This PDF file contains the front matter associated with SPIE Proceedings Volume 11973, including the Title Page, Copyright information, and Table of Contents.

Photonic data can be used to characterize the biochemical composition of samples and often in a non-destructive and label-free manner. To utilize these label-free measurements for applications like diagnostics or analytics, data driven modeling is utilized to translate photonic data into higher-level information. In this contribution, two scenarios of data driven modeling will be presented. We will present the translation of nonlinear multi-contrast images into diagnostic information like tissue types, disease types, and histopathological stainings. Additionally, we will demonstrate deep learning as tool for the extraction of the imaginary part of the third-order susceptibility of spectral CARS measurements.

Spectroscopy allows for the collection of optical data from the skin's underlying organs. Data were collected and analyzed from 26 premature infants using broadband optical spectrometry (BOS) within a 350–2500 nm wavelength range using a handheld probe in contact with the skin. Patients varied in physical characteristics (such as weight and skin tone), and scans were taken across multiple days allowing for different subject physical conditions (such as illness, hydration, feed regimen, etc). Statistical analysis and deep learning were leveraged to provide proof-of-concept that optical data is sufficient to distinguish the abdomen from the thigh, indicating that intestinal tissue can be detected, and potential for ischemic disease prediction in future study. We utilized feature-based modeling using principal component analysis (PCA) that discovered a panel of markers from spectra with high univariate AUC and low feature correlation. Our model proposed five features that distinguish abdomen from thigh with an AUC of 0.89 using unsupervised PCA and an AUC of 0.92 using supervised linear discriminant analysis (LDA). Neural network (NN) modeling of a signal wavelength, a panel of 12 selected wavelengths, and a whole spectrum yielded respective accuracies of 62%, 92%, and 95% for spectra-wise, and 65%, 100%, and 100% for subject-wise classification for the validation data set. For the test data set, accuracies of 68%, 84%, and 85% in spectra-wise and 83%, 92%, and 92% in subject wise analysis were achieved. We conclude that analysis of human tissue spectra is sufficient to permit noninvasive characterization of specific underlying organs.

Volumetric imaging enables rapid, quantitative and global measurements of cells, tissues or organisms to obtain their biomolecular information and has become a powerful tool for studying cellular metabolism, brain function and developmental biology. Optical projection tomography (OPT) plays an important role in whole-body imaging of cells, organs, embryos and organisms because it enables three-dimensional (3D) imaging with high spatial and temporal resolution of samples at the millimeter level. However, the OPT technique relies on fluorescent labels for chemical targeting, which can perturb the biological function of living system. As a label-free molecular imaging technique, widefield Raman imaging enables high-resolution analysis of large field-of-view samples. Its combination with projection tomographic strategy enables high-resolution 3D imaging of large-scale samples in a label-free manner. However, this technique was failure to determine the tissue microstructure and specific spatial distribution. Here, we proposed a concept of new label free volumetric imaging, dual-modality of optical-Raman projection tomography. In this concept, Raman projection tomography was assigned to achieve volumetric imaging of chemical composition and distribution in a 3D volume, and the OPT was used to obtain structural information of the 3D volume with micron-level spatial resolution. We further homebuilt a dual-modality imaging system for optical-Raman projection tomography and the feasibility of the system was validated by imaging polystyrene microspheres and dimethyl sulfoxide. Finally, we demonstrated the application potential by a series of bio-sample experiments.

In Raman imaging, improving the imaging speed is important in various applications such as large-scale imaging analysis and multiplex imaging, where imaging time restricts their applicability. Stimulated Raman scattering (SRS) microscopy has improved the speed of Raman imaging, while it is still restricted by several factors such as laser tuning time and signal-to-noise ratio. Here we introduce various approaches to break these barriers: ultrahigh-speed wavelength-switching of optical pulses for large-scale multicolor vibrational imaging of cells with SRS imaging flow cytometry, integrated SRS and fluorescence microscopy for super-multiplex time-lapse imaging and cytometry, and quantum enhancement to reduce the shot noise below the standard quantum limit.

