Medicine, Faculty of
DSpace
UBCV
Sheng, Mingyu
Master of Science - MSc
University of British Columbia
Modern advanced optical microscopes are important for disease diagnosis and cancer detection since they provide important biochemical information of the biological specimen with subcellular spatial resolution. In my thesis, I have worked on developing a multimodal microscopy. Imaging by coherent anti-Stokes Raman scattering (CARS) has been widely exploited for studying biological properties of lipid structures that generate strong signals attributed to their C−H stretching vibrations. Multiphoton Microscopy (MPM) imaging is used for exogenous and endogenous fluorophores, ordered non-centrosymmetric molecular assemblies. Reflectance Confocal Microscopy (RCM) can provide overall information on surface tissue structures of the biological specimen. In this thesis, a multimodal optical imaging microscopy system was developed. The system combines three imaging modes (RCM, MPM, and CARS) and is the first of its kind. Previous multimodal systems with MPM and CARS have been developed but none have integrated RCM. This developed system is able to provide 3 distinct microscopic images simultaneously at a frame rate of 1 fps with a digital resolution of 1024×1024 pixels. This multimodal microscopy system uses a single femtosecond pulsed laser, a single microscope objective lens to create three separate beams, aimed at the same region of interest to allow simultaneous display of the three co-registered microscopic contrasts (RCM, MPM and CARS). The resulting contrasts and composite image provide complimentary biochemical and morphological information. This multimodal microscopy system could become a powerful tool for the study of complex biological tissues and for disease diagnosis, especially skin cancer diagnosis.
DEVELOPMENT OF A CARS-BASED MULTIMODAL MICROSCOPY SYSTEM FOR BIOLOGICAL TISSUE IMAGING by Mingyu Sheng Ph.D, Fudan University, 2008 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2021 © Mingyu Sheng, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Development of a CARS-Based Multimodal Microscopy System for Biological Tissue Imaging submitted by Mingyu Sheng in partial fulfillment of the requirements for the degree of Master of Science in Interdisciplinary Oncology Examining Committee: Dr. Haishan Zeng, Department of Dermatology and Skin Science, UBC Supervisor Dr. Sunil Kalia, Department of Dermatology and Skin Science, UBC Supervisory Committee Member Dr. Calum MacAulay, Department of Pathology and Laboratory Medicine, UBC Additional Examiner Additional Supervisory Committee Members: Dr. Harvey Lui, Department of Dermatology and Skin Science, UBC Supervisory Committee Member iii Abstract Modern advanced optical microscopes are important for disease diagnosis and cancer detection since they provide important biochemical information of the biological specimen with subcellular spatial resolution. In my thesis, I have worked on developing a multimodal microscopy. Imaging by coherent anti-Stokes Raman scattering (CARS) has been widely exploited for studying biological properties of lipid structures that generate strong signals attributed to their C−H stretching vibrations. Multiphoton Microscopy (MPM) imaging is used for exogenous and endogenous fluorophores, ordered non-centrosymmetric molecular assemblies. Reflectance Confocal Microscopy (RCM) can provide overall information on surface tissue structures of the biological specimen. In this thesis, a multimodal optical imaging microscopy system was developed. The system combines three imaging modes (RCM, MPM, and CARS) and is the first of its kind. Previous multimodal systems with MPM and CARS have been developed but none have integrated RCM. This developed system is able to provide 3 distinct microscopic images simultaneously at a frame rate of 1 fps with a digital resolution of 1024×1024 pixels. This multimodal microscopy system uses a single femtosecond pulsed laser, a single microscope objective lens to create three separate beams, aimed at the same region of interest to allow simultaneous display of the three co-registered microscopic contrasts (RCM, MPM and CARS). The resulting contrasts and composite image provide complimentary biochemical and morphological information. This multimodal microscopy system could become a powerful tool iv for the study of complex biological tissues and for disease diagnosis, especially skin cancer diagnosis. v Lay Summary Consistent high fat diets can lead to obesity and the development of various diseases. Type II diabetes, cardiovascular diseases, and various cancer types (breast, brain, and adrenal) are just some of the diseases known to be associated with obesity. In order to detect and diagnose these fat (also known scientifically as lipid) related diseases, a CARS-based microscopy is developed to visualize lipid cells from a sample. In this project, this multimodal system is developed to increase the imaging capability of the above microscopy. The multimodal system combines 3 unique microscopy techniques (RCM, MPM and CARS) into one to offer imaging capabilities of label-free tissue samples with subcellular spatial resolution and high signal-to-noise ratio. The combined system take advantage of each of the 3 unique microscopy techniques to provide a detailed composite image. The structure and lipid chemical contrast obtained from each signal makes 2D and 3D visualizations of elastin, collagen, and lipid-rich cells in complex biological tissues possible. This unique multimodal microscope system could become a powerful tool for studying disease process and detecting disease and cancer, especially skin cancer. vi Preface This project is completed under the proposal and guidance of Dr. Haishan Zeng. I was responsible for the design and development of optical system, mechanical system, electrical system and software of the multimodal microscopy. I also led the preparation of the biological sample and acquired and analyzed its image results. In chapter 2, I was responsible for the design and development of the CARS microscope. This included the design and construction of the optical mechanical system, electrical system, and system software. I also worked on the electrical circuit and software control of Photon counting, I prepared the biological samples and acquired its 2D and 3D CARS microscopic images. Dr. Yuan Zhao helped build the CARS spectrometer and helped with the oil-drop verification part of the CARS signal. Dr. Harvey Lui and Dr. Sunil Kalia helped to analyze the image of the biological sample. In Chapter 3, I was responsible for the design and development of CARS-based multimodal microscope which was based on the existing MPM and RCM microscope system in the laboratory. I was responsible for adding the CARS microscope to the existing MPM and RCM system. All together I synchronized the signal acquisition and display of CARS, MPM and RCM signal. The work included the design and construction of the optical mechanical system, electrical system, and software. Dr Zhenguo Wu helped rebuild the MPM and RCM system. Dr. Harvey Lui and Dr. Sunil Kalia helped to analyze the image information of the sample. vii All the formalin fixed murine tissue presented in chapters 2 to 3 were performed according to the protocol approved by the University of British Columbia Animal Care Committee (certificate number: A18-0035). viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Figures .................................................................................................................................x Acknowledgements .................................................................................................................... xiii Chapter 1: Introduction ................................................................................................................1 1.1 Various Modalities of Laser Scanning Microscopy ........................................................ 2 1.1.1 Reflectance Confocal Microscopy (RCM) ................................................................. 2 1.1.2 Two-Photon Excitation Microscopy ........................................................................... 