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Cutaneous pharmacokinetics (cPK) after topical formulation application has been a research area of particular interest for regulatory and drug development scientists to mechanistically understand topical bioavailability (BA). Semi-invasive techniques, such as tape-stripping, dermal microdialysis, or dermal open-flow microperfusion, all quantify macroscale cPK. While these techniques have provided vast cPK knowledge, the community lacks a mechanistic understanding of active pharmaceutical ingredient (API) penetration and permeation at the cellular level. One noninvasive approach to address microscale cPK is coherent Raman scattering imaging (CRI), which selectively targets intrinsic molecular vibrations without the need for extrinsic labels or chemical modification. CRI has two main methods-coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS)-that enable sensitive and selective quantification of APIs or inactive ingredients. CARS is typically utilized to derive structural skin information or visualize chemical contrast. In contrast, the SRS signal, which is linear with molecular concentration, is used to quantify APIs or inactive ingredients within skin stratifications. Although mouse tissue has commonly been utilized for cPK with CRI, topical BA and bioequivalence (BE) must ultimately be assessed in human tissue before regulatory approval. This paper presents a methodology to prepare and image ex vivo skin to be used in quantitative pharmacokinetic CRI studies in the evaluation of topical BA and BE. This methodology enables reliable and reproducible API quantification within human and mouse skin over time. The concentrations within lipid-rich and lipid-poor compartments, as well as total API concentration over time are quantified; these are utilized for estimates of micro- and macroscale BA and, potentially, BE.

Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysis─SPARTA─system through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (gt;95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.

Clinical medicine continues to seek novel rapid non-invasive tools capable of providing greater insight into disease progression and management. Raman scattering based technologies constitute a set of tools under continuing development to address outstanding challenges spanning diagnostic medicine, surgical guidance, therapeutic monitoring, and histopathology. Here we review the mechanisms and clinical applications of Raman scattering, specifically focusing on high-speed imaging methods that can provide spatial context for translational biomedical applications., The technological developments of high-speed spontaneous and coherent Raman scattering based imaging for translational applications including surgical guidance, histopathology, and pathophysiological monitoring.

Stimulated Raman scattering (SRS) is a nondestructive and rapid technique for imaging of biological and clinical specimens with label-free chemical specificity. SRS spectral imaging is typically carried out either via broadband methods, or by tuning narrowband ultrafast light sources over narrow spectral ranges thus specifically targeting vibrational frequencies. We demonstrate a multi-window sparse spectral sampling SRS (S4RS) approach where a rapidly-tunable dual-output all-fiber optical parametric oscillator is tuned into specific vibrational modes across more than 1400 cm−1 during imaging. This approach is capable of collecting SRS hyperspectral images either by scanning a full spectrum or by rapidly tuning into select target frequencies, hands-free and automatically, across the fingerprint, silent, and high wavenumber windows of the Raman spectrum. We further apply computational techniques for spectral decomposition and feature selection to identify a sparse subset of Raman frequencies capable of sample discrimination. Here we have applied this novel method to monitor spatiotemporal dynamic changes of active pharmaceutical ingredients in skin, which has particular relevance to topical drug product delivery.

In vivo Raman spectroscopy has been utilized for the non-invasive, non-destructive assessment of tissue pathophysiology for a variety of applications largely through the use of fiber optic probes to interface with samples of interest. Fiber optic probes can be designed to optimize the collection of Raman-scattered photons from application-dependent depths, and this critical consideration should be addressed when planning a study. Herein we investigate four distinct probe geometries for sensitivity to superficial and deep signals through a Monte Carlo model that incorporates Raman scattering and fluorescence. Experimental validation using biological tissues was performed to accurately recapitulate in vivo scenarios. Testing in biological tissues agreed with modeled results and revealed that microlens designs had slightly enhanced performance at shallow depths (< 1 mm), whereas all of the beampath-modified designs yielded more signal from deep within tissue. Simulation based on fluence maps generated using ray-tracing in the absence of optical scattering had drastically different results as a function of depth for each probe compared to the biological simulation. The contrast in simulation results between the non-scattering and biological tissue phantoms underscores the importance of considering the optical properties of a given application when designing a fiber optic probe. The model presented here can be easily extended for optimization of entirely novel probe designs prior to fabrication, reducing time and cost while improving data quality.

