Differential diagnosis of lung carcinoma with three-dimensional quantitative molecular vibrational imaging

Optical clearing and testing of lung tissue using inhalation aerosols: prospects for monitoring the action of viral infections

Optical clearing of the lung tissue aims to make it more transparent to light by minimizing light scattering, thus allowing reconstruction of the three-dimensional structure of the tissue with a much better resolution. This is of great importance for monitoring of viral infection impact on the alveolar structure of the tissue and oxygen transport. Optical clearing agents (OCAs) can provide not only lesser light scattering of tissue components but also may influence the molecular transport function of the alveolar membrane. Air-filled lungs present significant challenges for optical imaging including optical coherence tomography (OCT), confocal and two-photon microscopy, and Raman spectroscopy, because of the large refractive-index mismatch between alveoli walls and the enclosed air-filled region. During OCT imaging, the light is strongly backscattered at each air–tissue interface, such that image reconstruction is typically limited to a single alveolus. At the same time, the filling of these cavities with an OCA, to which water (physiological solution) can also be attributed since its refractive index is much higher than that of air will lead to much better tissue optical transmittance. This review presents general principles and advances in the field of tissue optical clearing (TOC) technology, OCA delivery mechanisms in lung tissue, studies of the impact of microbial and viral infections on tissue response, and antimicrobial and antiviral photodynamic therapies using methylene blue (MB) and indocyanine green (ICG) dyes as photosensitizers.

Chemical Analysis of Morphological Changes in Lysophosphatidic Acid-Treated Ovarian Cancer Cells

Ovarian cancer (OvCa) cells are reported to undergo biochemical changes at the cell surface in response to treatment with lysophosphatidic acid (LPA). Here we use scanning electron microscopy (SEM) and multiplex coherent anti-Stokes Raman scattering (CARS) imaging via supercontinuum excitation to probe morphological changes that result from LPA treatment. SEM images show distinct shedding of microvilli-like features upon treatment with LPA. Analysis of multiplex CARS images can distinguish between molecular components, such as lipids and proteins. Our results indicate that OvCa429 and SKOV3ip epithelial ovarian cancer cells undergo similar morphological and chemical responses to treatment with LPA. The microvilli-like structures on the surface of multicellular aggregates (MCAs) are removed by treatment with LPA. The CARS analysis shows a distinct decrease in protein and increase in lipid composition on the surface of LPA-treated cells. Importantly, the CARS signals from cellular sheddings from MCAs with LPA treatment are consistent with cleavage of proteins originally present. Mass spectrometry on the cellular sheddings show that a large number of proteins, both membrane and intracellular, are present. An increased number of peptides are detected for the mesenchymal cell line relative to the epithelial cell indicating a differential response to LPA treatment with cancer progression.

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer

Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging.

Live-cell quantification and comparison of mammalian oocyte cytosolic lipid content between species, during development, and in relation to body composition using nonlinear vibrational microscopy

Cytosolic lipids participate in the growth, development, and overall health of mammalian oocytes including many roles in cellular homeostasis. Significant emphasis has been placed on the study of lipids as a dynamic organelle, which in turn requires the development of tools and techniques to quantitate and compare how lipid content relates to cellular structure, function, and normalcy. Objectives of this study were to determine if nonlinear vibrational microscopy (e.g., coherent anti-Stokes Raman scattering or CARS microscopy) could be used for live-cell imaging to quantify and compare lipid content in mammalian oocytes during development and in relation to body composition; and compare its efficacy to methods involving cellular fixation and staining protocols. Results of this study demonstrate that CARS is able to identify lipids in live mammalian oocytes, and there exists quantifiable and consistent differences in percent lipid composition across ooctyes of different species, developmental stages, and in relation to body composition. Such a method of live-cell lipid quantification has (i) experimental power in basic cell biology, (ii) practical utility for identifying developmental predictive biomarkers while advancing biology-based oocyte/embryo selection, and (iii) ability to yield rationally supporting technology for decision-making in rodents, domestic species, and human assisted reproduction and/or fertility preservation.

Dual CARS and SHG image acquisition scheme that combines single central fiber and multimode fiber bundle to collect and differentiate backward and forward generated photons

In coherent anti-Stokes Raman scattering (CARS) and second harmonic generation (SHG) imaging, backward and forward generated photons exhibit different image patterns and thus capture salient intrinsic information of tissues from different perspectives. However, they are often mixed in collection using traditional image acquisition methods and thus are hard to interpret. We developed a multimodal scheme using a single central fiber and multimode fiber bundle to simultaneously collect and differentiate images formed by these two types of photons and evaluated the scheme in an endomicroscopy prototype. The ratio of these photons collected was calculated for the characterization of tissue regions with strong or weak epi-photon generation while different image patterns of these photons at different tissue depths were revealed. This scheme provides a new approach to extract and integrate information captured by backward and forward generated photons in dual CARS/SHG imaging synergistically for biomedical applications.

Texture analysis and classification in coherent anti-Stokes Raman scattering (CARS) microscopy images for automated detection of skin cancer

Coherent anti-Stokes Raman scattering (CARS) microscopy is a powerful tool for fast label-free tissue imaging, which is promising for early medical diagnostics. To facilitate the diagnostic process, automatic image analysis algorithms, which are capable of extracting relevant features from the image content, are needed. In this contribution we perform an automated classification of healthy and tumor areas in CARS images of basal cell carcinoma (BCC) skin samples. The classification is based on extraction of texture features from image regions and subsequent classification of these regions into healthy and cancerous with a perceptron algorithm. The developed approach is capable of an accurate classification of texture types with high sensitivity and specificity, which is an important step towards an automated tumor detection procedure.

Differential diagnosis of lung carcinoma with coherent anti-Stokes Raman scattering imaging

Aimed at bridging imaging technology development with cancer diagnosis, this paper first presents the prevailing challenges of lung cancer detection and diagnosis, with an emphasis on imaging techniques. It then elaborates on the working principle of coherent anti-Stokes Raman scattering microscopy, along with a description of pathologic applications to show the effectiveness and potential of this novel technology for lung cancer diagnosis. As a nonlinear optical technique probing intrinsic molecular vibrations, coherent anti-Stokes Raman scattering microscopy offers an unparalleled, label-free strategy for clinical cancer diagnosis and allows differential diagnosis of fresh specimens based on cell morphology information and patterns, without any histology staining. This powerful feature promises a higher biopsy yield for early cancer detection by incorporating a real-time imaging feed with a biopsy needle. In addition, molecularly targeted therapies would also benefit from early access to surgical specimen with high accuracy but minimum tissue consumption, therefore potentially saving specimens for follow-up diagnostic tests. Finally, we also introduce the potential of a coherent anti-Stokes Raman scattering-based endoscopy system to support intraoperative applications at the cellular level.