Broadband SRS microscopy is a powerful imaging technique that offers authentic chemical contrast to identify and disentangle the chemical constituents of a heterogeneous sample. The potential of this analytical tool can be beneficial to several research fields, ranging from materials science to histopathology. The downside of broadband SRS microscopy is the fact that it is technically demanding; the experimentalist not only requires know-how on broadband laser sources but also needs to manipulate the laser pulses to efficiently generate SRS, a signal that, in turn, needs to be measured with sophisticated detection schemes. This paper presents a protocol that describes a workflow to produce chemical maps of mixed chemical compounds using a multiplex broadband SRS microscope. Although the work described may be trivial for some laser physicists and nonlinear microscopists, it may not be the case for readers interested in the benefits of broadband SRS microscopy whose scientific knowledge resides outside these domains. Therefore, we aimed to detail every step to guide the wide audience interested in broadband SRS microscopy.
The protocol at hand started by showing how to prepare a simple yet spectroscopically rich sample composed of several strong and well-known Raman scatterers. We discussed how to obtain the broadband pump and narrowband Stokes beams necessary to set up an SRS microscope. Figure 5C shows a scheme of the SHG and OPO setups. Note that lens f1 focuses the fundamental beam onto LBO1 to generate the SHG, while a dichroic mirror reflects the SHG radiation and transmits the residual fundamental beam. A second lens f2 collimates the SHG beam. As f2 > f1, the SHG beam is expanded by a factor equal to f2/f1. A third lens f3 focuses the expanded SHG beam onto a second Type I LBO crystal (LBO2) cut at θ = 90° and φ = 29.0°. By pumping LBO2 with the aforementioned SGH (520 nm), radiation within the 680-910 nm range will emerge from LBO2 through difference-frequency generation (DFG), producing two beams: the signal and idler27 (Figure 5D,E). The latter is discarded while the former is amplified in the OPO cavity to deliver the pump pulses employed in the SRS experiments. The pump of the OPO at 520 nm, namely, the SHG beam, should not be confused with the pump of the SRS experiments (i.e., the signal beam of the OPO).
The contrast in SRS microscopy originates from a nonlinear signal generated at the focal spot of the microscope, a signal that demands confining a large number of photons in the sample plane at a given time. This photon confinement is achieved with a high numerical aperture (NA) microscope objective, an array of lenses that also sets the spatial resolution of the system: the higher the NA, the higher the spatial resolution. However, high NA objectives are densely packed with glass, which introduces positive GDD to pulsed radiation, a frequency chirp that ultimately broadens the temporal profile of the pulses39. Thus, the GDD introduced by the microscope objective might increase the duration of the broadband pump pulses, making it even longer than the Stokes temporal envelope and reducing the effective, accessible bandwidth of the Raman signal. Furthermore, this broadening might also introduce a distortion of the spectral profile of the measured SRS spectrum.
In CARS, the spectroscopically relevant signal emerges at wavelengths that differ from those of the excitation fields. A simple photomultiplier tube or charge-coupled device (CCD) camera can be used to integrate the CARS signal in time, summing up thousands of pulses to average out the laser noise. Instead, the SRS signal appears as a faint modulation transfer embedded within a strong and fluctuating laser background. Because this modulation is weak, the laser noise can readily overwhelm it, reducing both the imaging speed and the sensitivity of the SRS microscope. Therefore, before imaging, it is imperative to measure the relative intensity noise (RIN) to determine whether the laser is suitable for high-speed SRS imaging and to select the modulation frequency with the lowest noise. The RIN is defined as the noise power spectral density [δP(f), with W2/Hz units] of the laser normalized by the mean optical power (
)40,41. In other words, the RIN describes the normalized laser fluctuations at different frequencies (Eq [4]).
(4)
Thus, the RIN is a parameter of the SRS system that determines the ideal modulation frequency range for the experiments. For example, the olive bar in Figure 8 shows the ideal modulation frequency range for SRS imaging. In the case of narrowband SRS, the user should measure the RIN of both pump and Stokes to choose what beam needs to be modulated to achieve optimum performance. Note from Figure 8, for example, that the Stokes beam has a slightly higher RIN than the pump, implying that the SRG measurements would turn out noisier than their SRL counterparts. In the case of broadband SRS, the beam that should be modulated is the narrowband beam.
The angular dispersion D of the grating expresses the diffraction angle as a function of wavelength, and is defined as the derivative of the grating equation. For the Littrow configuration, the angular dispersion is given by Eq (5).