4 1.1.3 Second Harmonic Generation Microscopy (SHG) ..................................................... 6 1.1.4 Coherent Anti-Stokes Raman Scattering Microscopy (CARS) .................................. 8 1.2 Biomedical Applications of Multimodal Laser Scanning Microscopy ........................... 9 1.2.1 Lipid Metabolism Detection ..................................................................................... 10 1.2.2 Cancer Detection and Diagnosis ............................................................................... 10 1.3 Hypothesis and Structure of the Thesis ........................................................................ 11 Chapter 2: Development of CARS Microscopy for Biological Tissue Imaging .....................13 2.1 Introduction ............................................................................................................... 13 2.2 CARS Spectrum ........................................................................................................ 13 2.2.1 Experimental Setup of CARS Spectrometer ......................................................... 14 ix 2.2.2 CARS Signal Verification Using Oil Droplets ......................................................... 15 2.3 CARS Microscopy for Biological Tissue Imaging .............................................. 16 2.3.1 Experimental Setup of CARS Microscopy ............................................................... 17 2.3.2 2D Planar CARS Images of Biological Tissue ......................................................... 19 2.3.3 3D Volumetric CARS Images of Biological Tissue ................................................. 22 Chapter 3: Development of CARS-based Multimodal Microscopy System for Biological Tissue Imaging .............................................................................................................................24 3.1 Introduction ................................................................................................................... 24 3.2 CARS-based Multimodal Microscopy .......................................................................... 24 3.2.1 Experimental Setup of CARS-based Multimodal Microscopy ................................. 25 3.2.2 Comparison between Modes of Photomultiplier Tubes............................................ 28 3.3 Multimodal Microscopy Imaging of Biological Tissue ................................................ 32 3.4 Potential Applications in Skin Cancer Detection .......................................................... 35 Chapter 4: Conclusion .................................................................................................................38 4.1 Conclusion .................................................................................................................... 38 4.2 Future Directions .......................................................................................................... 40 Bibliography .................................................................................................................................41 Appendices ....................................................................................................................................44 Appendix A Mechanical and Optical Layout of the CARS Microscopy System ..................... 44 Appendix B Optical Adjustments of Supercontinuum Generation ........................................... 45 Appendix C System Control Software ...................................................................................... 47 x List of Figures Fig. 1.1 Schematic of a reflectance confocal microscope system. .................................................. 3 Fig.1.2 Diagram for the process of two-photon excitation. . .......................................................... 4 Fig. 1.3 A two-photon fluorescence microscope using a Ti:Sapphire femtosecond laser. ............. 5 Fig.1.4 Comparison of the photon interaction process of TPEF and SHG. .................................... 7 Fig.1.5 Diagram of the coherent anti-Stokes Raman scattering (CARS) process. . ....................... 8 Fig. 2.1 Block diagram of the CARS Spectrometer. ..................................................................... 14 Fig. 2.2 CARS spectral signal of mineral oil. ............................................................................... 16 Fig. 2.3 B diagram of CARS Microscopy. ................................................................................... 18 Fig.2.4 Photograph of CARS microscopy. ................................................................................... 18 Fig. 2.5 CARS images of 10 µm beads ......................................................................................... 19 Fig. 2.6 Porcine adipose tissue image with field of view (FOV=400µm). ................................... 20 Fig. 2.7 Murine ear skin image with field of view (FOV=200µm) .............................................. 21 Fig. 2.8 Murine liver imaging with field of view at the same depth but at different locations .... 21 Fig. 2.9 Video and 3D volumetric of murine liver tissue ............................................................ 22 Fig. 3.1 Block diagram of CARS-based multimodal microscopy system. ................................... 27 Fig.3.2. Images of tissue paper acquired by PMT in photon counting mode vs analog mode .... 29 Fig.3.3 Averaged images of tissue paper from PMT in analog mode (FOV=200µm). ................ 30 Fig.3.4 Averaged images of tissue paper from PMT in photon counting mode (FOV=200µm). . 31 Fig.3.5 SNR of images from PMT in analog vs in photon counting mode. ................................. 32 Fig.3.6 Multimodal microscopic images of murine ear (FOV=200µm). ...................................... 33 Fig.3.7 Multimodal microscopic images of murine liver (FOV=200µm) .................................... 34 xi List of Abbreviations APD: Avalanche photodiode BCC: Basal cell carcinoma BS: Beamsplitter CARS: Coherent anti-Stokes Raman scattering FOV: Field of view fps: Frames per second fs: Femtosecond HWP: Half wave plate IR: Infrared LP: Long pass MPM: Multiphoton microscopy MFC: Microsoft foundation class N.A.: Numerical aperture NAD: Nicotinamide adenine dinucleotide NAD(P)H: Reduced nicotinamide adenine dinucleotide (phosphate) NMSC: Non-melanoma skin cancer PBS: Polarization beamsplitter PCF: Photonic crystal fibre PMT: Photomultiplier tube RCM: Reflectance confocal microscopy RD: Reticular dermis xii ROI: Region of interest SC: Stratum corneum SCC: Squamous cell carcinoma SHG: Second-harmonic generation SNR: Signal-to-noise ratio STD: Standard deviation MPM: Multiphoton microscopy UV: Ultraviolet xiii Acknowledgements I would like to express my sincere thanks to my supervisor Dr. Haishan Zeng. He gave me an opportunity to engage in this exciting project under his guidance. He provided me with financial support and academic suggestions that made this smooth and the educational project possible. His positive attitude, thorough views, profound knowledge, patience, and enthusiasm guided me to complete the thesis project. I would also like to thank other members of my supervisory committee: Dr. Harvey Lui. and Dr. Sunil Kalia for their encouragement, suggestions, insightful comments of clinical experience and analysis of microscopy images. Their support and guidance provided a lot of help in the experiment. I would also like to express my heartfelt thanks to Dr. Jianhua Zhao for all the helpful discussion, and knowledge sharing sessions. I would also like to thank Dr. Caigan Du for providing me with the murine tissue samples. I would also like to thank Dr Yuan Zhao and Dr Zhenguo Wu for their help in the development of the experimental system. I would like to thank my fellow student Dr. Giselle (Yunxian) Tian, Mr. Cayden (Shujian) Li and Ms. Liwei Jiang for their cooperation and help. xiv I would like to thank my parents, husband, and son for their continuous encouragement and support. 