Since its first lab demonstration in 2008, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy in the “long-pulse” regime (50 ps - 1 ns) has become a mature and straightforward technology for label-free bioimaging, offering the high spectral resolution of conventional Raman spectroscopy with reduced acquisition time. In this paper, we review the last developments relative to this technology, in terms of instrumentation (simplified MCARS), data analysis (unsupervised chemical analysis of hyperspectral big data) and biological applications (cell/tissue imaging, time-lapse imaging). It is reminded that running MCARS microspectroscopy in such long-pulse regime allows to get the temporal overlapping of the pump and all the Stokes spectral components without dispersion compensation, enabling simultaneous and effortless hyperspectral operation in both O–H, C–H and fingerprint regions. This simplification of the experimental setup is consolidated by the use of a dual-fiber-output laser source, for which the synchronization between the pump and Stokes pulses can be adjusted by equalizing the fiber lengths of both arms, without the need for a delay line. Chemometric methods as multivariate curve resolution (MCR) are most appropriate for the unsupervised analysis of MCARS hyperspectral data. MCR is an iterative matrix decomposition method, constructing an approximation of data by means of their projection into a subspace guided by different constraints. In this context, we introduce a new approach of cell/tissue imaging, based on a simple workflow and without any phase retrieval computation.

Coherent anti-Stokes Raman scattering (CARS) microscopy enables the analysis of the chemical composition and distribution within living cells, biomolecules, or living organisms in a label-free manner. Compared with the traditional spontaneous Raman imaging technology, its advantages of high imaging sensitivity and resolution, fast imaging speed and strong signal intensity make it more popular in multiple disciplines. The available CARS microscopes are most adopted advanced crystal solid-state lasers, which are expensive, bulky, and sensitive to the environmental changes. Supercontinuum fiber lasers with a wide spectral tuning range are increasingly used in biomedical applications due to their low cost, small size, and low environmental impact. Here, we homebuilt a CARS microscope based on a supercontinuum fiber laser, a specially tailored laser with a dual-channel time-synchronous outputs. The influence factors were investigated including the objective numerical aperture, laser power, and sample concentration, etc. The feasibility of CARS microscope was then verified by imaging the polystyrene microspheres (PS) and polymethyl methacrylate microspheres (PMMA). Finally, we imaged the lipid droplet distribution of EC109 cell, which revealed the application potential of the supercontinuum fiber laser-based CARS microscope in biomedical applications.

We propose the use of 14 ps chirped laser pulses in stimulated Raman scattering (SRS) microscopy to improve the spectral resolution and signal-to-background ratio (SBR) in SRS imaging. We developed a single-grating-based pulse chirper and implemented it into an intensity-modulation SRS microscope to stretch the excitation pulse width from 2 to 14 ps. We confirmed that the 14 ps pulses provide a spectral resolution of 2 cm^-1 by measuring the SRS spectra of diamond crystals. We found that the 14 ps pulses have smaller nonlinear background signals and improve SBR in SRS imaging of various samples due to the instantaneous narrow-band excitation and low peak power. Our technique can broaden the application of the 2 ps intensity modulation SRS microscopy by improving the spectral resolution and sensitivity.

Emerging studies have shown that oxidative imbalance is critical in disease progression such as cancer and Alzheimer’s [1, 2]. This variation can lead to the upregulation of certain metabolic pathways inducing diseases and disorders. Aromatic amino acids (AAA) are involved with the production of Reactive Oxygen Species (ROS), resulting in the increase of oxidative stress [3]. AAA studies typically rely on gas chromatography (GC) or mass spectroscopy (MS)-based imaging techniques to study lipids; however, these methods lack the ability to show the cell’s lipid spatial distribution or require fluorescent dyes that can interfere with the cell’s molecular activities [4, 5]. Here, we established an optical imaging approach that combines D2O (heavy water) probed Stimulated Raman scattering (DO-SRS) and Multiphoton Fluorescence (MPF) microscopy to directly visualize metabolic activities in situ in cancer cells under the regulation of excess AAA, specifically Phenylalanine and Tryptophan. The cellular spatial distribution of de novo lipogenesis, unsaturated and saturated lipids, NADH, Flavin, and new protein synthesis were quantitatively imaged and examined. We discovered an increase in de novo lipogenesis, Flavin/(Flavin + NADH), and unsaturated to saturated lipids in the cancer cells treated with excess AAAs. Decrease of protein turnover rate occurred in the same treated cells with observations of higher lipid droplet content. These observed metabolic activities are signs of mitochondrial dysfunction and oxidative stress. Our study demonstrates that DO-SRS can be used as a high-resolution imaging platform to study AAA regulated metabolic activities in cells and elucidates the linkage between lipid metabolism and cancer.