We performed label-free observation of molecular dynamics in apoptotic cells by Raman microscopy. Dynamic changes in cytochrome c distribution at the Raman band of 750 cm -1 were observed after adding an apoptosis inducer to the cells. The comparison of mitochondria fluorescence images and Raman images of cytochrome c confirmed that changes in cytochrome c distribution can be distinguished as release of cytochrome c from mitochondria. Our observation also revealed that the redox state of cytochrome c was maintained during the release from the mitochondria. Monitoring mitochondrial membrane potential with JC-1 dye confirmed that the observed cytochrome c release was associated with apoptosis.

We demonstrate dynamic imaging of molecular distribution in unstained living cells using Raman scattering. By combining slit-scanning detection and optimizing the excitation wavelength, we imaged the dynamic molecular distributions of cytochrome c, protein beta sheets, and lipids in unstained HeLa cells with a temporal resolution of 3 minutes. We found that 532-nm excitation can be used to generate strong Raman scattering signals and to suppress autofluorescence that typically obscures Raman signals. With this technique, we reveal time-resolved distributions of cytochrome c and other biomolecules in living cells in the process of cytokinesis without the need for fluorescent labels or markers.

Dynamic SERS imaging inside a living cell is demonstrated with the use of a gold nanoparticle, which travels through the intracellular space to probe local molecular information over time. Simultaneous tracking of particle motion and SERS spectroscopy allows us to detect intracellular molecules at 65 nm spatial resolution and 50 ms temporal resolution, providing molecular maps of organelle transport and lisosomal accumulation. Multiplex spectral and trajectory imaging will enable imaging of specific dynamic biological functions such as membrane protein diffusion, nuclear entry, and rearrangement of cellular cytoskeleton.

SummaryWe propose an extension to Nomarski differential interference contrast microscopy that enables isotropic linear phase imaging. The method combines phase shifting, two directions of shear and Fourier-space integration using a modified spiral phase transform. We simulated the method using a phantom object with spatially varying amplitude and phase. Simulated results show good agreement between the final phase image and the object phase, and demonstrate resistance to imaging noise.

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We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.

SummaryDifferential interference contrast (DIC) is frequently used in conventional 2D biological microscopy. Our recent investigations into producing a 3D DIC microscope (in both conventional and confocal modes) have uncovered a fundamental difficulty: namely that the phase gradient images of DIC microscopy cannot be visualized using standard digital image processing and reconstruction techniques, as commonly used elsewhere in microscopy. We discuss two approaches to the problem of preparing gradient images for 3D visualization: integration and the Hilbert transform. After applying the Hilbert transform, the dataset can then be visualized in 3D using standard techniques. We find that the Hilbert transform provides a rapid qualitative pre-processing technique for 3D visualization for a wide range of biological specimens in DIC microscopy, including chromosomes, which we use in this study.

In the last couple of decades, the spatial resolution in optical microscopy has increased to unprecedented levels by exploiting the fluorescence properties of the probe. At about the same time, Raman imaging techniques have emerged as a way to image inherent chemical information in a sample without using fluorescent probes. However, in many applications, the achievable resolution is limited to about half the wavelength of excitation light. Here we report the use of structured illumination to increase the spatial resolution of label-free spontaneous Raman microscopy, generating highly detailed spatial contrast from the ensemble of molecular information in the sample. Using structured line illumination in slit-scanning Raman microscopy, we demonstrate a marked improvement in spatial resolution and show the applicability to a range of samples, including both biological and inorganic chemical component mapping. This technique is expected to contribute towards greater understanding of chemical component distributions in organic and inorganic materials.