This PDF file contains the front matter associated with SPIE Proceedings Volume 11236 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Extracellular vesicles (EVs) in body fluids are promising biomarkers for cancer and other diseases. Due to their small size in the range between 50 and 800 nm, spectroscopic characterization is challenging. First Raman studies of single EVs suffered from weak signal intensities which complicated detection of small variations between different EVs. New Raman results will be presented on EVs from the blood of prostate carcinoma patients and control patients. Three EV fractions were prepared by sequential gradient centrifugation at 5000 g, 12000 g, and 120000 g called EV5, EV12, and EV120, respectively. Additionally, an EV-depleted fraction was obtained from the EV120 supernatant after additional overnight centrifugation. Nanoparticle tracking analysis and electron microscopy were used to determine particle concentration and control quality. High quality Raman spectra were collected from dried pellets using a Raman microscope at 785 nm excitation. Main spectral contributions were assigned to proteins. Protein secondary structure changes distinguished EV fractions from non-cancer and cancer patients consistent with results reported in an earlier paper. Suspensions with aggregated silver nanoparticles increased the band intensities due to the surface enhanced Raman scattering (SERS) effect at 785 nm excitation. Non-cancer EV fractions showed typical SERS bands of proteins. SERS spectra of cancer EV fractions showed an intense signature of new bands at 713, 853, 1004, 1132, 1238 and 1392 cm^-1 . No SERS enhancement was observed in the EV-depleted fraction. We concluded that fractions EV12 and EV120 containing small EVs are most applicable for Raman and SERS measurements.

The development of rapid and objective diagnostic techniques with high accuracy is highly desirable for real-time in vivo cancer diagnosis and characterization during endoscopic examination. This work reports a deep learning-based fiberoptic Raman technique for improving in vivo cancer detection of nasopharyngeal carcinoma (NPC) in clinical settings. We have developed a robust cancer diagnostic platform based on deep neural network (DNN) model in combination with fiberoptic Raman endoscopic technique for effectively extracting latent discriminative features contained in in vivo tissue Raman spectra. We applied the platform onto the tasks of predicting new NPC patients as well as follow-up of post-irradiated patients at endoscopy. A better diagnostic performance was achieved in the testing dataset by using this diagnostic platform as compared to the classic chemometric classification methods such as partial least squares-discriminate analysis (PLSDA). This work demonstrates that DNN-based fiberoptic Raman technique is more effective and reliable for NPC classification, particularly robust for clinical prediction of new NPC patients and post-irradiated patients surveillance.

Review of key spectroscopy methods for label free tissue analysis - compared for tumour margin guidance capability. Comparison of key methods: Raman scattering, Mid IR-absorption, Diffuse NIR-reflection and fluorescence, – enables to select the most sensitive, specific and accurate methods for kidney cancer analysis in-vitro: fluorescence and combi Fluo/ Mid IR-absorption. 2 other combi-methods were tested ex-vivo for: a) Oral cancer – HW-Raman + Fluorescence; b) Colorectal Cancer – Fluorescence + Near-Infrared Spectroscopy. Chemometric treatment of spectral data obtained with combi-fiber probes enables to improve discrimination capability vs the single methods and shows the capability to use multi-spectral fiber spectroscopy in clinical oncology.

Raman spectroscopy is a powerful technique for biochemical composition analysis. The inherent weak signal makes it a great challenge for in vivo tissue analysis. We have developed a platform technology that meets the challenge. One of our systems measures Raman spectra from a macro-size (mm3) tissue volume using a proprietary technology to correct spectrograph image distortion, realized >16 S/N improvement. The macro-Raman system is capable of acquiring a spectrum from tissue in vivo in 1 s, facilitating practical clinical applications. Two application examples will be presented: non-invasive skin cancer detection and endoscopic lung cancer detection. The other system measures Raman from micro-size (um3) tissue volume. It provides confocal imaging monitoring in the whole micro-Raman acquisition period (20 s) to guarantee the measurement stayed on target. Application for non-invasive glucose measurement and for mechanism study of multiphoton phototermolysis therapy will be presented.

In oral cavity cancer only 15% of operations succeed in removing the whole tumor with the required margin of more than 5 mm of surrounding healthy tissue. This negatively affects prognosis. Clearly the hands and eyes of the surgeon do not suffice. We use a fiber optic needle probe for high-wavenumber Raman spectroscopic analysis of the freshly resected tissue to determine if the distance between the resection surface and the tumor is sufficient. A system is under development to inspect the resected tissue in under 15 minutes, while the patient is still in the OR.