Label-free surface-enhanced Raman spectroscopy (SERS) can interrogate systems by directly fingerprinting their components’ unique physicochemical properties. In complex biological systems however, this can yield highly overlapping spectra that hinder sample identification. Here, we present an artificial-nose inspired SERS fingerprinting approach where spectral data is obtained as a function of sensor surface chemical functionality. Supported by molecular dynamics modeling, we show that mildly selective self-assembled monolayers can influence the strength and configuration in which analytes interact with plasmonic surfaces, diversifying the resulting SERS fingerprints. Since each sensor generates a modulated signature, the implicit value of increasing the dimensionality of datasets is shown using cell lysates for all possible combinations of up to 9 fingerprints. Reliable improvements in mean discriminatory accuracy towards 100% are achieved with each additional surface functionality. This arrayed label-free platform illustrates the wide-ranging potential of high-dimensionality artificial-nose based sensing systems for more reliable assessment of complex biological matrices., Label-free surface-enhanced Raman spectroscopy is an emergent method for the detection and discrimination of biological analytes. Here, the authors describe SERS sensors with arrayed mildly-selective surface chemistries to give a fingerprint based on different interactions for analysing biological samples.

The interactions of programmed death protein-1 (PD-1) on T cells with its ligand, PD-L1, strongly contributes to an immunosuppressive microenvironment. Blockades of PD-L1 have shown long-term survival in patients. However

Background and objective Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that are often treated through surgical resection. Current intraoperative margin assessment methods are limited and highlight the need for an improved approach with respect to time and specificity. Here we investigate the potential of near-infrared Raman spectroscopy for the intraoperative differentiation of STS from surrounding normal tissue. Materials and methods In vivo Raman measurements at 785 nm excitation were intraoperatively acquired from subjects undergoing STS resection using a probe based spectroscopy system. A multivariate classification algorithm was developed in order to automatically identify spectral features that can be used to differentiate STS from the surrounding normal muscle and fat. The classification algorithm was subsequently tested using leave-one-subject-out cross-validation. Results With the exclusion of well-differentiated liposarcomas, the algorithm was able to classify STS from the surrounding normal muscle and fat with a sensitivity and specificity of 89.5% and 96.4%, respectively. Conclusion These results suggest that single point near-infrared Raman spectroscopy could be utilized as a rapid and non-destructive surgical guidance tool for identifying abnormal tissue margins in need of further excision. Lasers Surg. Med. 48:774-781, 2016. © 2016 Wiley Periodicals, Inc.

A fiber optic probe-based Raman spectroscopy system using a single laser module with two excitation wavelengths, at 680 and 785 nm, has been developed for measuring the fingerprint and high wavenumber regions using a single detector. This system is simpler and less expensive than previously reported configurations of combined fingerprint and high wavenumber Raman systems, and its probe-based implementation facilitates numerous in vivo applications. The high wavenumber region of the Raman spectrum ranges from 2800–3800 cm(−1) and contains valuable information corresponding to the molecular vibrations of proteins, lipids, and water, which is complimentary to the biochemical signatures found in the fingerprint region (800–1800 cm(−1)), which probes DNA, lipids, and proteins. The efficacy of the system is demonstrated by tracking changes in water content in tissue-mimicking phantoms, where Voigtian decomposition of the high wavenumber water peak revealed a correlation between the water content and type of water-tissue interactions in the samples. This dual wavelength system was then used for in vivo assessment of cervical remodeling during mouse pregnancy, a physiologic process with known changes in tissue hydration. The system shows that Raman spectroscopy is sensitive to changes in collagen content in the fingerprint region and hydration state in the high wavenumber region, which was verified using an ex vivo comparison of wet and dry weight. Simultaneous fingerprint and high wavenumber Raman spectroscopy will allow precise in vivo quantification of tissue water content in the high wavenumber region, paired with the high biochemical specificity of the fingerprint region.

Renal mass biopsy is still controversial due to imperfect accuracy. Raman spectroscopy (RS) demonstrated promise as an in vivo real-time, nondestructive diagnostic tool in many malignancies. Short wave infrared (SWIR) RS has the potential to improve on previous RS systems for renal mass diagnosis. The aim of this study is to evaluate a SWIR RS system in differentiating normal and malignant renal samples. Measurements were acquired using a benchtop RS system with excitation wavelength at 1064 nm and an InGaAs array detector. Processed spectra were classified with a Bayesian machine learning algorithm, sparse multinomial logistic regression. Sensitivity and receiver operating characteristic curve analyses evaluated the classifier accuracy. Accuracy of the classifier was 92.5% with sensitivity and specificity of 95.8% and 88.8%, respectively. For posterior probability of malignant class assignment, the area under the ROC curve is 0.94 (95% confidence interval: 0.89-0.99, P < .001). SWIR RS accurately differentiated normal and malignant kidney tumors. RS has the potential to be used as a diagnostic tool in kidney cancer.