1 Chapter 1: Introduction Optical microscopes play an important role in biomedical research and pathological diagnosis. It is a critical instrument that helps us understand the pathogenesis of cancer and improve cancer diagnosis. The traditional optical microscope is used to observe abnormalities of tissue structure and cellular morphology, but they are limited in many ways when compared to current optical microscopes. Today’s advanced microscopes offer inherent advantages such as label-free observations, 3D-sectioning capability, small focal volume, and greater penetration depth. The emergence of new laser-scanning microscopic-imaging techniques improved the basic and clinical research of pathobiology. It improves disease diagnosis, early cancer detection, and treatment strategy planning. In the past few decades, the progress in the field of optical microscopy has been revolutionary. Various confocal and nonlinear laser scanning microscopy methods have emerged. They use a variety of complementary contrast mechanisms to form 3-dimensional (3D) volumetric images of tissues. The mechanisms include confocal, two-photon emission fluorescence (TPEF), second harmonic generation (SHG), third harmonic generation (THG), and coherent anti-Stokes Raman scattering (CARS). Each of these mechanisms has an inherent optical slicing ability. These techniques can be applied to labeled and labeled-free samples to visualize molecular and morphology structures that were previously invisible. This chapter will introduce these advanced laser scanning microscopy techniques and their application and potential in cancer related bioresearch. 2 1.1 Various Modalities of Laser Scanning Microscopy 1.1.1 Reflectance Confocal Microscopy (RCM) In biomedical imaging, the main advantage of a reflectance confocal microscope (RCM) is that it can produce high resolution images of noninvasive tissues in-vivo. Compared to previous imaging methods, this technology requires less sample preparation and instrument reconfiguration. In fact, the technology has been used to create images of a variety of biological tissues, including skin, bone, teeth, brain, and eye tissue. RCM can simultaneously provide information on cell composition, structure, and morphological characteristics for in-vivo tissue, showing great potential in tumor identification1. At the same time, this method can also be used in combination with other non-linear laser scanning microscopy techniques to improve early detection of cancer and visualization of tumor margin2-5. In 1955, Marvin Minsky developed the first RCM for in-vivo research of brain tissue without the need for surgical biopsy2 . Ten years later, Petran, Hadravsky and his colleagues developed another RCM system, using a mercury lamp as a light source. This technology was used to study animals3 . RCM was first applied to human skin in 1995. Rajadhyaksha and his colleagues used lasers with different wavelengths (400-800 nm) as a light source to describe the microscopic features of the skin by focusing on imaging melanocytic lesions4 . In the late 1990s, (Fig. 1.1) the first commercialized RCM system, named Vivascope 1000 B5 , was developed by Lucid Incorporated in Rochester, New York, USA. 3 The RCM imaging system is dependent on the reflection (backscattering) of light with different refractive index structures. Reflection occurs at the boundary of two structures with different refractive indices, such as membranes, transparent keratinocytes, and melanosomes. RCM imaging system consists of a light source, a beam splitter, an objective lens, and a detector, shown in Fig. 1.16. The imaging process starts from the light source. The light source produces a monochromatic coherent light (a laser beam) through the beam splitter and the objective lens to focus on a biological tissue. Light is then reflected ("backscattered") from the tissue to reach the optical detector through a pinhole - only the light from the imaging focus area can enter through the pinhole, the out-of-focus light is blocked. RCM images are displayed in grayscale and reflects the refractive index variations of tissue elements. Figure 1.1 Schematic of a reflectance confocal microscope system (reproduced with permission from ref.6) 4 1.1.2 Two-Photon Excitation Microscopy Two-photon excitation Microscopy was invented by Winfried Denk and James Strickler 7. They combined the concept of two-photon excitation with the method of laser-scanning microscopy. The result completely changed 3D in-vivo imaging of cells and live tissues. TPEF is widely used today for 3D imaging of living cells, it is used on brain slices, embryos, whole organs, and even whole animals. When compared with RCM imaging technology, the TPEF is superior. It does not need pinholes and has the advantages of deep tissue penetration, reduced light damage, and better optical sectioning. Figure 1.2 Diagram for the process of two-photon excitation. (reproduced with permission from ref. 8) The two-photon excitation concept is a classical theoretical concept in quantum optics. The theoretical basis was established by Maria Goppert Mayer in 19319 . Then, in 1963, Kaiser and Garret verified the theory through experiments 10. Two-photon excitation is a fluorescence 5 process in which a fluorophore in the sample is excited by simultaneously absorbing two photons (Fig. 1.2). This process only occurs if the sum of the energies of the two incident photons is greater than the energy gap between the molecular ground state and the excited state8. Because this process depends on the simultaneous absorption of two infrared photons, the probability of photon absorption is directly related (quadratically) to the intensity of the radiation Figure 1.3 A two-photon fluorescence microscope using a Ti:Sapphire femtosecond laser (reproduced with permission from ref. 11). Two-photon excitation microscopy is generally composed of a femtosecond laser source, a X-Y beam scanning device, an objective lens, and a detector, shown in Fig. 1.3. The collimated beam from the pulse laser is directed towards the X-Y beam scanning unit. A beam expander is placed between the X-Y scanner and the objective lens to ensure the back aperture of the objective lens is filled completely. The objective lens allows the expanded beam to be focused on the sample 6 where the two-photon fluorescence is generated. The signal from the focused area of the sample is then collected by the objective lens and detected by the Photomultiplier Tube (PMT). TPEF has been widely used in many medical fields such as skin cancer, neuroscience, and brain in-vivo imaging. It has also proven to study tumor cell arrest, tumor cell-platelet interaction, tumor cell-leukocyte interaction, and metastatic colonization processes12. 1.1.3 Second Harmonic Generation Microscopy (SHG) Second harmonic generation microscopy (SHG) is another innovative label-free imaging technology, like TPEF, that can visualize biological molecules in tissues. Second harmonic generation is a two-photon coherent nonlinear optical process, which is produced by ordered polar molecules in non-centrosymmetric assemblies. Examples of polar molecules that can produce SHG signals include endogenous proteins (such as collagen, myosin, and tubulin), and collagen fibers in biological tissues (such as tendons and ligaments). The development of SHG enables new modality imaging in biology, cancer, and medicine, it also provides quantitative indicators for a wide range of disease diagnosis. In 1971, Fine and Hansen proposed that biological tissues can produce SHG signals13. In 1974, Hellwarth and Christensen applied SHG to an optical microscope to visualize the microscopic crystal structure of polycrystalline ZnSe 14. In 1986, Freund used a high-resolution SHG scanning microscope to obtain the first biological SHG images from a rat tail tendon15. Freund did so using the combination of SHG scanning microscope and a Ti:Sapphire femtosecond laser to acquire live cell images16. 