Raman spectroscopic analysis of fatty acids in tumor micro- and macroenvironments in breast cancer

Fatty acids play essential roles in the growth and metastasis of cancer cells. To facilitate their avid growth and proliferation, cancer cells not only alter the fatty acid synthesis and metabolism intracellularly and extracellularly, but also in the macroenvironment via direct and indirect pathways. This thesis reports that by using Raman micro-spectroscopy, an increase in the production of polyunsaturated fatty acids (PUFAs) was identified in both cancerous and normal appearing breast tissue obtained from breast cancer patients and tumor-bearing rats. By minimizing confounding effects from mixed chemicals and optimizing the signal-to-noise ratio of Raman spectra, a large-scale transition from monounsaturated fatty acids to PUFAs was observed in the tumor while only a small subset of fatty acids transitioned to PUFAs in the tumor micro- and macroenvironment. These findings demonstrate the power of this spectroscopic analysis, and may provide new insights into the macroenvironmental regulations in breast cancer

Tumor galaxy

Standard histopathology for diagnoses and prognoses of cancer has been limited by sample treatment-induced artifacts, long turnaround time, high cost and inadequate accuracy. We aim to overcome these limitations by using stain-free nonlinear optical histopathology, which can image endogenous molecules directly from untreated, unstained fresh tissue in real time. The presented image is a representative stain-free slide-free image from fresh human cancer tissue featuring tumor cells (purple) surrounded by reorganized collagen matrix (green) and adipocytes (blue). This "organic" visualization at molecular level (no tissue processing involved) is enabled by our custom-built nonlinear optical imaging system, which efficiently exciting various endogenous molecules via coherently controlled nonlinear optical processes. The long-term goal of this research project is to transform our approach and ability to detect and quantify early changes in breast cancer, and establish the clinical utility of using this new image-based information for diagnosis, prognosis, and medical decision making in the treatment of breast cancer.Ope

Learned, Uncertainty-driven Adaptive Acquisition for Photon-Efficient Multiphoton Microscopy

Multiphoton microscopy (MPM) is a powerful imaging tool that has been a critical enabler for live tissue imaging. However, since most multiphoton microscopy platforms rely on point scanning, there is an inherent trade-off between acquisition time, field of view (FOV), phototoxicity, and image quality, often resulting in noisy measurements when fast, large FOV, and/or gentle imaging is needed. Deep learning could be used to denoise multiphoton microscopy measurements, but these algorithms can be prone to hallucination, which can be disastrous for medical and scientific applications. We propose a method to simultaneously denoise and predict pixel-wise uncertainty for multiphoton imaging measurements, improving algorithm trustworthiness and providing statistical guarantees for the deep learning predictions. Furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample. We demonstrate our method on experimental noisy MPM measurements of human endometrium tissues, showing that we can maintain fine features and outperform other denoising methods while predicting uncertainty at each pixel. Finally, with our adaptive acquisition technique, we demonstrate a 120X reduction in acquisition time and total light dose while successfully recovering fine features in the sample. We are the first to demonstrate distribution-free uncertainty quantification for a denoising task with real experimental data and the first to propose adaptive acquisition based on reconstruction uncertaint

Stain-free histopathology by programmable supercontinuum pulses

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 were developed historically, and are used ubiquitously in pathology, despite being highly time and labor intensive. Here we introduce a unique optical imaging platform and methodology for label-free multimodal multiphoton microscopy that uses a novel photonic crystal fiber source to generate tailored chemical contrast based on programmable supercontinuum pulses. We demonstrate collection of optical signatures of the tumor microenvironment, including evidence of mesoscopic biological organization, tumor cell migration, and (lymph-)angiogenesis collected directly from fresh ex vivo mammary tissue. Acquisition of these optical signatures and other cellular or extracellular features, which are largely absent from histologically processed and stained tissue, combined with an adaptable platform for optical alignment-free programmable-contrast imaging, offers the potential to translate stain-free molecular histopathology into routine clinical use

Multiphoton microscopy for stain-free slide-free histopathology

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.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste

Multiphoton microscopy for stain-free slide-free histopathology

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.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste

Spectral-temporal-spatial customization via modulating multimodal nonlinear pulse propagation

Abstract Multimode fibers (MMFs) are gaining renewed interest for nonlinear effects due to their high-dimensional spatiotemporal nonlinear dynamics and scalability for high power. High-brightness MMF sources with effective control of the nonlinear processes would offer possibilities in many areas from high-power fiber lasers, to bioimaging and chemical sensing, and to intriguing physics phenomena. Here we present a simple yet effective way of controlling nonlinear effects at high peak power levels. This is achieved by leveraging not only the spatial but also the temporal degrees of freedom during multimodal nonlinear pulse propagation in step-index MMFs, using a programmable fiber shaper that introduces time-dependent disorders. We achieve high tunability in MMF output fields, resulting in a broadband high-peak-power source. Its potential as a nonlinear imaging source is further demonstrated through widely tunable two-photon and three-photon microscopy. These demonstrations provide possibilities for technology advances in nonlinear optics, bioimaging, spectroscopy, optical computing, and material processing