We present a rapidly tunable dual-output all-fiber light source for coherent Raman imaging, based on a dispersively matched mode-locked laser pumping a parametric oscillator. Output pump and Stokes pulses with a maximal power of 170 and 400 mW, respectively, and equal durations of 7 ps could be generated. The tuning mechanism required no mechanical delay line, enabling all-electronic arbitrary wavelength switching across more than 2700 cm−1 in less than 5 ms. The compact setup showed a reliable operation despite mechanical shocks of more than 25 m/s2 and is, thus, well suited for operation in a mobile cart. Imaging mouse and human skin tissue with both the portable light source and a commercial laboratory-bound reference system yielded qualitatively equal results and verified the portable light source being well suited for coherent Raman microscopy.

Raman spectroscopy (RS) has received increasing attention as a potential tool for clinical diagnostics. However, the unknown comparability of multiple tissue RS systems remains a major issue for technique standardization and future multisystem trials. In this study, we evaluated potential factors affecting data collection and interpretation, utilizing the skin as an example tissue. The effects of contact pressure and probe angle were characterized as potential user-induced variability sources. Similarly, instrumentation-induced variability sources of system stability and system-dependent response were also analyzed on skin and a nonvolatile biological tissue analog. Physiologically induced variations were studied on multiple tissue locations and patients. The effect of variability sources on spectral line shape and dispersion was analyzed with analysis-of-variance methods, and a new metric for comparing spectral dispersion was defined. In this study, in vivo measurements were made on multiple sites of skin from five healthy volunteers, with four stand-alone fiber optic probe–based tissue RS systems. System stability and controlled user-induced variables had no effects on obtained spectra. By contrast, instrumentation and anatomical location of measurement were significant sources of variability. These findings establish the comparability of tissue Raman spectra obtained by unique systems. Furthermore, we suggest steps for further procedural and instrumentation standardization prior to broad clinical applications of the technique.

Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.

Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm(-1)); lactic acid (838 cm(-1)); collagen (873 cm(-1)); and nucleic acids (1485 cm(-1)). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection.

Light can interact with tissue in dierent ways and probing this interaction can yield information about the state of tissue with respect to its physiology as well as pathology. e use of ber optics allows applications of optical techniques in tissue to be performed remotely and with minimal damage. Since tissue response is virtually instantaneous, the results are obtained in real time, and the use of data processing techniques allows for automated detection of disease. ese properties have resulted in a variety of applications where optical techniques have been used for tissue diagnosis.

Raman spectroscopy (RS) has been extensively used to characterize bone composition. However, the link between bone biomechanics and RS measures is not well established. Here, we leveraged the sensitivity of RS polarization to organization, thereby assessing whether RS can explain differences in bone toughness in genetic mouse models for which traditional RS peak ratios are not informative. In the selected mutant mice - activating transcription factor 4 (ATF4) or matrix metalloproteinase 9 (MMP9) knock-outs - toughness is reduced but differences in bone strength do not exist between knock-out and corresponding wild-type controls. To incorporate differences in the RS of bone occurring at peak shoulders, a multivariate approach was used. Full spectrum principal components analysis of two paired, orthogonal bone orientations (relative to laser polarization) improved genotype classification and correlation to bone toughness when compared to traditional peak ratios. When applied to femurs from wild-type mice at 8 and 20 weeks of age, the principal components of orthogonal bone orientations improved age classification but not the explanation of the maturation-related increase in strength. Overall, increasing polarization information by collecting spectra from two bone orientations improves the ability of multivariate RS to explain variance in bone toughness, likely due to polarization sensitivity to organizational changes in both mineral and collagen.

Raman spectroscopy is an established technique for molecularly specific characterization of tissues. However, even with near-infrared (NIR) excitation, some tissues possess background autofluorescence, which can overwhelm Raman scattering. Here, we report collection of spectra from tissues with strong autofluorescence using a 1064 nm system with a high-throughput dispersive spectrometer and deep-cooled InGaAs array. Spectra collected at 1064 nm were compared with those collected at 785 nm in specimens from human breast, liver, and kidney. The results demonstrate superior performance at 1064 nm in the liver and kidney, where NIR autofluorescence is intense. The results indicate the feasibility of new biomedical applications for Raman spectroscopy at 1064 nm in tissues with strong autofluorescence.