(5)
To get Eq (5), we assumed α = β, solved Eq (2) for m/d and inserted the result into dβ/ dλ. Note that in the Littrow configuration, β = sin-1(mλ/2d). Within the small-angle approximation, the change in position along the spectrum is fdβ ≈ dl (Figure 10). Thus, by inserting dβ into Eq (5), we can calculate the linear dispersion, a quantity with units of nm mm-1 using Eq (6):
(6)
For a diffraction grating operating in the Littrow configuration with 1,851.85 grooves/mm, d = 540 nm. If we use the first-order diffraction of light at ~789 nm, D = 0.0027 rad nm-1. With an f = 750 mm lens, we get a linear dispersion of ≈ 0.5 nm mm-1, translating into ≈ 7.8 cm-1mm-1. Thus, the focal length of the lens determines the "density" of nm per mm at the detector plane: The longer the focal length, the fewer nm per mm obtained, increasing the space between the spectral lines of the broadband pump. Conversely, with shorter focal lengths, there will be more nm per mm at the detector plane, reducing the space occupied by the dispersed pump.
Balanced detection improves the image quality and sensitivity of noisy setups. For example, according to the RIN spectra shown in Figure 8 and considering typical SRS with an amplitude of 1 x 10-5, the unbalanced signal-to-noise ratio (SNR) is ≈60. Using balanced detection (i.e., close to the shot-noise), it is possible to attain an SNR of ≈145. Figure 11 shows spectra and composite images under balanced and unbalanced conditions. Naturally, the effects of balanced detection impact the final results of the experiments, namely, the chemical maps. Supported by these results, we emphasize that balanced detection is a powerful technique to counter the detrimental effects of laser fluctuations on image quality. It is worth mentioning that balanced detection is best suited for noisy lasers, such as fiber oscillators. SRS microscopes operating with quiet optical light sources (e.g., solid-state lasers), may not require balanced detection.
The protocol also explains an approach based on nonlinear optics to find the spatiotemporal overlap between the pulses of these beams. We described the advantages of using the 1st instead of the 0th diffraction-order of an AOM as the modulated Stokes beam. Further, the detrimental effects of dispersion on the SRS generation efficiency were described with suggestions of ways to mitigate them via a prism compressor. In addition, the protocol explains how to align the prisms and highlights three critical aspects to consider for optimum performance. We not only discuss the relevance of the RIN for SRS microscopy but also show how to measure it with a lock-in amplifier and, with the RIN spectrum, define the best modulation frequency. With a concrete example, this paper explains how the grating equation helps in designing the detection chain. Finally, the protocol illustrates, with real SRS data, the structure of the SRS hypercube and how to analyze it with a conventionally used scientific programming language.
This protocol has three minor limitations. First, the detection scheme employed in this contribution consists of a nonconventional, multichannel lock-in detector designed and built in-house by Sciortino et al.26 As demonstrated previously25, this detector can be replaced by an off-the-shelf balanced photodiode. Although this modification concerns only the detector and leaves the protocol virtually unchanged, with a single-photodiode, one needs to scan each spectral component on the detector instead of measuring them all at once. Second, this protocol employs inline balanced detection, which requires inserting several optical elements into the beam path. These optical elements increase the system complexity and lead to losses of optical power and pulse broadening.
Inline balanced detection also demands that the two pump replicas pass through the sample, a situation that may not be ideal for light-sensitive samples, such as living cells, or for strongly birefringent ones in which the two pump replicas may experience different optical properties, thus canceling the balanced detection. Third, the protocol relies on a home-built OPO, a device that may not be readily available. However, alternatives to the broadband spectra delivered by the OPO are the supercontinua from nonlinear optical fibers or bulk crystals. The latter could be employed only with low repetition rate lasers (up to 5 MHz). Thus, as with every experimental design, the protocol at hand has some limitations. However, they are minimal and do not compromise the success of this approach.
Although a reference sample is described here, this protocol can successfully disentangle chemical species within cells and animal and plant tissues, such as cellulose, lipidic species, or proteins, finding practical applications in different biochemical quests or as a diagnostics tool in histopathology. Similarly, this protocol can be a valuable tool in material sciences. For example, following this protocol, one can interrogate the molecular composition and concentration of polymeric species42. Furthermore, this methodology is compatible with other nonlinear microscopy techniques, such as broadband microscopy based on pump-probe43 and heterodyne CARS44, four-wave mixing processes that, as with SRS, also require two excitation light beams and modulation-transfer measurements. Finally, some of the information contained in this paper can be applied to nonlinear imaging techniques that do not rely on modulation transfer techniques, but require aligning two or more pulsed laser beams, such as conventional CARS45 and SFG microscopies46.
In summary, this protocol describes a powerful methodology based on broadband SRS microscopy to extract chemical maps and their characteristic SRS spectra from chemically heterogeneous mixtures, delivering datasets that allow straightforward quantitative data analysis. The versatility and simplicity of the method also give the interested reader the possibility to adapt it to different nonlinear techniques.