Based on Visible Resonance Raman (VRR) method, we have developed a novel label-free portable VRR LRR2000 Raman analyzer with a portable fiber-optic probe and used it for the classification of human gliomas ex vivo and for the analysis of changes in tumor chemical compositions in molecular level. The purpose of this study was to examine the performance of the LRR2000 Raman analyzer as an optical biopsy tool for detecting human brain tumors compared to the commercial laboratory HR800 and WITec300 micro confocal Raman spectroscopy instruments. As of 2018, a total 1,938 VRR spectra were collected using LRR2000, HR800 and WITec300 Raman system, ex vivo. Identification of the four grades of glioma tumors and control tissues was performed based on the characteristic native molecular fingerprints. LRR2000 demonstrated consistent diagnostic results with HR800 and WITec300 Raman systems. LRR2000 showed the advantages of high speed, convenience and low cost compared to the two confocal micro Raman systems. Using artificial intelligence (AI)-based analysis of part of the data, the cross-validated accuracy for identifying glioma tumors is ~90% compared with gold standard histopathology examination.

The skin is a barrier between the environment and the body. When a product is applied to the skin, which constituents are penetrating, how much, how fast, how deep? Until now no technology was available to answer that question. Based on in vivo Raman spectroscopy, we have developed a method which enables quantitative determination of the skin penetration of molecular substances, expressed in g/cm2 of skin surface. The method is applicable to water-soluble, alcohol-soluble and lipid-soluble compounds. Quantitative in vivo analysis of dermal product penetration is important in product development, in toxicology and in dermatological research.

Oral cancer has a poor prognosis of only 50% even in the light of current technological advances. This poor prognosis may be attributed to the still unmet clinical need to diagnose oral pre-cancer and dysplasia. Raman spectroscopy, which can detect subtle biochemical changes, has been explored for the diagnosis of cancer. This study aims to address the clinical need by exploiting the high amplification factor of Surface Enhanced Raman Spectroscopy (SERS) to analyse the saliva samples of 10 healthy controls and 10 patients with oral dysplasia. Furthermore, this technique was compared to conventional Raman spectroscopy. The saliva samples were centrifuged at 14000g for 15 minutes and the supernatant was applied directly on the SERS substrate and dried. Simultaneously, the saliva samples were prepared in the same way on slides for conventional Raman analysis. A peak at 2108 cm^-1, attributed to salivary thiocyanate was present in all samples from dysplasia subjects but absent in samples from healthy non-smoking subjects. Partial least squares – discriminant analysis models for classification of oral pre-cancer were developed for both Raman spectroscopy and SERS to discriminate between healthy, mild and moderate dysplasia cohorts.

Two biologically transparent windows residing within the near-infrared (NIR) spectrum are of substantial interest for in vivo and intraoperative imaging and spectroscopic detection. NIR light penetrates biological tissues more efficiently than visible light because these tissues scatter and absorb less light at longer wavelengths. In particular, my group has developed a class of ‘broad-band’ plasmonic nanostructures that are well suited for strong SERS signals across a broad range of wavelengths allowing a direct comparison of detection sensitivity and tissue penetration between the two NIR windows. Moreover, SERS nanoparticles are generally nontoxic and are much brighter than near-infrared fluorescing probes, raising new possibilities for ultrasensitive detection of microscopic metastases and image-guided tumor resection.

We present the development and preliminary results of a novel intraoperative line-scanning Raman imaging system, developed for neurosurgery applications. The system records fingerprint Raman spectral images over a large field of view of 1.0cm2 through a handheld imaging probe placed in gentle contact with the interrogated tissue. With a spatial resolution of 250µm2 and an acquisition time on the order of 10s, brain structure margins can be identified within an adequate timeframe for clinical applications. The system was designed using detailed optical simulations and its performance was verified on tissue phantoms and in vivo on animals using our laboratory’s Raman point system as reference.

Cancer which has metastasized creates an issue in the medical field because the life expectancy of the patient doesn’t improve until the origin, type, and stage of metastatic cancer is determined. We present a system which uses Raman microspectroscopy to scan cancer cell cultures and determine the type of cancer present. A machine learning algorithm has been trained to distinguish between four cancer cell types. Known mixtures of cancer cells are mapped on a Raman hyperspectral image which displays where each cancer type is located.