7 Similar to TPEF, SHG depends on two photons simultaneously interacting with the samples. TPEF is a process where two photons are absorbed by the sample to produce a single fluorescent emission photon (with some energy loss), shown in Fig.1.4. In contrast, SHG does not absorb photons, and involves the scattering of photons with no energy loss. SHG is a process where two photons interact simultaneously with the non-centrosymmetric assemblies to produce a new photon, and its energy is exactly twice that of the interacting photon. The TPEF signal is generated from the exogenous or endogenous fluorescent components, while the SHG signal comes from ordered non-centrosymmetric molecular assemblies. Figure 1.4 Comparison of the photon interaction process of TPEF and SHG (reproduced with permission from ref.17) Because TPEF and SHG both depend on two-photon interaction with the sample, most of the hardware can be shared between the microscopes. The essential difference is the use of emission filters with different spectra to capture different SHG and TPEF signals. 8 1.1.4 Coherent Anti-Stokes Raman Scattering Microscopy (CARS) Coherent anti-Stokes Raman scattering (CARS) Microscopy utilizes pulsed lasers to initiate molecular vibration. It allows us to get specific information on chemical composition and molecular structure in a label-free and nondestructive way. In other words, the process avoids the use of bio-perturbing molecular labels, which is advantageous in the research of biomedical and cancer detection and diagnosis. Compared with the spontaneous Raman scattering system, CARS imaging is significantly faster and higher in resolution with better sensitivity. It is expected that CARS is able to be used for imaging human tissues in-vivo. Even though CARS microscope systems have been in development for the past two decades, there still are many challenges that require further research and development. Figure 1.5 Diagram of the coherent anti-Stokes Raman scattering (CARS) process. 9 The theory of CARS was first researched in 1965 by the Ford Motor Company18. The theory was not formally named “CARS” until ten years after by Bengly and co-workers 19. The first CARS microscope was developed in 1982 with a non-collinear beam geometry. Then, in 1999, Xie and colleagues developed a CARS microscope using two synchronized pulsed lasers and a collinear beam geometry with a tight-focusing condition20-21. It was Xie’s work that sparked the evolution of modern CARS microscopy. Coherent anti-Stokes Raman scattering (CARS) is a four-wave mixing process. Generally, the CARS process involves the interaction of two beams (a pump beam and a Stokes beam) with a sample. When the frequency difference between a pump beam and a Stokes beam (𝜔𝜔𝑝𝑝 − 𝜔𝜔𝑠𝑠) matches the vibration frequency of a Raman active molecule, a coherent anti-Stokes Raman scattering signal is generated at 𝜔𝜔𝑎𝑎𝑠𝑠 = 2𝜔𝜔𝑝𝑝 − 𝜔𝜔𝑠𝑠. 1.2 Biomedical Applications of Multimodal Laser Scanning Microscopy Multimodal laser scanning microscopy is the integration of multiple imaging modalities on a single microscopy system. These imaging modalities include RCM, MPM, and CARS microscopy. The combined system improves the ability to study different properties of cells and tissues. Because each microscopy modality can detect specific molecules or structures, multimodal laser scanning microscopy enables the study of complex biological tissues, biological processes, and disease (such as lipid metabolism, cardiovascular disease, and cancer detection and diagnosis). 10 1.2.1 Lipid Metabolism Detection Lipid droplets (LDs) are complex proteolipid organelles that affect lipid metabolism25-27. Abnormal accumulation of LDs can be found in people with obesity, hypertriglyceridemia, hypercholesterolemia, type II diabetes, and atherosclerosis. As such, understanding the LD transportation, delivery, and localization of the inner body is critical in the detection and diagnosis of the above diseases. Since CARS microscopes have the label-free ability to visualize LDs, it is a powerful tool to observe the dynamics of LDs. By combining both CARS and TPEF imaging technologies27-32, one is able to observe both the dynamics of lipids and proteins together in live cells and tissue. 1.2.2 Cancer Detection and Diagnosis Multimodal microscopy can be used to differentiate tumor cells from normal tissue cells in the brain and breast. Evans found that lipid content was low in brain tumor tissue, but protein content was high22 . Since lipid-rich cell membranes and lipid-lacking nuclei create good contrast in CARS imaging, Evans used CARS microscope to display tumor cells with submicron resolution in a label-free manner. Because breast tumors lack normal glandular structure and low cytoplasmic / nuclear ratio23, the morphological description of CARS imaging will be able to distinguish breast tumor tissues from normal tissues. Even though the relation between lipid metabolism and cancer development remains unclear, excessive intracellular lipids have been observed in various cancers35-38. Recently, Le et al used multimodal microscopy imaging to demonstrate that excessive free fatty acid not only induced lipid accumulation in cancer cells, it also disturbed cancer cell membrane24. Research showed 11 that lipid-rich cancer cells have more aggressive behavior. More interestingly, circulating tumor cells (CTC) also accumulated a considerable amount of LDs, which indicates that it is possible to develop a label-free in vivo multimodal microscopy for clinical CTC detection. Future studies are required to link the quantity and composition of intracellular lipids with the invasiveness of cancer cells, which will allow better understanding of lipids’ role in cancer development. 1.3 Hypothesis and Structure of the Thesis In order to achieve a single microscopy system that can be used for imaging diverse biological samples, we set forth to integrate multiple image modes on a single microscope to obtain comprehensive information for biomedical research and disease diagnosis. The hypothesis is that a CARS-based multimodal microscopy (RCM, MPM and CARS) can be developed to provide 3-dimensional ex-vivo sample imaging capabilities. The resulting microscopy would be able to provide information of biological tissue structure, cell morphology, and biochemical components in a non-invasive label-free way to diagnose and detect diseases. The thesis is described in detail in the following 4 chapters. Below are brief summaries of each chapter. Chapter 1 introduces the basic concepts of various optical microscopy, as well as the basic principles of various advanced optical microscopy systems (RCM, SHG, TPEF and CARS). This chapter also details the development history of these systems and their applications in biomedical research and disease detection and diagnosis. 12 Chapter 2 introduces the basic principles and development of a CARS microscope system. 2D and 3D imaging capabilities of this CARS microscope is demonstrated using tissue samples from porcine fat, murine skin (ear), and murine liver. Chapter 3 introduces the basic principles and development of a CARS-based multimodal microscope. Imaging capabilities are again demonstrated using a murine skin and liver. Chapter 4 concludes this thesis and outlines future directions. 13 Chapter 2: Development of CARS Microscopy for Biological Tissue Imaging 2.1 Introduction Coherent anti-Stokes Raman scattering (CARS) microscopy is a proven tool used in the field of biomedical studies that allows for label-free imaging with high sensitivity and chemical selectivity25,26. Since its development, CARS microscopes have been widely used in lipid component studies. A CARS signal is generated by the interaction of two laser beams with a sample. The signal (𝜔𝜔𝑎𝑎𝑠𝑠 = 2𝜔𝜔𝑝𝑝 − 𝜔𝜔𝑠𝑠) is created when the frequency difference between the pump beam (𝜔𝜔𝑝𝑝) and the Stokes beam (𝜔𝜔𝑠𝑠) matches the vibration frequency of a Raman active molecule. 2.2 CARS Spectrum The most challenging part in the construction of the CARS spectrum is overlapping the two excitation beams (pump and Stokes beam) in time and space. The overlapping is done by roughing measuring the length of the optical path of the two beams as equal and then setting the delay line so that the two optical path lengths are exactly matched at the sample. A CARS signal can only be generated and detected when the two optical paths length match. In this alignment and verification process, an immersion oil sample is used. Below details the optical path and experimental set up of the CARS spectrometer to detect the CARS signal. 