|Abstract:||The preparation, staining, visualization and interpretation of histological images of tissue is well accepted as the gold standard process for the diagnosis of disease. These methods have a long history of development, and are used ubiquitously in pathology, despite being highly time- and labor-intensive. Label-free nonlinear optical microscopy, which produces high-resolution images with rich functional and structural information based on intrinsic molecular contrast, has demonstrated significant potential to overcome these problems by leveraging its nonperturbative nature and intrinsic molecular profiling capability. Despite these advantages, conventional methods for label-free multiphoton imaging are burdened by limited contrast and efficiency of individual modalities as well as complex laser systems, which hinders this technology from wider application in biomedicine.
This thesis developed a fiber-based single-excitation multiphoton microscope capable of real-time, structural and functional imaging of living tissue which will be used to identify potential biomarkers for human breast cancer and facilitate automated histopathology. We introduce single-shot label-free autofluorescence-multiharmonic (SLAM) microscopy, a single-excitation source nonlinear imaging platform that uses a custom-designed excitation window at 1110 nm and shaped ultrafast pulses at 10 MHz to enable fast (2-orders-of-magnitude improvement), single-shot, and efficient acquisition of autofluorescence (FAD and NADH) and second/third harmonic generation from a wide array of cellular and extracellular components (e.g., tumor cells, immune cells, vesicles, and vessels) in living tissue. This innovation achieves better, faster, and richer visualization of living systems and is an enabling advance for stain-free slide-free in vivo histopathology.
Encouraged by its versatility and efficiency in visualizing biological tissue, SLAM microscopy is further used for label-free in situ characterization of extracellular vesicles, which has been an important but challenging research topic due to their small size and largely unknown and heterogenous cargo. The in situ metabolic profiling capacity of the proposed method, together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer, has the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.
Application to label-free histopathology and EV characterizations demonstrate the unique advantage of SLAM microscopy with its single-band, single-shot, structural-metabolic profiling capacity. However, to take full advantage of the rich, multi-dimensional information provided by these technologies, reproducible and reliable computational tools that could facilitate the diagnosis are needed. Therefore, a deep-learning-based framework was developed to recognize cancer versus normal human breast tissue from real-time label-free virtual histology images. These results demonstrate that through the combination of real-time virtual histopathology and a deep-learning framework, accurate real-time diagnosis could be achieved in point-of-procedure clinical applications.
Combining the novelties and advantages of SLAM microscopy, the distinct signatures of cancer EVs, and the development of a classification algorithm, the work presented in this thesis paves way for stain-free slide-free real-time molecular histopathology and is expected to broadly impact the biosciences and clinical medicine.