Coherent Raman Scattering (CRS) Microscopy: A vast and growing range of applications revealed

In addition to imaging lipid-rich brain structures, such as myelin, and its degeneration due to diseases, such as multiple sclerosis, it has now been shown that SRS can characterize label-free biophysical/biochemical pathological Amyloid-β deposits for Alzheimer’s research. More importantly, SRS is sensitive to protein secondary structure and misfolding, one of the key mechanisms underlying several neurodegenerative diseases [1].

CARS and SRS microscopy is increasingly being used to follow the uptake and distribution of pharmaceuticals in cells and tissues. Even the metabolism of administered compounds can be studied based on vibrational spectra, opening up entirely new perspectives for the study of drug modes of action. CRS is also emerging as a versatile tool for quality control of pharmaceutical formulations, allowing for high-resolution, label-free imaging of active pharmaceutical ingredients (APIs), and excipients to determine their spatial distribution on tablet surfaces [2] or in powder formulations. Interestingly, even different API polymorphs can be distinguished spectrally [3]. Some of these new label-free procedures are already replacing previous cumbersome ones done with electron microscopy.

SRS and CARS, often in conjunction with two-photon-excited autofluorescence and second-harmonic generation, provide a versatile toolbox for studying tissue physiology and pathology. Early and sensitive detection for a wide spectrum of cancers [4,5,6], neurodegenerative diseases [1], and fibrosis [7], among many others, has been demonstrated. First studies are beginning to show how deep learning algorithms can aid in CRS-based tissue classification and diagnostics [4]. 

Label-free cytopathology of urine samples for bladder cancer [8] and of pap smears for cervical cancer [4,5,6] has been demonstrated. Cancerous cells were identified with high accuracy using deep-learning analyses [9].

Label-free fingerprinting of various cell and tissue phenotypes has been demonstrated [1]. Exciting applications for cell and gene therapy are expected to arise in the near future. Even label-free high-content screening applications are on the horizon.

Two-color in vivo SRS imaging of a beating Zebrafish heart [10] was shown. Metabolic imaging of organisms ranging from bacteria to C. elegans is enabling the development of applications from biofuels research to toxicity sensing.

Label-free imaging of lipids, proteins, and water to determine their distribution in emulsions goes a long way for analyzing food samples. Furthermore, CARS was shown to be sensitive to NaCl concentrations below the 1-percent level in solution [11]. Interestingly, this sensitivity arises from ion-induced changes in the intermolecular coordination of the solvent molecules. The coordination changes leave a fingerprint in the CARS spectra, suggesting it can be used as a more generally applicable detection mechanism. 

Prof. Wei Min (Columbia University, New York, USA) delivered a fantastic keynote presentation on the emerging applications of “vibrational tags” [12]. Based on alkyne or nitrile bonds or the substitution of hydrogen by deuterium, these tags produce a unique spectral peak in the “silent region” of SRS spectra. Because these tags are so small (just a single chemical bond added to the molecule of interest, compared to a ‘giant’ antibody or fluorescent protein), non-perturbative imaging of small molecules is now becoming a possibility. Some of the coolest applications included:

• Following the distribution of tagged compounds (drugs, fatty acids, nutrients, metabolites, …) in cells and tissues. An example in lipid biology that could not be studied previously by any other means demonstrated the aberrant subcellular localization of unhealthy saturated fatty acids, and the restoring effects of healthy unsaturated ones found, for example, in fish oil [13].
• Observing local protein synthesis in the dendritic spines of neurons, one of the proposed key steps in memory formation.
• Pushing this concept even further, Stimulated Raman Excited Fluorescence Spectroscopy and Imaging was introduced, a new technique [14] that combines single-molecule sensitivity with the chemical specificity of coherent Raman imaging.
• Super-multiplexed optical imaging [15]: 10-color imaging in brain tissues was shown using a newly developed palette of vibrational tags, termed “Carbow”, which are opening up new avenues for future studies of complex biological processes with highly-multiplexed optical readouts.