14 2.2.1 Experimental Setup of CARS Spectrometer The CARS Spectrometer consists of four main parts: (1) a femtosecond laser source, (2) a supercontinuum generation from a photonic crystal fiber (PCF), (3) two delay lines, and (4) an optical focusing & acquisition part. These elements are described in detail in the following sections. The block diagram of the CARS Spectrometer is shown in Fig. 2.1. Figure 2.1 Block diagram of the CARS Spectrometer The light source of the CARS Spectrometer is a 90 MHz Ti: Sapphire femtosecond laser (Chameleon, Coherent Inc., Santa Clara, California) with output wavelength of 808 nm. The output beam from this femtosecond laser first travels to a Faraday isolator (FI), it is used to prevent any reflection of the beam back toward the laser. The output is then divided by a 50/50 ultrafast beam splitter into two arms to form the pump and Stokes beams. Then, each beam is treated with a variable attenuator, which consist of a ½ wave plate（Newport 10RP52-2) and a Glan-Laser Calcite Polarizer（Newport，10GL08AR), to decrease the laser output power to avoid 15 damaging the tissue sample. The Stokes beam of CARS spectrometer is generating using Newport’s supercontinuum generation kit (SCG-800). The optical path length of both the stokes and the pump beam is adjusted by its own delay line to match each other - the two optical path lengths are to be equal. Following the delay line, the pump beam spectrum is narrowed to 3nm width using a band pass filter BP2 (LL01-808-25, Semrock). The central wavelength of the Stokes beam is 1050 nm and its spectrum is narrowed to 50nm using a band pass filter BP1. The beams are then combined using a 810nm RELP long pass filter. Then the combined beam goes through a 60X objective lens. This 60x objective lens OBJ1(LUMPLFLN60XW, Olympus) is used to focus the beams onto the sample and a 20x objective lens OBJ2 (M20x, Newport Corporation) is used to collect and collimate the signal beam. The CARS signal can only be generated if the two beams are overlapped in time and space. Adjustments are made using the delay line to ensure correct alignment. An Ocean Optics spectrometer is used at the end to detect the final CARS signal filtered by a 650nm bandpass filter (FF02-655/40-25, Semrock, central wavelength 655 nm, band width 40 nm). 2.2.2 CARS Signal Verification Using Oil Droplets The above CARS spectrometer was set up and the light paths were verified using oil droplets as samples. This was done by placing oil droplet samples on microscope slides with cover slip on the top. By fine-tuning the delay lines of the pump and the Stokes beam, we were able to maximize the intensity of the detected signal. This can only be achieved when both beams are matched in their optical paths as detailed above. The final detected signal wavelength and 16 wavenumber is measured at 650nm and 2900 cm-1. Fig.2.2 shows the CARS signal generated from the oil sample and the CARS signal corresponding to the C-H stretching of about 2900 cm-1. Figure 2.2 CARS spectral signal of mineral oil 2.3 CARS Microscopy for Biological Tissue Imaging After validating the CARS spectrometer system using the oil droplet sample, we continued to build the CARS microscopy. This development includes taking out a portion of the spectrometer system and integrating a PMT, filter, and a dichroic mirror to complete a microscope. 17 2.3.1 Experimental Setup of CARS Microscopy As mentioned above, a CARS microscope setup includes a portion of the original spectrometer. The overall equipment layout includes the following: a light source (90MHZ Ti: Sapphire femtosecond laser), a Faraday isolator, a 50/50 ultrafast beam splitter, a RELP long-pass filter, and a variable attenuator, a delay line, and a band pass filter for each beam. Similar to the CARS spectrometer, the output wavelength of the pump beam is set at 808nm and the output wavelength of the Stokes beam is set at 1050nm. The overlapped beams are reflected by a pair of galvano mirrors (GS: GVS002, Thorlabs) for scanning the beams to observe XY images. To integrate the microscope portion, a 60x objective lens (LUMPLFLN60XW, Olympus) is used to focus the beams to the sample. This objective lens is of the water immersion type, it is semi-apochromat and has long working distances. It offers good image flatness and high transmission from the visibility to the near-infrared region of the spectrum. Following the exposure to the beams, the sample will emit CARS signal to a PMT (R3896, Hamamatsu) with the help of a long pass dichroic mirror (FF665-Di02-25×36, Semrock) and a 650 nm bandpass filter (FF02-655/40-25, Semrock). The light path described here can be seen in Fig.2.3. A photograph of the setup can be seen in Fig.2.4, it captures all of the components mentioned above. In it, the red line represents the pump beams, the yellow line represents the stokes beam and the magenta line represents the combined pump and stoke beams. If samples are transparent, such as mineral oil, a forward-propagating CARS signal would be generated. However, a sample that contains many local changes in refractive index can redirect forward-propagating photons in the epi direction. In turbid samples, such as skin, initially forward-propagating photons undergo multiple linear scattering events. These events redirect a large number of photons back toward 18 the objective lens. This phenomenon was found to be the primary contributor to epi-CARS signals from tissues. Therefore, epi-directed signals were observed in CARS microscopy. Figure 2.3 Block diagram of CARS Microscopy Figure 2.4 Photograph of CARS microscopy 19 After completing the setup, the imaging capability of the system was tested using 10 µm size polystyrene beads (Polybead® Microspheres 10.00µm, Polyscience). It was found that the detected CARS signal is low. In order to achieve a better image quality, image averaging technique is used. Stacking multiple images on top of each other allows the overly bright or dark noisy pixels to balance out, this reduces the total level of noise in the image and improves the image quality. Fig. 2.5 shows the images from the CARS microscope. Fig. 2.5(a) shows a single captured image, and Fig. 2.5(b) shows an averaged image composed of 10 images with brightness and contrast enhanced to improve grayscale image quality. Figure 2. 5 CARS images of 10 µm beads (FOV=200µm). (a) Image from a single shot, (b) Averaged Image from a combination of 10 shots with brightness and contrast enhanced 2.3.2 2D Planar CARS Images of Biological Tissue As introduced in Chapter 1, CARS imaging technology is used for ex-vivo with the capability of providing subcellular spatial resolution images of label-free samples. Ex-vivo CARS imaging was first demonstrated to visualize abundant lipid structures of pig fat. It is found that porcine 20 adipose tissue produces strong CARS signals (Fig.2.6). The bright polygonal cuticles of adipocytes are visible because of the presence of intracellular “mortar”, which gathers in stratum corneum. This intracellular substance is rich in lipids and therefore produces strong CARS signals. CARS microscopy has also been used to visualize the cellular structures of murine ear skin. The sebaceous glands, can be seen in Fig. 2.7(a), are filled with micron-sized sebum particles - a compound rich in triglycerides and wax esters. The subcutaneous fat layer can be seen in Fig.2.7 (b), this layer is found deeper in the murine skin at the bottom of the dermis. Figure 2.6 Porcine adipose tissue image with field of view (FOV=400µm) 21 Figure 2.7 Murine ear skin image with field of view (FOV=200µm) (a)sebaceous glands (b) subcutaneous fat layer Figure 2.8 Murine liver imaging with field of view at the same depth but at different locations (FOV=200µm) (a) (b) (b) (a) 22 The CARS microscopy has also been used to visualize murine liver tissue (Fig.2.8) As seen in the figure below, Image (a) and (b) are captured at the same depth but at different locations, and the liver tissue contains well distributed hepatic lipids that allows for good CARS signal. 2.3.3 3D Volumetric CARS Images of Biological Tissue As introduced in Chapter 1, CARS microscope system can help create 3D volumetric images. Fig.2.9(a) features a scan video of a 2mm thick liver tissue. It demonstrates that the intensity of the CARS signal first increases with the increase of scanning depth. Then, it gradually decreases with tissue depth and almost disappears at a depth of 70µm. Figure 2. 