Third Harmonic Generation microscopy distinguishes malignant cell grade in human breast tissue biopsies

Abstract

The ability to distinguish and grade malignant cells during surgical procedures in a fast, non-invasive and staining-free manner is of high importance in tumor management. To this extend, Third Harmonic Generation (THG), Second Harmonic Generation (SHG) and Fourier-Transform Infrared (FTIR) spectroscopy were applied to discriminate malignant from healthy cells in human breast tissue biopsies. Indeed, integration of non-linear processes into a single, unified microscopy platform offered complementary structural information within individual cells at the submicron level. Using a single laser beam, label-free THG imaging techniques provided important morphological information as to the mean nuclear and cytoplasmic area, cell volume and tissue intensity, which upon quantification could not only distinguish cancerous from benign breast tissues but also define disease severity. Simultaneously, collagen fibers that could be detected by SHG imaging showed a well structured continuity in benign tumor tissues, which were gradually disoriented along with disease severity. Combination of THG imaging with FTIR spectroscopy could provide a clearer distinction among the different grades of breast cancer, since FTIR analysis showed increased lipid concentrations in malignant tissues. Thus, the use of non-linear optical microscopy can be considered as powerful and harmless tool for tumor cell diagnostics even during real time surgery procedures.

Polarization‐resolved SHG imaging as a fast screening method for collagen alterations during aging: Comparison with light and electron microscopy

“…The first hyperpolarizability or β coefficient was calculated from PSHG images [ 11 ] defined as: where I ∥ and I ⊥ represent SHG intensity in a pixel of an image detected through the analyzer oriented parallel and perpendicularly to the incident laser polarization, respectively. The incident laser polarization was kept constant throughout the measurements, and it was parallel to the horizontal axis of all images.…”

“…[ 9,10 ] Moreover, TPEF and SHG have been used in multimodal imaging studies simultaneously with the third harmonic generation (THG), as one more modality of label‐free nonlinear imaging, revealing, even more, mutually complementary information of the specimen. [ 11–13 ]…”

Raman Spectroscopy Reveals Abnormal Changes in the Urine Composition of Prostate Cancer: An Application of an Intelligent Diagnostic Model with a Deep Learning Algorithm

“…With the application of advanced optical technology, such as third harmonic generation (THG) microscopy, surgical biopsy has improved in speed and accuracy. [5][6][7][8][9][10][11] However, surgical biopsy is invasive and causes pain for patients, so it is not suitable for early cancer screening during physical examination. Liquid biopsy is a noninvasive cancer diagnosis method that detects highly sensitive biomarkers, such as circulating tumor cells (CTCs) and exosome microRNA, that could be used in early cancer detection [12] and postoperative monitoring.…”

Advances in non linear imaging microscopy for tissue and in-vivo biological samples characterization

“…The threshold was set in a way to allow the higher THG signal that indicates mainly inhomogeneities and lipid content of the cell area to be analyzed and measured. [71] After the optically isolation of the cells from each sample like presented in Figure 2.2 follows the quantification processing which includes the calculation of each cell volume, the mean THG area of each cell and the mean THG intensity signal of the tissue area. Quantification of the THG signal was accomplished by setting a threshold that allows the higher THG signal to be analysed.…”

“…Indeed, at the cellular level THG signal has been largely correlated to lipid bodies (LBs) [65], [69], which are highly dynamic and functional cellular organelles, actively involved in inflammation and cancer [70]. Thus, THG utilized to shed light on cancer research as a novel imaging diagnostic tool for differentiating malignant from benign breast tissue samples and discriminating among the different grades of cancer [71]. Two different approaches for data analysis were followed, a simple time consuming manual based analysis and a faster via the application of a deep-learning technique to the obtained nonlinear images.…”