We introduce a high resolution scanning surface plasmon microscope for long term imaging of living adherent mouse myoblast cells. The coupling of a high numerical aperture objective lens with a fibered heterodyne interferometer provides both enhanced sensitivity and long term stability. This microscope takes advantage of the plasmon resonance excitation and the amplification of the electromagnetic field in near-field distance to the gold coated coverslip. This plasmon enhanced evanescent wave microscopy is particularly attractive for the study of cell adhesion and motility since it can be operated without staining of the biological sample. We show that this microscope allows very long-term imaging of living samples, and that it can capture and follow the temporal deformation of C2C12 myoblast cell protusions (lamellipodia), during their migration on a at surface.
The internal distribution of refractive indices (RIs) of a living cell is much more complex than usually admitted in multi-shell models. The reconstruction of RI maps from single phase images has rarely been achieved for several reasons: (i) we still have very little knowledge of the impact of internal macromolecular complexes on the local RI and (ii) phase changes produced by light propagation through the sample are mixed with diffraction effects by internal cell bodies. We propose the implementation a 2D wavelet-based contour chain detection method to distinguish internal boundaries thanks to their greatest optical path difference gradients. These contour chains correspond to the highest image phase contrast and follow the local RI inhomogeneities linked to the intracellular structural intricacy. Their statistics and spatial distribution are morphological indicators for distinguishing cells of different origins and to follow their transformation in pathologic situations. We use this method to compare non adherent blood cells from primary and laboratory culture origins, in healthy and pathological situations (chronic myelogenous leukaemia). In a second part of this presentation, we concentrate on the temporal dynamics of the phase contour chains and we discuss the spectral decomposition of their dynamics in both health and disease.
The distribution of refractive indices (RIs) of a living cell contributes in a nonintuitive manner to its optical phase image and quite rarely can be inverted to recover its internal structure. The interpretation of the quantitative phase images of living cells remains a difficult task because (1) we still have very little knowledge on the impact of its internal macromolecular complexes on the local RI and (2) phase changes produced by light propagation through the sample are mixed with diffraction effects by the internal cell bodies. We propose to implement a two-dimensional wavelet-based contour chain detection method to distinguish internal boundaries based on their greatest optical path difference gradients. These contour chains correspond to the highest image phase contrast and follow the local RI inhomogeneities linked to the intracellular structural intricacy. Their statistics and spatial distribution are the morphological indicators suited for comparing cells of different origins and/or to follow their transformation in pathologic situations. We use this method to compare nonadherent blood cells from primary and laboratory culture origins and to assess the internal transformation of hematopoietic stem cells by the transduction of the BCR-ABL oncogene responsible for the chronic myelogenous leukemia.
We propose a two-dimensional (2-D) space-scale analysis of fringe patterns collected from a diffraction phase microscope based on the 2-D Morlet wavelet transform. We show that the adaptation of a ridge detection method with anisotropic 2-D Morlet mother wavelets is more efficient for analyzing cellular and high refractive index contrast objects than Fourier filtering methods since it can separate phase from intensity modulations. We compare the performance of this ridge detection method on theoretical and experimental images of polymer microbeads and experimental images collected from living myoblasts.
We describe a formalism that allows us to study space (or time)-scale correlations in multiscale processes. This method, based on the continuous wavelet transform, is particularly well suited to study multiplicative random cascades for which the correlation functions take very simple expressions. This two-point space-scale statistical analysis is illustrated on synthetic multifractal signals and then applied to financial time series and fully developed turbulence data.
The Wavelet Transform Modulus Maxima method is used to analyze the fractal scaling properties of DNA sequences. This method, based on the definition of partition functions which use the values of the wavelet transform at its modulus maxima, allows to determine accurately the singularity spectrum of a given singular signal. By considering analyzing wavelets that make the wavelet transform microscope blind to `patches' of different nucleotide compositions which are ubiquitous to genomic sequences, we demonstrate and quantify the existence of long-range correlations in the noncoding regions. The fluctuations around the patchy landscapes of the DNA walks reconstructed from both the noncoding and coding regions are found to have Gaussian statistics. Whereas the fluctuations from the former behave like fractional brownian motions, those of the latter cannot be distinguished from uncorrelated random brownian walks.
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