Biomolecular imaging with coherent nonlinear vibrational microscopy

Abstract

Optical imaging with spectroscopic vibrational contrast is a label-free solution for visualizing, identifying, and quantifying a wide range of biomolecular compounds in biological materials. Both linear and nonlinear vibrational microscopy techniques derive their imaging contrast from infrared active or Raman allowed molecular transitions, which provide a rich palette for interrogating chemical and structural details of the sample. Yet nonlinear optical methods, which include both second-order sum-frequency generation (SFG) and third-order coherent Raman scattering (CRS) techniques, offer several improved imaging capabilities over their linear precursors. Nonlinear vibrational microscopy features unprecedented vibrational imaging speeds, provides strategies for higher spatial resolution, and gives access to additional molecular parameters. These advances have turned vibrational microscopy into a premier tool for chemically dissecting live cells and tissues. This review discusses the molecular contrast of SFG and CRS microscopy and highlights several of the advanced imaging capabilities that have impacted biological and biomedical research.

Figure 1

Comparison of immunofluorescence images (a–d ) with Raman-based images (e–h). Fluorescence stains for actin (a; red ), Golgi apparatus (b; green), and nucleus (c; blue) were used. The different compartments in the cell can also be distinguished by sets of Raman intensities at selected wave numbers determined by an information-based spectral decomposition approach (13): actin (e; red ), Golgi apparatus ( f; green), and nucleus ( g; blue). Panels d and h show the overlapped fluorescence and Raman-based images, respectively. Figure reproduced with permission from Reference .

Figure 2

Chemical information in the CH stretching vibrational range. (a) Raman (black) and IR ( gray) spectra of 1-octanol. (b) Band assignments of 1-octanol based on surface-sensitive sum-frequency generation measurements. Ssp, ppp, and sps (signal, visible, IR beam) correspond to the polarization orientations of the beams (s, surface polarized; p, plane polarized). Panels a and b reproduced in part with permission from Reference . Copyright 2005 American Chemical Society. Raman spectra of (c) elastin, a structural protein; (d ) cellulose, a carbohydrate; (e) palmityl palmitate, a wax ester; ( f ) cholesterol; ( g) cholesteryl linoleate, a cholesteryl ester; and (h) tristearin, a triglyceride.

Figure 3

Energy diagrams of (a) sum-frequency generation and (b) coherent anti-Stokes Raman scattering. In these diagrams, |a〉 is the ground state, |b〉 is the first vibrationally excited state, and |n〉, |n′〉 are higher-energy states in the material. The arrows are not necessarily ordered in time. The arrows to the far right correspond to the signal field: An upward arrow corresponds to field absorption, whereas a downward arrow denotes a field emission.

Figure 4

Vibrational phase microscopy with interferometrically detected coherent anti-Stokes Raman scattering (CARS). (a) CARS amplitude of poly(methyl)-methacrylate (red ) and polyethylene (blue). (b) Corresponding vibrational phase of the two polymers. (c) CARS amplitude image and (d ) corresponding phase image of a sample containing a sheet of polyethylene (PE), poly(methyl)-methacrylate (PMMA) beads, and water. The images were acquired at 2,940 cm⁻¹, corresponding to the dashed line in panels a and b. (e) Density graph of the projected amplitude and phase points in the complex plane. Images are plotted below for several locations in the complex plane, showing chemically distinct contrast that enables the separation of the several components. Figure reproduced in part with permission from Reference . Copyright 2010 American Chemical Society. Courtesy of Herman Offerhaus, Twente University.

Figure 5

Surface-sensitive sum-frequency generation (SFG) imaging of a patterned monolayer of hexadecane-dithiocarboxylic acid and 16-phenyl-hexadecanethiol. (a) Image taken at 2,875 cm⁻¹ showing strong contrast from the methyl group of hexadecane-dithiocarboxylic acid. (b) Image taken at 3,065 cm⁻¹ showing strong contrast from the aromatic CH stretching mode. Note that the contrast in panels a and b is inverted. (c) Close-up of the highlighted area in panel a. (d ) SFG spectra of the selected blue and green areas in panel c. Spectra are offset for clarity. Based on results presented in Reference . Courtesy of Steven Baldelli, University of Houston.

Figure 6

Sum-frequency generation imaging of noncentrosymmetric biomolecular compounds using tightly focused beams and raster scanning of the sample: (a) cellulose fibers, (b) collagen fibers from rat tail tendon, and (c) cholesterol monohydrate crystals. Images were taken at 2,950 cm⁻¹. Scale bar is 20 μm.

Figure 7

Spectral stimulated Raman scattering (SRS) imaging of human lung A549 cancer cells in the 2,810 cm⁻¹ to 2,980 cm⁻¹ range. (Top row) The projected SRS intensity image of the spectral data stack. (Middle row) The data represented by the dominant three principal components (PCs) of the spectral data stack plotted as red (PC1), blue (PC2), and green (PC3). Each pixel in the image is assigned an RGB (red, green, blue) value, according to the weight of each principal component. The color differences in the image correspond to differences in the spectral properties between the pixels. (Bottom row) The same data represented with the help of a K-means clustering analysis. Each color (dark blue, cyan, green, yellow, and red ) corresponds to a characteristic spectrum, each found by the clustering analysis. In this case, the data are decomposed into five distinct clusters. The first column (a–c) and second column (d–f ) show control cells. The third column ( g–i ) shows cells treated with a low dose of indole-3-carbinol (IC3), an anticancer drug. The fourth column ( j–l ) shows cells treated with a high dose of IC3. Note that differences between the nuclear and cytosolic parts of the cells are readily recognized in the spectral images, which facilitates identification of phenotype differences resulting from drug treatments.