Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

The Raman scattering phenomenon was first observed in 1928 by C. V. Raman¹. When an incident photon is interacting with a sample, an inelastic scattering event can spontaneously occur, in which the energy change of the photon matches a vibrational transition of the analyzed chemical species. This process does not require the use of a chemical tag, making it a versatile, label-free tool for chemical analysis while minimizing sample perturbation. Despite its advantages, spontaneous Raman scattering suffers from a low scattering cross-section (typically 10¹¹ lower than the infrared [IR] absorption cross-section), which necessitates long acquisition times for analysis². Thus, the quest for increasing the sensitivity of the Raman scattering process is essential in pushing Raman technologies for real-time imaging.

One effective way to greatly enhance the sensitivity of Raman scattering is through coherent Raman scattering (CRS) processes, for which two laser pulses are typically used to excite molecular vibrational transitions^3,4. When the photon energy difference between the two lasers matches the vibrational modes of sample molecules, strong Raman signals will be generated. The two most commonly used CRS processes for imaging are coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS)⁵. Over the past two decades, technological developments have advanced CARS and SRS microscopy techniques to become powerful tools for label-free quantification and elucidation of chemical changes in biological samples.

Chemical imaging by CARS microscopy can be dated to 1982 when laser scanning was first applied to acquire CARS images, demonstrated by Duncan et al⁶. The modernization of CARS microscopy was greatly accelerated after the wide applications of laser scanning multiphoton fluorescence microscopy⁷. Early work from the Xie group using high repetition rate lasers has transitioned CARS to be a high-speed, label-free, chemical imaging platform for the characterization of molecules in biological samples^8,9,10. One of the major issues for CARS imaging is the presence of a nonresonant background, which reduces the image contrast and distorts the Raman spectrum. Many efforts have been made to either reduce the nonresonant background^{11,12,13,14,15} or to extract resonant Raman signals from the CARS spectra^16,17. Another advancement that has greatly advanced the field is hyperspectral CARS imaging, which allows for spectral mapping at each image pixel with improved chemical selectivity^18,19,20,21.

Stimulated Raman scattering (SRS) is a younger imaging technology than CARS, although it was discovered earlier²². In 2007, SRS microscopy was reported using a low repetition rate laser source²³. Soon, several groups demonstrated high-speed SRS imaging using high repetition rate lasers^24,25,26. One of the major advantages of SRS microscopy over CARS is the absence of the nonresonant background²⁷, although other backgrounds such as cross-phase modulation (XPM), transient absorption (TA), two-photon absorption (TPA), and photothermal (PT) effect, may occur with SRS²⁸. In addition, the SRS signal and sample concentration have linear relationships, unlike CARS, which has a quadratic signal-concentration dependence²⁹. This simplifies chemical quantification and spectral unmixing. Multicolor and hyperspectral SRS has evolved in different forms^{30,31,32,33,34,35,36}, with spectral focusing being one of the most popular approaches for chemical imaging^37,38.

Both CARS and SRS require the focusing of the pump and Stokes laser beams onto the sample to match the vibrational transition of the molecules for signal excitation. CARS and SRS microscopes also share a lot in common. However, the physics underlying these two processes, and signal detections involved in these microscopy technologies have disparities^3,39. CARS is a parametric process that does not have net photon-molecule energy coupling³. SRS, however, is a nonparametric process, and contributes to energy transfer between photons and molecular systems²⁷. In CARS, a new signal at anti-Stokes frequency is generated, while SRS manifests as the energy transfer between the pump and Stokes laser beams.

CARS signal satisfies Eq (1)²⁸.

    (1)

Meanwhile, SRS signal can be written as Eq (2)²⁸.

    (2)

Here, I_p, I_s, I_CARS, and ΔI_SRS are the intensities of the pump beam, Stokes beam, CARS signal, and SRS signals, respectively. χ⁽³⁾ is the third-order nonlinear optical susceptibility of the sample, and is a complex value composed of real and imaginary parts.

These equations express the spectral profiles and signal-concentration dependence of CARS and SRS. Differences in physics result in disparate detection schemes for these two microscopy technologies. Signal detection in CARS usually involves spectral separation of newly generated photons and detection using a photomultiplier tube (PMT) or charge-coupled device (CCD); for SRS, the energy exchange between the pump and Stokes beams is usually measured by high-speed intensity modulation using an optical modulator and demodulation using a photodiode (PD) paired with a lock-in amplifier.

Although many technological developments and applications have been published in recent years in both CARS and SRS fields, no systematic comparisons of the two CRS techniques have been performed on the same platform, especially for hyperspectral CARS and SRS microscopy. Direct comparisons in sensitivity, spatial resolution, spectral resolution, and chemical separation capabilities would allow biologists to select the best modality for chemical quantification. In this protocol, detailed steps to construct a multimodal imaging platform with both hyperspectral CARS and SRS modalities based on a femtosecond laser system and spectral focusing are provided. The two techniques have been compared in the forward direction for spectral resolution, detection sensitivity, spatial resolution, and imaging contrasts of cells.