Super‐Multiplex Nonlinear Optical Imaging Unscrambles the Statistical Complexity of Cancer Subtypes and Tumor Microenvironment. Issue 5 (19th December 2021)

• Record Type: Journal Article
• Authors: Li, Yanping; Shen, Binglin; Zou, Gengjin; Hu, Rui; Pan, Ying; Qu, Junle; Liu, Liwei
• Is Part Of: Advanced science. Volume 9:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-19
• Subjects: cancer research -- fluorescence lifetime microscopy -- nonlinear optical imaging -- tumor microenvironment; Science -- Periodicals; 505
• DOI: 10.1002/advs.202104379
• Languages: English
• ISSNs: 2198-3844

Abstract

Abstract: Label‐free nonlinear optical imaging (NLOI) has made tremendous inroads toward unscrambling the microcosmic complexity of cancers. However, harmonic and Raman microscopy offers throughput without redox information to reveal metabolic differentiation, and fluorescence lifetime microscopy lacks the vibrational response of molecules to visualize specific molecular constituents such as lipid. Here, a flexible, robust simultaneous multi‐nonlinear imaging and cross‐modality system that combines complementary imaging contrast mechanisms is demonstrated. This system, utilizing multiplexed ultrashort pulses, ingeniously integrates typical nonlinear processes, and high‐dimension lifetime extension in a single setup to enhance the imaging dimensions and quality. Using this system, the authors perform label‐free comprehensive evaluation of clinicopathological tissues of ovarian carcinoma due to its statistical complexity. The results show that the technology provides statistically rich, insightful information with high accuracy, sensitivity, and specificity, in contrast to standard histopathology, and can potentially be a powerful tool for fundamental cancer research and clinical applications. Abstract : In this paper, a flexible, robust simultaneous multi‐nonlinear imaging and cross‐modality system that combines complementary imaging contrast mechanisms is demonstrated. This technology provides statistically rich, insightful information with high accuracy, sensitivity, and specificity, in contrast to standard histopathology, and can potentially be a powerful tool for fundamental cancer research and clinical applications.