Identification of Plasma Membrane Macro- and Microdomains from Wavelet Analysis of FRET Microscopy

Abstract

In this study, we sought to characterize functional signaling domains by applying the multiresolution properties of the continuous wavelet transform to fluorescence resonance energy transfer (FRET) microscopic images of plasma membranes. A genetically encoded FRET reporter of protein kinase C (PKC)-dependent phosphorylation was expressed in COS1 cells. Differences between wavelet coefficient matrices revealed several heterogeneous domains (typically ranging from 1 to 5 microm), reflecting the dynamic balance between PKC and phosphatase activity during stimulation with phorbol-12,13-dibutyrate or acetylcholine. The balance in these domains was not necessarily reflected in the overall plasma membrane changes, and observed heterogeneity was absent when cells were exposed to a phosphatase or PKC inhibitor. Prolonged exposure to phorbol-12,13-dibutyrate and acetylcholine yielded more homogeneous FRET distribution in plasma membranes. The proposed wavelet-based image analysis provides, for the first time, a basis and a means of detecting and quantifying dynamic changes in functional signaling domains, and may find broader application in studying fine aspects of cellular signaling by various imaging reporters.

Spatiotemporal analysis of endocytosis and membrane distribution of fluorescent sterols in living cells

“…where the function W 2 L 2 with R Following the protocol of Kobrinsky et al (2005) a Daubechies wavelet of fourth order (db4) was chosen as mother wavelet (Kobrinsky et al 2005). These wavelet functions are limits of an iterative process and are bandlimited meaning that they equal zero outside the support.…”

“…These wavelet functions are limits of an iterative process and are bandlimited meaning that they equal zero outside the support. They have compact support and are suitable for local analysis (Blatter 2003;Kobrinsky et al 2005). The wavelet analysis toolbox implemented in MatLab (Mathworks, Inc., USA) was used to calculate wavelet coefficient matrices C containing the wavelet coefficients C ab according to Eq.…”

“…4. These were used to calculate temporal differences (D t ) in the coefficients according to (Kobrinsky et al 2005):…”

“…4) as well as frequency information, the expression in Eq. 5 is a measure of local spatiotemporal changes of DHE staining patterns obtained from the time-lapse data (Kobrinsky et al 2005).…”

“…By analogy with music this would mean that information about notes is maintained after transformation while information about when the notes should be played is lost. In contrast, a wavelet analysis allows for localizing spectral components assigned to a certain wavelet as function of pixel position x or time increment t in case of spatially and temporally varying signals, respectively (see ''Materials and methods '' and (Blatter 2003;Kobrinsky et al 2005;Mager and Abernethy 2007) for a detailed discussion). Again, in musical terms, the whole composition can be recovered from the wavelet transformation because it contains all information found in the original score.…”