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Coherent Raman Microscopy: From Instrumentation To Applications

Published: January 20, 2023


Over the last decade, there is a surge of interest in applying coherent Raman microscopy (CRM) to cell and tissue imaging1,2. CRM is a powerful chemical imaging technique with two popular variants: coherent anti-Stokes Raman scattering (CARS) microscopy and stimulated Raman scattering (SRS) microscopy. SRS is increasingly popular compared to CARS because it is less affected by non-Raman background interference and has a linear dependence on concentration3. CRM acquires molecular vibration information similar to Raman spectroscopy, but offers significantly improved acquisition speed4. This is achieved by coherent vibrational excitation through a pair of pulsed lasers with high peak intensity. Because CRM provides spatially resolved chemical mapping at high spatial and temporal resolution, it has found important applications in cancer diagnosis, lipid metabolism, cell metabolism, drug transport, and many others5,6,7,8. However, one major bottleneck that hinders widespread adoption of the technique is its technical complexity and lack of standardized protocols. Although commercial lasers and microscopes are already available, almost all systems that are currently used in the field involve various levels of customization of lasers, optics, microscopes, and detectors, which create a significant barrier for researchers interested in entering the field or adopting this technology. The lack of access further impedes biological applications. The aim of this methods collection is to provide detailed examples and guidance on instrumentation, new imaging approaches, and in vitro/in vivo cell or tissue imaging applications. We included seven publications from experts in the field that are summarized here.

Clark et al. present a side-by-side comparison of hyperspectral SRS and CARS in terms of sensitivity, spatial resolution, and spectral resolution9. Both techniques are implemented with spectral focusing of femtosecond lasers, a commonly employed approach that uses the spectrally chirped broadband femtosecond lasers to achieve narrowband Raman resolution. A direct comparison allows the assessment of detection differences and optimal selection of the modality for different chemical imaging applications.

De la Cadena et al. publish a new multiplex SRS method that can acquire broadband SRS images rapidly10. The technique is based on a custom differential multichannel lock-in amplifier detection, which allows for detection of up to 32 channels simultaneously. In addition, detailed noise characterization is shown, and a balanced detection method is demonstrated to eliminate laser noise. This protocol provides practical guidance for users interested in building multiplex SRS systems.

Shi et al. demonstrate the use of highly multiplexed Raman dyes in SRS imaging11. With pre-resonance enhancement, they demonstrate that SRS could provide sensitivity that is comparable to standard immunofluorescence. This method circumvents the limit of spectrally resolvable channels in conventional immunofluorescence and allows monitoring of many proteins simultaneously and studying their interactions. Detailed guidance on the sample processing and Raman dye staining is provided to facilitate adoption of this powerful method.

Miao et al. present vibrational imaging of swelled tissue and analysis (VISTA), a super-resolution imaging technique that combines SRS with expansion microscopy12. VISTA circumvents obstacles associated with fluorescent imaging and achieves label-free super-resolution volumetric imaging in biological samples with spatial resolution down to 78 nm. They further exploit a deep learning algorithm to achieve multi-component segmentation, including protein aggregates in cells and brain tissues.

Espinoza et al. present the use of real-time two-color SRS imaging of proteins and lipids for tissue diagnostic applications13. Two-color SRS imaging can generate H&E-equivalent images for pathology applications. This protocol describes the details of implementing an orthogonal modulation technique and spectral focusing for simultaneous two-color SRS imaging. Conversion of two-color SRS images to pseudo-H&E images is also demonstrated with mouse brain tissue to illustrate tissue diagnosis applications.

Yuan et al. publish a flexible chamber system for long-term SRS imaging of live cells14. Most of the existing live-cell observation chambers are designed for bright field and fluorescence imaging. They are incompatible with SRS detection because of the requirement of a high NA objective and condenser. The authors demonstrate 24 h time-lapse SRS imaging with the flexible chamber, which maintained constant temperature, CO2, and humidity.

Lastly, Wu et al. demonstrate simultaneous in vivo two-photon fluorescence (TPEF) and SRS imaging15. Two separate laser systems are combined to achieve optimal sensitivity for both imaging modalities to provide complementary information. In vivo SRS imaging of tissue has unique challenges because of the need for epi-detection of depolarized, backscattered light. This protocol discusses the details of the optimal setup of a TPEF-SRS microscope and how it can be used for in vivo imaging of the mouse’s spinal cord.

This collection of publications represents the current state-of-the-art in CRM instrumentation and technical advances. Researchers interested in both method development and applications will find this collection useful to compare setups and establish benchmarks (sensitivity, resolution, etc.) for constructing or optimizing their own systems. The guidelines and best practices provided in these protocols will inform the existing and future in vitro and in vivo applications. The large-scale adoption of the CRM technology still awaits key and innovative applications and the commercialization of complete CRM solutions. Nonetheless, standardization of imaging approaches and benchmarks is essential to ensure reproducibility and reliability and facilitate broader implementation of CRM16.