9 (a)Video of murine liver tissue (FOV=200µm) (b) Volumetric 3D image of murine liver tissue with the depth=70 µm (FOV=200µm) Fig.2.9(b) features a volumetric 3D image composed from this series of 2D images. This 3D image is created by staking a series of 2D images using ImageJ. With the use of a 3D viewer plugin, this image can be rotationally visualized in 3D. The size of the resulting 3D image in X, 23 Y, and Z is 200µm x 200µm x 70 µm. This 3D image can provide a clearer visual and better information of overall lipid droplets in the sample tissue 24 Chapter 3: Development of CARS-based Multimodal Microscopy System for Biological Tissue Imaging 3.1 Introduction In the pursuit of better imaging technologies and systems, we explored more ways to improve CARS capabilities - specifically using a multimodal system. This chapter will detail the development of CARS-based multimodal microscope for tissue imaging. Multimodal technique is used to combine a variety of optical imaging technologies under one laser source and one objective lens. This technique helps overcome shortcoming of any specific imaging technique. This technique works well because biological cells and tissues are composed of various molecules, when these biological specimens are irradiated by high-energy light sources (such as lasers), several different types of photophysical events occur. With different types of photophysical events, there are different optical imaging techniques. By strategically targeting each photophysical event with specific imaging techniques, a more detailed image can be captured to determine key characteristics of microstructure for biomedical research. The combined image from multimodal systems offers a more complete and enhanced visual of the sample subject. This chapter details the development of a CARS-based multimodal microscopy system for improved diagnosis and detection of cancers and diseases. 3.2 CARS-based Multimodal Microscopy With the above multimodal microscopy imaging advantages in mind, CARS-based multimodal microscope systems continue to be improved through research and development. A multimodal 25 CARS-based microscope uses a single femtosecond pulse laser to emit signals of different bands and uses multiple filters and detectors to obtain three different imaging modes simultaneously. Our particular multimodal microscopy system can simultaneously obtain images from reflectance confocal microscopy, multiphoton microscopy, and coherent anti-Stokes Raman scattering microscopy. Combined with photon counting methods in CARS imaging, the resulting multimodal microscopy has improved sensitivity and achieved higher signal to noise ratio. The system has successfully identified collagen, elastin, and lipid rich regions in biological tissue samples. 3.2.1 Experimental Setup of CARS-based Multimodal Microscopy The entire CARS-based multimodal microscopy setup can be seen below in Fig. 3.1. The system uses a single light source and an objective lens. The femtosecond pulse laser is tuned at the near-infrared wavelength (808nm) and focused on the target sample through a high numerical aperture (NA) objective lens to induce nonlinear and linear optical processes. In nonlinear optical processes, MPM signals can be generated by non-centrosymmetric molecules and various endogenous fluorophores in tissues, while CARS signals can be generated from molecules containing C-H bonds. In addition to these nonlinear effects, some parts of the incident light can be backscattered to provide RCM signals. Our multimodal microscopy system is able to obtain all three different signals (MPM, CARS, RCM) generated from different optical processes. Because the optical spectrum of each optical signal is different, two dichroic mirrors are used to chromatically separate them, and two sets of emission filters and detectors are used to detect them separately. 26 In our setup, we use a 90 MHz Ti: Sapphire femtosecond laser (Chameleon, Coherent Inc., Santa Clara, California) as the light source with output wavelength set to 808 nm. A Faraday isolator is placed in front of the laser output to prevent any reflected light from returning to the femtosecond oscillator cavity. The output is then divided by a 50/50 ultrafast beam splitter into two arms to form the pump and Stokes beams (the Stokes beam is obtained from a Newport supercontinuum generation kit SCG-800). Each beam is treated with a variable attenuator, which consist of a ½ wave plate（Newport 10RP52-2) and a Glan-Laser Calcite Polarizer（Newport，10GL08AR), to decrease the laser output power to avoid damaging the tissue sample. Each of these two beams is also paired with their own delay line to enable adjustments of the optical path lengths. As mentioned in previous sections, the optical path lengths of the two beams are to be equal to produce the CARS signal. Following the delay line, a bandpass filter is used to filter the optical spectrum. In the pump beam, the spectrum is narrowed to 3nm using a band pass filter BP2 (LL01-808-25, Semrock). In the Stokes beam, the central wavelength is 1050nm and the spectrum is narrowed to 50nm bandwidth with a band pass filter BP1. The two beams are then combined using an 810nm RELP long pass filter. The combined collimated beam passes through a pair of galvano mirrors (GS: GVS002, Thorlabs) for two-dimensional scanning. The scanner allows a field of view of 200×200µm and an imaging resolution of 1024×1024 pixels. Following the scanner, a beam expander is designed to expand the beam and provide uniform imaging performance across the entire rear aperture of the objective lens (LUMPLFLN60XW, Olympus). This beam expander is in a dual-telecentric configuration with 2.0× magnification, it consists of an achromatic doublet as a scan lens and an achromatic doublet as a tube lens. Behind the beam expander, the overlapped and collimated Stokes beam and pump beam are focused by an objective lens onto the sample to generate CARS signals, our system uses descanned and non-27 descanned detection schemes for multimodal image acquisition. The non-descanned detection configuration is used for MPM and CARS signals. The two signals are reflected using a Dichroic mirror (DM1: FF665-Di02-25×36, Semrock) with a cut-off wavelength of 665nm. They are then diverted again by another Dichroic mirror (DM2: FF605-Di02-25x36). The DM2 transmits CARS signal and reflects the MPM signal. The signals then continue through emission filters before being received by their respective detectors. The CARS signals pass through a 650 nm bandpass filter (FF02-655/40-25, Semrock, bandwidth=40 nm) and is detected by photomultiplier tube PMT2 (R3896, C12597-04, Hamamatsu) and a photon counting module (C9744, Hamamatsu), while MPM signals passes through a 650 short pass filter and is detected by photomultiplier tube PMT1 (R3896, Hamamatsu). Figure 3.1 Block diagram of CARS-based multimodal microscopy system 28 In contrast with CARS and MPM signals paths to detection, the RCM signal is not through the dichroic mirror DM1. The RCM signal reflected back by the sample goes through the beam expander, the scanning device, the RELP, the delay line, a pinhole (150 µm) and is detected by an Avalanche photodiode (APD). To reduce detecting reflected signals from optical elements other than the sample, the RCM signal is filtered through a quarter wave plate (wpq20me-780, thorlabs) at the scanning unit (GM). 3.2.2 Comparison between Modes of Photomultiplier Tubes In the application of multimodal microscopes, the current optical signal intensity of many nonlinear microscopes, such as CARS and MPM, is very weak. As such, the photoelectric detection method with high sensitivity is very important. In order to identify the more sensitive photomultiplier tube (PMT) signal detection system, analog and photon counter modes were compared. In our findings, both detecting modes demonstrated the ability to measure the signal light of the low intensity. When comparing signal in low intensity, PMT in photon counting mode excelled prominently over PMT in analog mode. The following figures are acquired using the two PMT modes with tissue paper as the sample. As seen below, the image from photon counting provides more detailed information of the sample. 