Bottom-up approach to nonlinear optical imaging of biomaterials

Abstract

Biological tissues are complex systems. Conventional microscopy, ultimately limited to a spatial resolution of roughly half the wavelength of light, is often unable to see molecular details unless sophisticated and invasive methods are employed. However, recent advances in nonlinear optical-imaging techniques for biological tissues have resulted in both higher spatial resolution and better tissue contrast than achieved before.1 As a result, a great deal of focus has been placed on engineering nonlinear optical probes for use with two-photon excitedfluorescence and second-harmonic-generation (SHG) imaging. Both employ femtosecond-laser microscopy to measure nonlinear optical responses. In SHG, two photons at a fundamental frequency are converted into one at once or twice the harmonic frequency. In addition to the numerous organic and inorganic optical probes that have been developed for labeling tissues and cell membranes, certain cellular components, such as collagen, have intrinsic SHG properties.2 This makes SHG a favourable method for noninvasive imaging of large structures and cellular processes. However, it still lacks resolution at the molecular level. In addition, for smaller components the relationship between the intensity and polarization state of the nonlinear optical response and the molecule’s conformation remains elusive. Here, we provide preliminary insights into this dependence. In addressing this problem, the focus is usually placed on the imaging potential of the techniques. Very few studies have tackled the problem in a bottom-up approach, starting from the elementary bricks of the proteins—the individual amino acids— and working up to the protein itself. The nonlinear optical properties of these molecules can be observed using hyper-Rayleigh Figure 1. Quadratic hyperpolarizability of tryptophan (W)-rich peptides as a function of the number of these amino acids. Dots: Experimental data. Empty circles and squares: coherent and incoherent models, respectively.