We present advances in coherent Raman imaging for gastrointestinal cancer detection. We use stimulated Raman scattering (SRS) combined with second harmonic generation (SHG) to reveal cell nuclei, cytoplasm and collagen simultaneously in human tissues. Cell nuclei, cytoplasm and extra cellular matrix can be visualized in real time with image quality similar to conventional histology.

Using transcutaneous spatially offset Raman spectroscopy (SORS) and partial least squares regression (PLSR), we recently predicted the areal bone mineral density (aBMD), volumetric bone mineralization density (vBMD) and maximum torque (MT) of tibiae in living mice. Despite the spatial offset geometry, the accuracy of the predictions was still affected by the signal from the overlying soft tissue that, like bone, contains large amounts of Type I collagen. Here we report a way to use SOLD (simultaneous, overconstrained, library-based decomposition) to improve the PLSR accuracy. The SOLD processing generates one bone spectrum estimate, one soft tissue spectrum estimate, and a residual. We combine the bone and residual spectra together for submission to PLSR, discarding only the soft tissue contribution. With the implementation of this soft-tissue-subtracted SOLD processing, we demonstrate that we can predict vBMD and MT more accurately than our previous transcutaneous measurements.

Shifted excitation Raman difference spectroscopy (SERDS) is a powerful tool for the investigation of fluorescent samples such as biological materials. In case of rapidly changing emission backgrounds the efficiency of SERDS can however be limited as alternating detection of spectra excited at the two shifted laser wavelengths is usually restricted to sampling rates of less than 10 Hz. To overcome this issue, a novel optical lock-in detection approach enabling rapid SERDS operation in the kilohertz range using a custom 830-nm dual-wavelength diode laser and a specialized CCD enabling charge shifting on the CCD chip is presented. As an example of fluorescent and heterogeneous natural specimens, six mineral samples were selected and moved irregularly during spectral acquisition. Compared to conventional CCD read-out (operated at 5.4 Hz) the fast charge-shifting read-out performed at 1,000 Hz demonstrated superior reproducibility between repeat spectra. Using partial least squares-discriminant analysis an improved classification performance of the charge-shifting mode (sensitivity: 99 %, specificity: 94 %) over conventional read-out (sensitivity: 90 %, specificity: 92 %) was achieved. Translating the charge-shifting concept to sub-surface analysis using spatially offset Raman spectroscopy (SORS) enabled also the successful detection of charge-shifting SERDS-SORS spectra from a polytetrafluoroethylene layer concealed behind a 0.25 mm thick opaque heterogeneous layer. Chargeshifting SERDS-SORS results demonstrate two-fold improvement in signal-to-background-noise-ratio and match reference spectra much more closely. The charge-shifting approach shows large potential when rapidly changing background interference due to sample heterogeneity, dynamically evolving systems and ambient light variations presents a major challenges, e.g. in biological and biomedical applications.

Surface enhanced Raman spectroscopy (SERS) is a powerful molecular analytical tool that allows for highly sensitive chemical detection of low concentration analytes through the amplification of electromagnetic (EM) fields generated by the excitation of localized surface plasmons. SERS performance such as enhancement factor (EF), reproducibility and repeatability is highly related to distribution profile of Au nanoparticles on SERS substrates. The uniformity distribution of Au nanoparticles usually results in good SERS performance. We introduce a new SERS substrate that produces improved performance through surface modification of silicon wafers. For this purpose, hydrophilic silicon wafers are prepared and then their surfaces are coated with tannic acid (TA) by thermal treatment. TA is used as a surface modifier with low cost and high adhesion to synthesize uniform and dense Au nanoparticles. The direct synthesis of Au nanoparticles is carried out through the successive ionic layer absorption and reaction (SILAR) method. 2- naphthalenethiol (2-NAT) dye was utilized to confirm the SERS performance of the as-fabricated substrate. The SERS performance was optimized by controlling the thickness of the Au nanoparticle layer synthesized by repeating the SILAR cycle. We expect the proposed SERS substrate to exhibit good reproducibility and repeatability due to the high uniformity distribution of Au nanoparticles.