29 Figure 3.2. Images of tissue paper acquired by PMT (a) in photon counting mode (FOV=200µm) vs (b) in analog mode (FOV=200µm) Next, we compare the SNR of the image in two modes. SNR is defined using the following formula27: • n is the number of pixels for an image, • ref(i) is the intensity value of i-th pixel of the reference image (since image averaging minimizes random noises effectively, a 50 times averaged image is used as a reference image) • img(i) is the intensity value of i-th pixel of the image To compare the two modes in detail, we compared single shot images and averaged images. The averaged images are composed of varying numbers of single shot images. As seen below in Fig. 3.3 and Fig. 3.4, averaged images are composed from 10, 20, 30, and 40 images. Fig. 3.5 shows 30 the relation of SNR of each averaged image in the two modes. We concluded that for both single-shot images and averaged images, the SNR is lower in analog mode than photon counting mode. Figure 3.3 Averaged images of tissue paper from PMT in analog mode (FOV=200µm) Averaging 10 images Averaging 20 images Averaging 30 images Averaging 40 images (a) (b) (c) (d) 31 Figure 3.4 Averaged images of tissue paper from PMT in photon counting mode (FOV=200µm) Averaging 10 images Averaging 20 images Averaging 30 images Averaging 40 images (a) (b) (c) (d) 32 Figure 3.5 SNR of images from PMT (a) in analog and (b) in photon counting mode 3.3 Multimodal Microscopy Imaging of Biological Tissue As explained and demonstrated in the above sections, multimode microscopy is able to overcome the limitations of single-mode microscopy and increase the variety of tissues that can be studied. Our multimodal microscope is capable of simultaneously imaging label-free samples using three different signals: MPM, CARS, and RCM. These three signals are able to provide distinct information individually to offer new insights and comprehensive understanding of biological processes. The following images (Fig.3.6) show the multimodal images of the skin of a murine ear. Fig.3.6(a) is the CARS images that show the shape and distribution of lipids cells in the skin. Fig.3.6(b) is the RCM images that display the morphology of the murine skin. Fig.3.6 (c) is the MPM images that show the distribution and morphology of endogenous fluorophores (NADH), collagen and fibroblasts. Fig.3.6 (d) shows the composite image where the green represents (a) (b) 33 CARS, magenta represents MPM, and blue represents RCM. This composite image can be used to clearly show the morphology and distribution of each component of murine skin. Figure 3.6 Multimodal microscopic images of a murine ear (FOV=200µm) (a) CARS image (b) RCM image (c) MPM image (d) composite image of CARS, RCM and MPM image (a) (b) (c) (d) 34 Figure 3.7 Multimodal microscopic images of a murine liver (FOV=200µm) (a) CARS image (b) RCM image (c) MPM image (d) composite image of CARS, RCM and MPM image The above images (Fig. 3.7) show the multimodal images of a murine liver. Fig. 3.7 (a) is the CARS images that show the shape and distribution of lipids cells in the liver. Fig. 3.7 (b) is the RCM images that display the morphology of the murine liver. Fig. 3.7 (c) is the MPM images that show the distribution and morphology of endogenous fluorophores (NADH) in the liver. Fig. 3.7 (d) shows the composite image where green represents CARS, magenta represents MPM, and (a) (b) (c) (d) 35 blue represents RCM. This composite image can be used to clearly show the morphology and distribution of each component of murine liver. 3.4 Potential Applications in Skin Cancer Detection Keratinocyte carcinomas accounts for approximately 50% of all new malignant tumors in humans28. The diagnosis of keratinocyte carcinomas is increasing every year - in the United States, the incidence has increased by 4.2% each year between 1992 and 200629. The reasons for the rise in skin cancer is due to a shift in entertainment behavior that results in increased exposure to UV, the overall rise in life expectancy, and more complete reporting of skin cancer has both contributed to this trend. Diagnosing skin cancer is done by microscopic examination of a stained tissue - it is today’s "gold standard" for diagnosis. This examination requires tissue resection and subsequent sectioning which is invasive and puts the patient under stress. On top of that, the procedure also requires time between examination and diagnosis. This gap in diagnosis of skin cancer provides an opportunity for improvement, an opportunity for a non-invasive label-free diagnosis method. CARS-based multimodal microscopy has been identified as a potentially useful tool in diagnosis. The basis for signal generation in CARS is molecular vibration of the sample tissue. The chemical contrast provided by CARS microscopes complement the other two modes, RCM and MPM. To diagnose keratinocyte carcinomas, researchers have studied basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) and their SHG and TPEF signals. Current findings show that BCC and SCC cells in cancer tissue are different in appearance, and both lack SHG signals and have increased TPEF signals when compared to non-tumorous tissue 30-31. 36 In CARS imaging, the BCC shows a relatively weak CARS signal compared with the surrounding tissue, while the SCC nests show a stronger CARS signal30. CARS intensity differs for BCC and SCC tumor nests when compared to non-tumorous tissue. SCC arises from higher differentiated epithelial cells than BCC with a relatively increased amount of lipids and proteins, such as keratin and filaggrin, which would cause the observed higher CARS signal. However, some cancer cells affect lipid metabolism. This alteration of lipid anabolism and catabolism affects numerous processes including cell proliferation, growth, differentiation, and motility. As such, the lipid content can become an increasingly recognized hallmark of cancer。It is reported that tumor cells do not store significant amounts of triglycerides, which could explain the low CARS intensity for BCC tumor nests. In addition to the depiction of tumor nests, through the evaluation of multi-modal images, prognostic information about lipid metabolism, bioenergetics, and tumor-stromal interactions can also be obtained. This allows for good comprehensive information that can clearly distinguish healthy skin from keratinocyte carcinomas. Current research and findings show that CARS-based multimodal microscopy can distinguish BCC from SCC and have potential to assess the biochemistry of other tumors. Current research in RCM imaging has demonstrated that it has the ability to improve diagnostic accuracy and early detection of melanoma and keratinocyte carcinoma. It has also been found to accurately depict the lateral tumor edge of these cancers, which allows for instantaneous confirmation of margin clearance when tumors are surgically removed. Research has shown that RCM imaging significantly improves the diagnostic accuracy and early detection of melanocytic and non-melanocytic skin cancer. In a meta-analysis that evaluated 21 studies with a total of 3602 lesions, pooled sensitivity and specificity were 93.6% and 82.7% for all skin cancers32. The 37 data show that RCM significantly improved the specificity of detecting malignant skin tumors and melanoma. In addition to diagnosis, RCM also plays an important role in guiding the treatment of skin cancer33. RCM imaging capabilities can accurately depict the lateral tumor edge of melanoma and non-melanoma skin cancer before surgery. This ability can reduce the number of surgical layers and be particularly useful on lesions with unclear boundaries. Our work demonstrated, for the first time, that the three imaging modalities (CARS, MPM, and RCM) can be integrated into one system to simultaneously acquire co-registered CARS, MPM, and RCM images from a sample tissue in the same region of interest. 