1. Label-free imaging of amyloid plaques in Alzheimer’s disease with stimulated Raman scattering microscopy.
   Minbiao Ji, Michal Arbel, Lili Zhang, Christian W. Freudiger, Steven S. Hou, Dongdong Lin, Xinju Yang, Brian J. Bacskai and X. Sunney Xie.
   Sci Adv. 2018 Nov; 4(11): eaat7715. Published online 2018 Nov 16. doi: 10.1126/sciadv.aat7715
2. Preparation and characterization of multi-component tablets containing co-amorphous salts: Combining multimodal non-linear optical imaging with established analytical methods.
   Ojarinta R, Saarinen J, Strachan CJ, Korhonen O, Laitinen R.
   Eur J Pharm Biopharm. 2018 Nov;132:112-126. doi: 10.1016/j.ejpb.2018.09.013. Epub 2018 Sep 22.
3. Multimodal non-linear optical imaging for the investigation of drug nano-/microcrystal-cell interactions.
   Darville N. et al. 
   Eur J Pharm Biopharm 96, 338-348, (2015). 
4. Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy.
   Lili Zhang, Yongzheng Wu, Bin Zheng, Lizhong Su, Yuan Chen, Shuang Ma, Qinqin Hu, Xiang
   Zou, Lie Yao, Yinlong Yang, Liang Chen, Ying Mao, Yan Chen, Minbiao Ji,Theranostics 2019, Vol. 9, Issue 9
5. Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy.
   Ji M1, Lewis S2, Camelo-Piragua S3, Ramkissoon SH4, Snuderl M5, Venneti S3, Fisher-Hubbard A3, Garrard M2, Fu D1, Wang AC2, Heth JA2, Maher CO2, Sanai N6, Johnson TD7, Freudiger CW8, Sagher O2, Xie XS9, Orringer DA10.
   Sci Transl Med. 2015 Oct 14;7(309):309ra163. doi: 10.1126/scitranslmed.aab0195.
6. Virtual staining of colon cancer tissue by label-free Raman micro-spectroscopy.
   Petersen D, et al.  Mavarani L, Niedieker D, Freier E, Tannapfel A, Kötting C, Gerwert K, El-Mashtoly SF.
   Analyst. 2017 Apr 10;142(8):1207-1215. doi: 10.1039/c6an02072k.
7. Continuous grading of early fibrosis in NAFLD using label-free imaging: A proof-of-concept study.
   Pirhonen, J. et al.
   PloS one 11, e0147804, (2016). 
8. Noninvasive Diagnosis of High-Grade Urothelial Carcinoma in Urine by Raman Spectral Imaging.
   Yosef HK, Krauß SD, Lechtonen T, Jütte H, Tannapfel A, Käfferlein HU, Brüning T, Roghmann F, Noldus J, Mosig A, El-Mashtoly SF, Gerwert K.
   Anal Chem. 2017 Jun 20;89(12):6893-6899. doi: 10.1021/acs.analchem.7b01403. Epub 2017 Jun 5.
9. Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology.
   Krauß SD, Roy R, Yosef HK, Lechtonen T, El-Mashtoly SF, Gerwert K, Mosig A.
   J Biophotonics. 2018 Oct;11(10):e201800022. doi: 10.1002/jbio.201800022. Epub 2018 Jul 5.
10. Dual-phase stimulated Raman scattering microscopy for real-time two-color imaging.
    He, R.; Xu, Y.; Zhang, L.; Ma, S.; Wang, X.; Ye, D.; Ji, M.*.
    Optica 2017, 4 (1), 44-47.
11. Jonathan Brewer, unpublished. 
12. Applications of vibrational tags in biological imaging by Raman microscopy.
    Zhao Z, Shen Y, Hu F, Min W.
    Analyst. 2017 Oct 23;142(21):4018-4029. doi: 10.1039/c7an01001j.
13. Metabolic activity induces membrane phase separation in endoplasmic reticulum.
    Yihui Shen, Zhilun Zhao, Luyuan Zhang, Lingyan Shi, Sanjid Shahriar, Robin B. Chan, Gilbert Di Paolo, and Wei Min.
    PNAS 2017, 114(51):13394-13399. DOI: 10.1073/pnas.1712555114
14. Stimulated Raman Excited Fluorescence Spectroscopy and Imaging
    (Letter, Nature Photonics, 01 April 2019)
15. Supermultiplexed optical imaging and barcoding with engineered polyynes.
    Hu F, Zeng C, Long R, Miao Y, Wei L, Xu Q, Min W.
    Nat Methods. 2018 Mar;15(3):194-200. doi: 10.1038/nmeth.4578. Epub 2018 Jan 15.