The field of CRM is growing at a rapid speed. The technical developments pushing the sensitivity, specificity, and resolution limit are continuing to drive the technology into new territories. For example, the spatial resolution of SRS is beginning to surpass the diffraction limit17,18. The sensitivity of electronic pre-resonance SRS is now approaching that of fluorescence19. As compared to fluorescence, CRM has distinct advantages. For example, the use of deuterium tracing allowed in vivo study of metabolic dynamics of small molecules at a subcellular resolution for the first time20. With small tags, it is possible to perform highly multiplexed Raman detection of 24 or more labeled targets21. The advances in artificial intelligence enable new capabilities for CRM by leveraging its high information content and molecular-specific features to segment and classify objects with subtle spatial and spectral feature differences22. Together, these technical advances in CRM are poised to accelerate and broaden applications in both biological and biomedical imaging.


The author has nothing to disclose.


The author acknowledges financial support from the National Institute of Health (NIH R35 GM133435) and the Eli Lilly Young Investigator Award.


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  2. Hill, A. H., Fu, D. Cellular imaging using stimulated Raman scattering microscopy. Analytical Chemistry. 91 (15), 9333-9342 (2019).
  3. Min, W., Freudiger, C. W., Lu, S., Xie, X. S. Coherent nonlinear optical imaging: Beyond fluorescence microscopy. Annual Review of Physical Chemistry. 62 (1), 507-530 (2011).
  4. Freudiger, C. W., et al. Label-Free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science. 322 (5909), 1857-1861 (2008).
  5. Hollon, T. C., et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nature Medicine. 26 (1), 52-58 (2020).
  6. Yue, S., et al. Cholesteryl ester accumulation induced by PTEN loss and PI3K/AKT activation underlies human prostate cancer aggressiveness. Cell Metabolism. 19 (3), 393-406 (2014).
  7. Fu, D., et al. In vivo metabolic fingerprinting of neutral lipids with hyperspectral stimulated Raman scattering microscopy. Journal of the American Chemical Society. 136 (24), 8820-8828 (2014).
  8. Fu, D., et al. Imaging the intracellular distribution of tyrosine kinase inhibitors in living cells with quantitative hyperspectral stimulated Raman scattering. Nature Chemistry. 6 (7), 614-622 (2014).
  9. Clark, M. G., Brasseale, K. A., Gonzalez, G. A., Eakins, G., Zhang, C. Direct comparison of hyperspectral stimulated Raman scattering and coherent anti-Stokes Raman scattering microscopy for chemical imaging. Journal of Visualized Experiments. (182), e63677 (2022).
  10. Dela Cadena, A., et al. Multiplex chemical imaging based on broadband stimulated Raman scattering microscopy. Journal of Visualized Experiments. (185), e63709 (2022).
  11. Shi, L., Wei, M., Min, W. Highly-Multiplexed tissue imaging with Raman dyes. Journal of Visualized Experiments. (182), e63547 (2022).
  12. Miao, K., Lin, L. E., Qian, C., Wei, L. Label-Free super-resolution imaging enabled by vibrational imaging of swelled tissue and analysis. Journal of Visualized Experiments. (183), e63824 (2022).
  13. Espinoza, R., Wong, B., Fu, D. Real-Time, two-color stimulated Raman scattering imaging of mouse brain for tissue diagnosis. Journal of Visualized Experiments. (180), e63484 (2022).
  14. Yuan, Y., Lu, F. A flexible chamber for time-lapse live-cell imaging with stimulated Raman scattering microscopy. Journal of Visualized Experiments. (186), e64449 (2022).
  15. Wu, W., Li, X., Qu, J. Y., He, S. In vivo imaging of biological tissues with combined two-photon fluorescence and stimulated Raman scattering microscopy. Journal of Visualized Experiments. (178), e63411 (2021).
  16. Manifold, B., Fu, D. Quantitative stimulated Raman scattering microscopy: Promises and pitfalls. Annual Review of Analytical Chemistry. 15 (1), 269-289 (2022).
  17. Qian, C., et al. Super-resolution label-free volumetric vibrational imaging. Nature Communications. 12 (1), 3648 (2021).
  18. Graefe, C. T., et al. Far-Field super-resolution vibrational spectroscopy. Analytical Chemistry. 91 (14), 8723-8731 (2019).
  19. Wei, L., Min, W. Electronic preresonance stimulated Raman scattering microscopy. The Journal of Physical Chemistry Letters. 9 (15), 4294-4301 (2018).
  20. Zhang, L., et al. Spectral tracing of deuterium for imaging glucose metabolism. Nature Biomedical Engineering. 3 (5), 402-413 (2019).
  21. Wei, L., et al. Super-multiplex vibrational imaging. Nature. 544 (7651), 465-470 (2017).
  22. Manifold, B., Men, S., Hu, R., Fu, D. A versatile deep learning architecture for classification and label-free prediction of hyperspectral images. Nature Machine Intelligence. 3, 306-315 (2021).

Cite this Article

Fu, D. Coherent Raman Microscopy: From Instrumentation To Applications. J. Vis. Exp. (191), e64882, (2023).More

Fu, D. Coherent Raman Microscopy: From Instrumentation To Applications. J. Vis. Exp. (191), e64882, (2023).

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