38 Chapter 4: Conclusion 4.1 Conclusion In this research, we have developed a CARS- based multimodal microscopy system with the ability to provide simultaneous multi-modal images with subcellular resolution and high signal-to-noise ratio from a label-free tissue sample. The key technical advantage of our system is in the hardware. Our complete microscopy system uses a single femtosecond pulsed laser and a single microscope objective lens to visualize 3 complementary and co-registered images (RCM, MPM, and CARS) in the same region of interest. The imaging speed is about 1 fps with the digital resolution of 1024×1024 pixels. This microscopy can provide clear 2D and 3D images for ex-vivo biological tissue samples. This multimodal system is the first system ever built that specifically combines the imaging capabilities of RCM, MPM and CARS to simultaneously produce a co-registered image of a sample in the same region of interest. Even though many other multimodal systems have been developed in the past, this specific combination of imaging modalities is unique and significant because of its advantages in providing more diagnostic and prognostic information through the addition of RCM to CARS/MPM. Compared to an existing single-pulsed CARS based multimodal nonlinear microcopy system for bioimaging25, the major differences in our system can be found in the build and operating specifications. The single-pulsed system is attractive for its simplicity and quick implementation capabilities - it uses a single light source, a single beam and a single pulse while the multimodal system developed in this research uses a single light 39 source and two beams. The major disadvantage in the build is that they use a notch filter to shape the excitation spectrum of the CARS signal, which produces a large non-resonant background. This non-resonant background can often be stronger than the resonant one and can distort or overwhelm the resonant signal of interest. As such, the use of lock-in amplifier detection is required, which greatly slows the imaging process. The resulting pixel dwell time for each pixel is limited (the lock-in amplifier must be allowed to integrate for longer than the modulation period), typically the minimum lock-in time constant is estimated about 10 µs which represents a frame rate of 0.2 fps with resolution 1024×1024 pixels while the multimodal system developed in this research has a frame rate of 1 fps. Another major component of the thesis is the comparison of the PMT detection modes (analog vs photon counting). The result showed that both modes had good ability to measure signal light of low intensity and were able to track rapidly changing signals. However, when comparing signal to noise ratio (SNR) and detecting low intensity signals, photon counting mode excelled over analog mode. When looking at photon counting mode, it should be noted that the imaging frame rate is limited due to “dead time” or recovery time of the photon counting. This time interval delay follows the absorption of a photon, during this specific moment, the detector is unable to reliably register a second photon. Dead time limits the maximum count rate of the detector thus limiting imaging frame rate. The advantage of PMT analog mode is that it does not require the photon count module, thus it is less costly to implement. The analog mode usually facilitates fast imaging frame rate if signal strength allows for its operation. Because of this, the CARS module in our multimodal system uses PMT with photon counting mode, while the MPM module uses PMT with the analog mode. 40 The 2D and 3D imaging capabilities of our system were demonstrated using tissue samples from animals: porcine fat, murine skin, and murine liver. As images of the three different modes can be taken simultaneously, this technology could be useful in the study of biological events in biological tissues, especially in the diagnosis of brain, breast, and skin cancer. The multimodal aspect of the system enables the final composite image to retain unique structure and chemical contrast obtained from each signal. This allows for good visualization of elastin, collagen, smooth muscle cell, and lipid-rich cell shapes. 4.2 Future Directions Our current model of the multi-modal microscope is limited in its imaging speed and use for ex-vivo applications. Future improvements can be made to increase imaging speed by replacing the 1 kHz Galvo scanner with an 8 or 16 kHz resonance scanner. Further increase in imaging speed can be achieved by reducing image pixels from 1024x1024 to 512x512. Ultimately, we expect our CARS-based multimodal microscopy system to be able to provide simultaneous imaging of the metabolic state of cells and the structural contrast of the extracellular matrix in the tissue. This means that we expect it to be able to monitor cancerous tissues in-vivo. 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JAAD Case Reports. 2018;4(10):1014-1023. doi:10.1016/j.jdcr.2018.09.019 44 Appendices Appendix A Mechanical and Optical Layout of the CARS Microscopy System The following figure shows the mechanical and optical layout of CARS Microscopy System. Most of the mechanical and optical components are from Thorlabs and Newport. 45 Appendix B Optical Adjustments of Supercontinuum Generation The process of aligning the laser beam into a 1.8 µm PCF is one of the more challenging aspects of the project, this step is critical to a functional system. White light (Supercontinuum Generation) is produced by a SCG-800 Photonic Crystal Fiber kit. Below, I will outline my alignment procedure: a) Set the laser power to 50 mW. b) Install the focusing objective lens assembly. Remove the objective lens, place a target about 5 inches behind the objective lens, and mark the beam position. Install a 20x objective and aim the beam at the target using the XZ adjustment of the focusing objective. This ensures the alignment of the focusing objective. c) Block the light beam, install the SCG-800 on the xyz stage, and ensure that the lines marking the polarization direction are aligned horizontally. For 20x, the input surface of the SCG-800 should be about 5mm from the tip of the objective lens. d) Aim the beam through the optical fiber by observing the transmitted beam with an IR observer on the screen about 5 inches behind the SCG-800. A small part of the light will be reflected from the small plane of the optical fiber. If the facet is located at the precise focus of the objective lens, the reflected light will be collimated. Adjust the X on the xyz bracket and collimate it by observing the reflected light at the exit aperture of the polarizer. Adjust the YZ (horizontal and vertical axes) on the xyz platform for maximum 46 coupling efficiency. Around the structured image of the beam, as light begins to couple into the fiber core, one will begin to see larger and weaker single-mode beam images. At first it was very uniform and fragile. By adjusting XYZ on the xyz platform, the brightness of the central spot is minimized and the zero-mode brightness is maximized until the center point disappears completely. e) Gradually increase the power until red light is observed at the output on white paper. Adjust the XYZ on the xyz stage to make the beam more yellow and white. Attempting to perform the primary alignment of the SCG-800 at a power greater than 50 mW may cause fiber damage. When it is necessary to optimize fiber alignment, always reduce the power to less than 50 mW. The input should not exceed 1.0 W. f) After SCG-800, install a 20x collimating objective lens and adjust it to collimate the output beam. 47 Appendix C System Control Software C.1. System Interface C.2. System Control Code This control code of multimodal microscopy is composed of three threads. The three threads include: data initialize thread, data acquisition thread, data display thread. 48 The above image features the data display thread of the multimodal microscopy. The following figure shows the data acquisition thread and data initialization thread. 49 The following image features the entire code of the entire multimodal system.
Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Zeng, Haishan
Development of a CARS-based multimodal microscopy system for biological tissue imaging
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