Dependent on various factors such as pH, temperature and shear forces, therapeutic insulins undergo a continuous process of chemical degradation during manufacturing and storage until administered by patients. Consequently, changes in secondary up to quaternary structures of the protein appear, with the consequence of a decrease in biological activity due to partial misfolding of the monomers and finally their aggregation to fibrils. Infrared spectrometry has been applied for quantifying chemical degradation processes of therapeutic insulins, based on changes in secondary structure. For the determination of insulin potency, the glucose metabolism rate of cells from the human monocytic cell line MONOMAC-6 has been monitored under standardized conditions, providing a measure of biological insulin activity. For cell culture monitoring with a focus on substrate and metabolite concentrations, microdialysis has been used in combination with infrared spectrometry of the continuously sampled dialysates with duration up to 48 h. The dialysate spectra were analyzed by a classical least-squares (CLS) method with appropriate reference spectra, including the determination of microdialysis recovery rates as obtained from perfusate losses of mannitol, which had been used as internal standard. By analysing the time dependent glucose utilization, the potency of tested insulins can be assessed without patient clamp experiments or animal testing.

Surface-enhanced infrared absorption (SEIRA) based on top-down fabricated nanostructures such as nanoantennas and metasurfaces has attracted much attention in recent years. These plasmonic resonant nanostructures can enhance the IR absorption signal of nearby molecules through its nearfield enhancement and have been shown to be able to detect adsorbed monolayers of proteins and lipids through their IR absorption spectra. Here, we demonstrate the continuous monitoring of cellular responses to stimuli using metasurface-enhanced infrared spectroscopy (MEIRS). A431 cells are seeded on a gold plasmonic metasurface fabricated on CaF₂ substrate. Continuous monitoring is made possible by integrating the metasurface with a flow chamber, and the IR absorption spectra of the attached cells are measured in reflectance mode under continuous perfusion of cell culture medium. Scanning electron microscopy (SEM) revealed that the cells preferentially adhere to gold surfaces rather than CaF₂ surfaces, suggesting that the IR signal measured through MEIRS is highly sensitive to the cells’ attachment and interaction with the gold metasurface. We have monitored the effect of methyl-beta-cyclodextrin, a cholesterol-depleting compound, on A431 cells. Principal component analysis highlighted the complex and subtle spectral changes of the cells.

Surface-enhanced Raman spectroscopy (SERS) enables the surface plasmon-based amplification and detection of Raman signals from biomarkers, which emerge at ultralow concentrations in the early phases of diseases. Thus, SERS chips could be used for early detection of diseases from their biomarkers obtained from liquid or tissue biopsies. While this surface enhancement capability of nanoscale gold or silver layers on different substrates were demonstrated in previous experiments and electromagnetic models, the position of the biomarker molecules on the SERS chips cannot be known or estimated a priori. As a result, SERS chips must be designed over millimeter-scale areas such that the signal amplification must be large (10⁶ times or higher with respect to no SERS) and must span the entire slide. Simultaneous surface-enhancement of Raman signals and distributing this enhancement factor (EF) over the sample surface requires an iterative and “learning” design procedure for the geometries of nanoscale metallic features that could maximize both EF and its area simultaneously. In this study, we develop genetic algorithms and use finite-difference time-domain (FDTD) modeling to optimize the geometry of gold nanostructures (NS) on glass microscope slides to functionalize these slides as SERS-active surfaces for SERS-based enhancement of Raman spectra. By using FDTD models, we calculated the enhancement factors in 3D on glass surface for 785 nm laser for Raman spectrum measurements and used genetic algorithms (GA) to iterate on the metal NS geometry to maximize the average and the hot spot EF over the periodic patterns on the slide. Field enhancement factors as high as 10¹⁷ and 10¹⁵ were calculated for hot-spots and for whole-slide averages, respectively. The optimized structures indicate that GA could help maximize label-free and whole-slide Raman signal enhancement factors for single-cell SERS detection.

We introduce two newly developed methods for high-throughput vibrational flow cytometry, namely Fourier-transform coherent anti-Stokes Raman scattering flow cytometry and stimulated Raman scattering imaging flow cytometry, which cover the fingerprint and high-frequency regions with high throughputs of >1,000 cells/s and >100 cells/s, respectively. With these methods, we also show large-scale single-cell analysis of diverse types of live cells (e.g., microbes, cancer cells, blood cells) based on carbohydrates, proteins, chlorophyll, carotenoids, and lipids with unique capabilities that are not possible with fluorescence-based flow cytometry. We also discuss a novel class of opportunities that can be offered by high-throughput vibrational flow cytometry.

