Probe-independent image correlation spectroscopy

A random motility assay based on image correlation spectroscopy

We demonstrate the random motility (RAMOT) assay based on image correlation spectroscopy for the automated, label-free, high-throughput characterization of random cell migration. The approach is complementary to traditional migration assays, which determine only the collective net motility in a particular direction. The RAMOT assay is less demanding on image quality compared to single-cell tracking, does not require cell identification or trajectory reconstruction, and performs well on live-cell, time-lapse, phase contrast video microscopy of hundreds of cells in parallel. Effective diffusion coefficients derived from the RAMOT analysis are in quantitative agreement with Monte Carlo simulations and allowed for the detection of pharmacological effects on macrophage-like cells migrating on a planar collagen matrix. These results expand the application range of image correlation spectroscopy to multicellular systems and demonstrate a novel, to our knowledge, migration assay with little preparative effort.

STICCS reveals matrix-dependent adhesion slipping and gripping in migrating cells

Two-color spatio-temporal image cross-correlation spectroscopy (STICCS) is a new, to our knowledge, image analysis method that calculates space-time autocorrelation and cross-correlation functions from fluorescence intensity fluctuations. STICCS generates cellular flow and diffusion maps that reveal interactions and cotransport of two distinct molecular species labeled with different fluorophores. Here we use computer simulations to map the capabilities and limitations of STICCS for measurements in complex heterogeneous environments containing micro- and macrostructures. We then use STICCS to analyze the co-flux of adhesion components in migrating cells imaged using total internal reflection fluorescence microscopy. The data reveal a robust, time-dependent co-fluxing of certain integrins and paxillin in adhesions in protrusions when they pause, and in adhesions that are sliding and disassembling, demonstrating that the molecules in these adhesions move as a complex. In these regions, both α6β1- or αLβ2-integrins, expressed in CHO.B2 cells, co-flux with paxillin; an analogous cotransport was seen for α6β1-integrin and α-actinin in U2OS. This contrasts with the behavior of the α5β1-integrin and paxillin, which do not co-flux. Our results clearly show that integrins can move in complexes with adhesion proteins in protrusions that are retracting.

Image correlation spectroscopy of randomly distributed disks

Image correlation spectroscopy (ICS) has been widely used to quantify spatiotemporal distributions of fluorescently labelled cell membrane proteins and receptors. When the membrane proteins are randomly distributed, ICS may be used to estimate protein densities, provided the proteins behave as point-like objects. At high protein area fraction, however, even randomly placed proteins cannot obey Poisson statistics, because of excluded area. The difficulty can arise if the protein effective area is quite large, or if proteins form large complexes or aggregate into clusters. In these cases, there is a need to determine the correct form of the intensity correlation function for hard disks in two dimensions, including the excluded area effects. We present an approximate but highly accurate algorithm for the computation of this correlation function. The correlation function was verified using test images of randomly distributed hard disks of uniform intensity convolved with the microscope point spread function. This algorithm can be readily modified to compute exact intensity correlation functions for any probe geometry, interaction potential, and fluorophore distribution; we show how to apply it to describe a random distribution of large proteins labeled with a single fluorophore.