Image Correlation Spectroscopy. II. Optimization for Ultrasensitive Detection of Preexisting Platelet-Derived Growth Factor-β Receptor Oligomers on Intact Cells

Abstract

Previously we introduced image correlation spectroscopy (ICS) as an imaging analog of fluorescence correlation spectroscopy (FCS). Implementation of ICS with image collection via a standard fluorescence confocal microscope and computer-based autocorrelation analysis was shown to facilitate measurements of absolute number densities and determination of changes in aggregation state for fluorescently labeled macromolecules. In the present work we illustrate how to use ICS to quantify the aggregation state of immunolabeled plasma membrane receptors in an intact cellular milieu, taking into account background fluorescence. We introduce methods that enable us to completely remove white noise contributions from autocorrelation measurements for individual images and illustrate how to perform background corrections for autofluorescence and nonspecific fluorescence on cell population means obtained via ICS. The utilization of photon counting confocal imaging with ICS analysis in combination with the background correction techniques outlined enabled us to achieve very low detection limits with standard immunolabeling methods on normal, nontransformed human fibroblasts (AG1523) expressing relatively low numbers of platelet-derived growth factor-beta (PDGF-beta) receptors. Specifically, we determined that the PDGF-beta receptors were preaggregated as tetramers on average with a mean surface density of 2.3 clusters micrometer(-2) after immunolabeling at 4 degreesC. These measurements, which show preclustering of PDGF-beta receptors on the surface of normal human fibroblasts, contradict a fundamental assumption of the ligand-induced dimerization model for signal transduction and provide support for an alternative model that posits signal transduction from within preexisting receptor aggregates.

Effect of the distribution and clustering of the type I A BMP receptor(ALK3) with the type II BMP receptor on the activation of signalling pathways

“…In order to understand the details of this process we need to study the quantitative distribution of the BMP receptors and the changes in their distribution following BMP-2 stimulation. Image correlation spectroscopy (ICS) provides a convenient and quantitative tool to measure the density of receptor clusters on cell surfaces (Brown and Petersen 1998;Brown et al, 1999;Srivastava and Petersen 1998;Wiseman and Petersen 1999;Wiseman et al, 1997).…”

Spatial and spectral detection of protein monolayers with deterministic aperiodic arrays of metal nanoparticles

“…Therefore, in the case of aperiodic structures, both the peak wavelength shift of the scattered radiation as well as the spatial structure of their distinctive colorimetric fingerprints can be utilized in order to detect the presence of nanoscale protein layers. The spatial modifications of the structural color fingerprints of aperiodic surfaces can be readily quantified by image autocorrelation analysis performed on the radiation intensity scattered by the bare surface and by the silk coated surface (29,30). The two-dimensional image autocorrelation function (ACF) of a colorimetric fingerprint Gðξ;ηÞ can be obtained directly from the scattering data by proper normalization as (detailed in the SI Text):…”

“…Once the normalized ACF of the structural color fingerprints of the aperiodic surfaces obtained from the bare and the silk coated surfaces has been calculated, the spatial modification of the fingerprints can be quantified by comparing their variances, which can be readily obtained by evaluating the normalization of the ACF in the limit of zero lateral displacements (29,30):…”

Resolving dynamics of cell signaling via real-time imaging of the immunological synapse.

“…Image correlation spectroscopy (ICS) was used to quantitatively measure receptor distributions on cell surfaces (Petersen et al, 1993;Wiseman and Petersen, 1999). ICS involves computing the two dimensional spatial autocorrelation function, g(,η) of an image i(x,y):…”

“…The intensity variance includes a significant contribution from spatially uncorrelated camera read noise and shot noise. The standard procedure (Petersen et al, 1993;Wiseman and Petersen, 1999) to obtain the noise-corrected variance is to fit data near the origin to the autocorrelation of the point spread function (PSF) itself if the PSF is larger than one pixel. In our case, a large field of view prevents oversampling and the noise corrected autocorrelation g s (0) is obtained by direct removal of camera read noise σ r , and shot noise σ p :…”