Application SHG imaging method for cancer metastaticApplication SHG imaging method for cancer metastatic

Application of coherent nonlinear microscopy

Second-harmonic generation (SGH) microscopy

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SGH microscope obtain contrasts from variations in a specimen’s ability to generate second-harmonic light from the incident light. It is different from the conventional optical microscopes; they obtain contrast by detecting variations in optical density, path length, or refractive index of the specimen. SHG is a coherent process in which two photons are upconverted to exactly twice the frequency (half the wavelength) of the excitation laser.  SHG does not involve the excitation of molecules like other techniques such as fluorescence microscopy. Therefore, the molecules should not suffer the effects of phototoxicity or photobleaching. Label-free 3D imaging is possible by Second-harmonic generation microscopy. SHG was first discovered in crystals in 1961 by Franken et al. 57. The first biological SHG imaging was demonstrated using rat tail tendon with a resolution of ~50 ?m 58. Later Brown et al. (2003) 59 described SHG microscopy for dynamic imaging of collagen in melanoma. Lin et al. (2006) 17 used SHG to distinguish basal cell carcinoma from normal dermal stroma. Kirkpatrick et al. (2007) 18 evaluated ovarian cancer by SHG microscopy. Kedrin et al. (2008) 20 described an intravital SHG imaging method for cancer metastatic characteristic imaging through a mammary imaging window. Provenzano et al. (2008) 60 demonstrated SGH based computational analysis of endogenous and exogenous fluorophores in breast cancer. Nadiarnykh et al. (2010) 61 described extracellular matrix’s abnormalities in ovarian cancer through SHG. In the same year, Williams et al. 23 demonstrated a laparoscopic SHG module for epithelial ovarian cancer imaging. Ajeti, et al. (2011) 62 demonstrated Structural changes in mixed Col I/Col V collagen in human breast cancer. Adur et al. (2011) 30 showed SHG microscopy for human serous ovarian tumor imaging. In 2011, Chen X 63 showed a quantitative SHG imaging of cancerous thyroid tissue based on collagen change. Xiong et al. (2011) 26 described SHG imaging of human basal cell carcinoma and squamous cell carcinoma. In 2012, Adur et al. (2012) 64 showed SHG for human ovarian cancer imaging. Xu et al. (2013) 42 used SHG microscopy for quantitative label-free lung cancer imaging. Tilbury and Campagnola (2015) 65 demonstrated SGH Microscopy for Ovarian and Breast Cancer Imaging. Watson et al. (2012) 66 described a SHG module for ovarian carcinoma analysis in mice model. Ambekar et al. (2012) 67 demonstrated a method to quantify collagen structural change in breast cancer by SHG. Eliceiri et al. (2014) 68 demonstrated an automated method for aligned collagen determination in human breast cancer. Meyer et al. (2012) 38 used SHG microscopic imaging for advanced carcinoma of the hypopharynx, larynx, and left tonsil. Burke et al. (2012) 69 demonstrated matrix alterations during breast tumor progression by using SGH imaging modality. Zheng et al. (2011) 70 described a label-free SHG method for comparing of normal and fibroadenomal breast tissue. 

Wen et al. (2014) 71 described a texture analysis method for SHG generated image of ovarian cancer. Bredfeldt et al. (2014) 72 developed an automated tracking algorithm called fiber extraction (FIRE) method of SHG image of breast cancer. Tilbury et al. (2014) 65 analyzed the collagen isoform Col I and Col III of ovarian cancer model by SHG. Chen et al. (2014) 46 used SHG for cancerous esophagus imaging. Han et al. (2008) 73 demonstrated SHG in tumor collagen and showed differences between reactive stroma and healthy stroma. Galli et al. (2013) 52 used SHG microscopy for kidney tumor imaging. Pal et al. (2015) 13 applied SGH for hamster oral mucosal neoplasia. Also recently in 2016, there was a development of SGH for non-invasive oral epithelial dysplasia imaging based on SHG by the same group 74. Recently Zeitoune, et al. (2017) 75 demonstrated semiautomatic collagen fiber quantification based SGH imaging of epithelial ovarian cancer. Also recently in 2017, Bower et al. 56 developed SHG and applied in rat mammary tumor imaging.