This study investigates an application of Fourier transform infrared (FTIR) spectroscopy of blood components in attenuated total reflection (ATR) sampling mode to quantify ulcerative colitis (UC) induced molecular alterations. Using infrared absorbance data of serum samples extracted from Interleukin 10 knockout (IL10-/-) and Dextran Sodium Sulfate (DSS)-induced experimental models of colitis, we have quantified associated markers with the aid of accompanying data analysis techniques. Identified spectral markers are absorbance at wavenumber 1033 cm^-1 , which is primarily due to the glucose; 1076 cm^-1 , representing mannose as well as phosphate presence; and the ratio of absorbance at 1121 cm^-1 RNA presence to its value at 1020 cm^-1 , associated with DNA; and at 1629 cm^-1 belonging to the protein to its value at 1737 cm^-1 belonging to lipids’ presence. Protein secondary structures as observed by spectral deconvolution in the Amide-I band was also identified. The quantified discriminatory values show significant fluctuations in colitis samples compared to their control types. The differentiating signatures between spectra are obtained by observing p-values comparisons, the ratio analysis and the use of statistical measures such as sensitivity and specificity. High diagnostic accuracy is seen with 80-100% sensitivity and specificity values. Thus, quantitative analysis of infrared (IR) spectral data may be useful for disease diagnostics and therapeutic analysis.

Fracture toughness, a bone’s resistance to breaking, is typically measured via invasive mechanical tests. In this ex vivo study on mouse femurs, four Raman spectral features associated with bone properties were significantly correlated to fracture toughness using a partial least squares regression model. By including parameters measured from dual-energy absorptiometry and micro-computed tomography in the model, fracture toughness predictions on ovariectomized mice were significantly lower than a control cohort’s. This shows that meaningful estimates of fracture toughness can be estimated using input parameters obtained non-destructively.

In biopharmaceutical products for therapeutic usage, proteins represent the most important substance class. For the quality control of insulins as representatives of life saving pharmaceuticals, analytical methods are needed allowing more than a total protein quantification in vials. Chemical and physical influences such as unstable temperatures or shear rate exposure under storage lead to misfolding, nucleation and subsequent fibril forming of the insulins. The hypothesis is that these processes go parallel with a decrease in bioactivity. Infrared spectroscopy has been successfully utilized for secondary structure analysis in cases of protein folding and fibril formation. A reliable method for the quantification of the secondary structure changes has been developed by using insulin dry-film Fourier-Transform infrared spectroscopy in combination with the attenuated total reflection (ATR) technique and subsequent data analyses such as band-shift determination, spectral band deconvolution and principal component analysis. A systematic study of insulin spectra was carried out with model insulin specimens, available either as original formulations or as hormones purified by ultrafiltration, stored at 0°C, 20°C and 37 °C, respectively, for up to three months. Weekly ATR-measurements allowed the monitoring of the hormone secondary structure changes, which are supposedly negatively correlated with the insulin bioactivity.

Preterm birth (PTB), when defined as labor before 37 weeks of gestation, affects approximately 1 out of every 10 births in the United States, leading to high rates of mortality. Complete understanding of the mechanism of PTB requires non-invasive, multi-modal techniques that can provide information about the cascade of labor onset. This study compares the cervical remodeling in wild-type term and induced preterm mouse models using Raman spectroscopy. This study demonstrates the potential of Raman spectroscopy as a non-invasive, real-time in-vivo modality to understand cervix remodeling, thus guiding future studies to improve reproductive and neonatal outcomes.

Visible component of sunlight has a physiologically significant effect on human skin and long-term exposure to concentrated blue light energy (sunlight, laptop, cell phones) could produce oxidative stress leading to the premature skin aging. In this work, in vivo confocal Raman spectroscopy was used to characterize biochemical changes in human skin after been irradiated with different doses of blue light. After ethical committee approval, volunteers’ phototype I and II (Fitzpatrick classification) have been selected. The River Diagnosis confocal Raman spectrometer was used, before (T0) and after 15, 30 and 60 minutes of blue light irradiation (LED 450 nm) with doses of 100 J/cm² . It was possible to evaluate the biochemical skin damage caused in the stratum corneum and in the viable epidermis.