Collagen has hierarchical structure and often undergoes remodeling at the tumor margin [1]. Polarimetric second harmonic generation (SHG) microscopy can be used to investigate changes in the collagen structure and provide insight into the metastatic progression of cancer. In this work, we apply double Stokes-Mueller polarimetry (DSMP) to investigate the disorder of collagen at the tumor margin. The disorder is analyzed at several levels of spatial organization – texture analysis (gray level co-occurrence matrix (GLCM) method) is applied at the microscopic tissue organizational level, while ultrastructure is analyzed with DSMP to obtain nonlinear susceptibility tensor for each image voxel. This allows to comprehensively investigate the changes occurring in collagen during tumor progression.
[1] Winkler, J. et al., “Concepts of extracellular matrix remodelling in tumour progression and metastasis”, Nat Commun 11, 5120 (2020).
Development and metastasis of cancer are known to change the structure of extracellular matrix (ECM), which affect the tumor's further growth and spread. A substantial part of ECM is comprised of collagen, which is a noncentrosymmetric structure. As a result, it generates second harmonic signals, dependent on the polarization of incoming light. This property of collagen led to the applications of polarization-resolved second-harmonic generation (P-SHG) microscopy in investigating collagen ultrastructure changes in different cancers.
In this work, multiphoton absorption fluorescence (MPF), third-harmonic generation (THG) and polarimetric second-harmonic generation (P-SHG) measurements were performed on various types and staging of human melanoma histological sections. Reduced polarimetry techniques, employing linear and circular polarization states, were used to obtain polarimetric SHG parameters of collagen in both normal and cancerous tissues. These parameters provide important information about the structural properties of collagen.
The parameter distributions were analyzed using a grey-level co-occurrence matrix (GLCM), which allows to obtain statistical parameters, such as correlation, contrast, entropy, angular second moment and inverse difference moment.
Statistical tests were performed on polarimetric and texture analysis data in order to determine whether parameter distribution differences in normal and cancerous tissues are statistically significant.
Furthermore, a machine learning classifier algorithm was trained to distinguish normal tissues from cancerous using aforementioned polarimetric and texture parameters as predictors. Firstly, separate training and testing datasets were formed from each sample and classification was carried out for each of them individually and afterwards, a common training dataset was used for all samples.
The results suggest that normal and cancerous skin tissues can be distinguished from each other with the help of multimodal nonlinear polarimetric microscopy. Also, depending on the type and stage of melanoma, the differences in some polarimetric and texture parameters are more pronounced, suggesting its possible application in melanoma diagnostics and differentiation.
Extracellular matrix (ECM) has important functions in cell proliferation, differentiation, and migration, which influence the development and progression of cancer. ECM in tumor microenvironment experiences changes in composition and structure that can appear early in tumor development and could serve as a biomarker for cancer diagnostics. In addition, some changes in ECM may correlate with the rate of tumor progression or its tendency to form metastases and would allow to predict future tumor development [1].
Collagen is an important structural protein found in ECM. It has a non-centrosymmetric structure, and, thus, can be easily visualized using second harmonic generation (SHG) microscopy. SHG microscopy employs certain polarimetric techniques to gain detailed information about the organization of collagen in various tissues [2].
In this work, polarimetric SHG microscopy is used to acquire collagen images from normal and cancerous regions of human colon and pancreas histological samples. Texture analysis is performed on SHG intensity and polarization images to characterize the distribution of ultrastructure parameters in the tissue. Significant differences are observed in collagen ultrastructure between normal and tumor areas. Further, collagen structures of colon and pancreas tumor microenvironments are compared to investigate relative differences in ECM organization between the tissues. Finally, a machine learning classifier is used to group the acquired images in tumor and normal groups. The results show potential for development of novel cancer diagnostic technique using polarimetric second harmonic generation microscopy and texture analysis.
[1] Winkler, J. et al., “Concepts of extracellular matrix remodelling in tumour progression and metastasis”, Nat Commun 11, 5120 (2020).
[2] Golaraei, A. et al., “Polarimetric second-harmonic generation microscopy of the hierarchical structure of collagen in stage I-III non-small cell lung carcinoma,” Biomed. Opt. Express 11, 1851-1863 (2020).
Polarimetric second harmonic generation (SHG) microscopy techniques are powerful tools to reveal sub-molecular information from biological specimens. Among biological samples collagen with a noncentrosymmetric structure and efficient SHG conversion has been the focus of many studies. Since collagen remodeling takes place due to cancer progression, it is important to develop tools to detect and understand the ultrastructural changes in collagen assembly using polarimetric nonlinear microscopy. Several polarimetric techniques have been developed to probe susceptibility ratios, in-plane orientation, and out of the image plane orientation of collagen. Polarization-In Polarization-Out (PIPO) and SHG circular dichroism (SHG-CD) techniques have been used to calculate the out of the image plane orientation and chirality of collagen. In this work, we study the correlation between SHG-CD and the chiral susceptibility ratio (C) in order to reveal the collagen chirality, and the collagen fiber tilt out of image plane. A numerical modeling is used to understand the relation between aforementioned parameters and the chirality and out of the image plane orientation of collagen. The results of numerical modeling show similar behaviors for SHG-CD and the chiral susceptibility ratio (C) calculated from PIPO measurements. The results obtained from rat tail tendon collagen confirms that the sign of both SHG-CD and C ratio changes by flipping the sample as it is predicted by the numerical modeling. The results also show that both SHG-CD and C ratio may become miscalculated when antiparallel chiral fibers are present in the focal volume of the microscope. The results of this study confirm that polarimetric SHG microscopy techniques are able to reveal 3D structure of biological samples and therefore they are beneficial to the diagnosis of collagen related diseases.
Multicontrast nonlinear microscopy with SHG and THG were used to image normal and cancerous human colon histology samples, and texture analysis was applied to investigate the changes in collagen structure occurring during carcinogenesis.
Polarimetric second harmonic generation (SHG) microscopy study of collagen in
cancerous and normal tissues showed the differences in SHG intensity, susceptibility ratio R and
fiber orientation distribution suggesting a modified collagen structure.
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