatively high avidity, followed by streptavidin-quantum dots. They were then imaged on an emCCD-equipped microscope and kICS analysis was applied. Spatial intensity fluctuations in an image measured the clustering of receptors on the 100s of nm length scale. Changes in the intensity correlation function of the blinking QDs characterized clustering on the 10s of nm length scale. We also used kICS to measure changes in TCR diffusive transport. When T cells exhibited maximum activity 3-4 days after exposure to antigen, the degree of their TCR aggregation on both length scales was significantly higher than that of naive cells, while TCR diffusion was a minimum. This new technology has powerful applications as it can be applied to just a few cells and we will show that it is able to detect changes in receptor organization of cells in vivo.

A guide to accurate measurement of diffusion using fluorescence correlation techniques with blinking quantum dot nanoparticle labels

Fluctuation-based fluorescence correlation techniques are widely used to study dynamics of fluorophore labeled biomolecules in cells. Semiconductor quantum dots (QDs) have been developed as bright and photostable fluorescent probes for various biological applications. However, the fluorescence intermittency of QDs, commonly referred to as "blinking", is believed to complicate quantitative correlation spectroscopy measurements of transport properties, as it is an additional source of fluctuations that contribute on a wide range of time scales. The QD blinking fluctuations obey power-law distributions so there is no single characteristic fluctuation time for this phenomenon. Consequently, it is highly challenging to separate fluorescence blinking fluctuations from those due to transport dynamics. Here, we quantify the bias introduced by QD blinking in transport measurements made using fluctuation methods. Using computer simulated image time series of diffusing point emitters with set "on" and "off" time emission characteristics, we show that blinking results in a systematic overestimation of the diffusion coefficients measured with correlation analysis when a simple diffusion model is used to fit the time correlation decays. The relative error depends on the inherent blinking power-law statistics, the sampling rate relative to the characteristic diffusion time and blinking times, and the total number of images in the time series. This systematic error can be significant; moreover, it can often go unnoticed in common transport model fits of experimental data. We propose an alternative fitting model that incorporates blinking and improves the accuracy of the recovered diffusion coefficients. We also show how to completely eliminate the bias by applying k-space image correlation spectroscopy, which completely separates the diffusion and blinking dynamics, and allows the simultaneous recovery of accurate diffusion coefficients and QD blinking probability distribution function exponents.

Advances in Image Correlation Spectroscopy: Measuring Number Densities, Aggregation States, and Dynamics of Fluorescently labeled Macromolecules in Cells

A brief historical outline of fluorescence fluctuation correlation techniques is presented, followed by an in-depth review of the theory and development of image correlation techniques, including: image correlation spectroscopy (ICS), temporal ICS (TICS), image cross-correlation spectroscopy (ICCS), spatiotemporal ICS (STICS), k-space ICS (kICS), raster ICS (RICS), and particle ICS (PICS). These techniques can be applied to analyze image series acquired on commercially available laser scanning or total internal reflection fluorescence microscopes, and are used to determine the number density, aggregation state, diffusion coefficient, velocity, and interaction fraction of fluorescently labeled molecules or particles. A comprehensive review of the application of ICS techniques to a number of systems, including cell adhesion, membrane receptor aggregation and dynamics, virus particle fusion, and fluorophore photophysics, is presented.

K-Space Image Correlation Spectroscopy: A Method for Accurate Transport Measurements Independent of Fluorophore Photophysics

We present the theory and application of reciprocal space image correlation spectroscopy (kICS). This technique measures the number density, diffusion coefficient, and velocity of fluorescently labeled macromolecules in a cell membrane imaged on a confocal, two-photon, or total internal reflection fluorescence microscope. In contrast to r-space correlation techniques, we show kICS can recover accurate dynamics even in the presence of complex fluorophore photobleaching and/or “blinking”. Furthermore, these quantities can be calculated without nonlinear curve fitting, or any knowledge of the beam radius of the exciting laser. The number densities calculated by kICS are less sensitive to spatial inhomogeneity of the fluorophore distribution than densities measured using image correlation spectroscopy. We use simulations as a proof-of-principle to show that number densities and transport coefficients can be extracted using this technique. We present calibration measurements with fluorescent microspheres imaged on a confocal microscope, which recover Stokes-Einstein diffusion coefficients, and flow velocities that agree with single particle tracking measurements. We also show the application of kICS to measurements of the transport dynamics of α5-integrin/enhanced green fluorescent protein constructs in a transfected CHO cell imaged on a total internal reflection fluorescence microscope using charge-coupled device area detection.