Current progress in SHG microscopy, it is showing that there are a variety of possible future applications of SHG in the study of tumor progression.

Third-harmonic generation (TGH) microscopy

THG is a third order NLO process and unlike SHG, is not limited to non-centrosymmetric structures. It is just frequency tripling as compare to SHG. Tsang (1995) 76 showed this THG for the first time and based on it, Barad et al. (1997) 77 developed a THG microscope for the first time. THG requires three photons to generate one at a tripled frequency. Tai et al. (2007) 78 used silver nanoparticles as THG contrast agent, and imaged cancer cell at molecular level in vitro. Adur et al. (2011) 30 successfully applied THG microscopy for human serous ovarian tumor imaging. They had successfully identified transformation of epithelium surface of human ovarian cancer through THG microscopy. Also this study suggests further in vivo studies to quantitatively analyze endogenous optical biomarkers of the ovary. Weigelin, Bakker, and Friedl (2012) 79 used third harmonic generation microscopy for collective melanoma cell invasion analysis. This study showed potentials of THG in molecular classification of invasion modes and routes in melanoma cells. Harpel et al. (2016) 80 developed a label-free imaging of microbubbles done by THG. This study suggests that contact-free THG images of microbubbles attached to tumor cells could enable unseen cancer to be imaged during surgery. This study demonstrates the potential of THG for endoscopic module development for point-of-care diagnostics as well as early detection of cancer. TGH based human brain tumors was done by Kuzmin et al. (2016) 81. This study THG has the potential to discriminate among the different cancer cell types.  Recently THG microscopy was used to distinguish breast cancer cell subtypes (Gavgiotaki et al., 2017) 82.

Coherent anti-Stokes Raman scattering (CARS) microscopy

Maker and Terhune (1965) 83 mentioned the idea about CARS for the first time. Later Begley, Harvey, and Byer (1974) invented CARS microscopy 84. CARS microscopy is a dye-free method which images structures by displaying the characteristic intrinsic vibrational contrast of their molecules. CARS probes chemical bond vibration levels, therefore, provides some degree of molecular specificity, particularly for small molecules. In a sense, CARS contrast can be thought of like a cross between SHG and fluorescence. The main advantage of this method is that the sample remains almost unaffected. Légaré et al. (2006) 85 demonstrated CARS endoscopy for cancer imaging. Le, Huff, and Cheng (2009) 86 used CARS for cancer cell behavior study in excess lipid environments in vivo and in vitro. Gao et al. (2011) 87 described label-free lung cancer diagnosis using CARS. Hammoudi et al. (2011) 88 demonstrated an automated nuclear segmentation based CARS imaging method by using artificial neural networks. Meyer et al. (2012) 38 used CARS microscopic imaging modality for advanced carcinoma of the hypopharynx, larynx, and left tonsil diagnosis. Xu et al. (2013) 42 used CARS microscopy for quantitative label-free lung cancer imaging. Galli et al. (2013) 52 used CARS microscopy for kidney tumor imaging. Chen et al. (2015) 63 designed an endo-microscopic probe, which is capable of identifying the location of the tumor in vivo during surgery. Legesse et al. (2015) 89 demonstrated a texture analysis based CARS microscopy for skin cancer detection. Recently Shadfan et al. (2017) 90 developed CARS based endomicroscope for oral cancer diagnosis. In association with nanoparticles, CARS microscopy was also used recently in vitro imaging of breast cancer cell line 54. Recently Weng et al. (2017) 91 combined a deep learning method and CARS for automated differential diagnosis of lung cancer.