On-the-fly Raman microscopy guaranteeing the accuracy of discrimination

Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back "optimal" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis.

Selective-sampling Raman imaging techniques for ex vivo assessment of surgical margins in cancer surgery

One of the main challenges in cancer surgery is to ensure the complete excision of the tumour while sparing as much healthy tissue as possible. Histopathology, the gold-standard technique used to assess the surgical margins on the excised tissue, is often impractical for intra-operative use because of the time-consuming tissue cryo-sectioning and staining, and availability of histopathologists to assess stained tissue sections. Raman micro-spectroscopy is a powerful technique that can detect microscopic residual tumours on ex vivo tissue samples with accuracy, based entirely on intrinsic chemical differences. However, raster-scanning Raman micro-spectroscopy is a slow imaging technique that typically requires long data acquisition times wich are impractical for intra-operative use. Selective-sampling Raman imaging overcomes these limitations by using information regarding the spatial properties of the tissue to reduce the number of Raman spectra. This paper reviews the latest advances in selective-sampling Raman techniques and applications, mainly based on multimodal optical imaging. We also highlight the latest results of clinical integration of a prototype device for non-melanoma skin cancer. These promising results indicate the potential impact of Raman spectroscopy for providing fast and objective assessment of surgical margins, helping surgeons ensure the complete removal of tumour cells while sparing as much healthy tissue as possible.

Induction and measurement of the early stage of a host-parasite interaction using a combined optical trapping and Raman microspectroscopy system

Understanding and quantifying the temporal acquisition of host cell molecules by intracellular pathogens is fundamentally important in biology. In this study, a recently developed holographic optical trapping (HOT)-based Raman microspectroscopy (RMS) instrument is applied to detect, characterize and monitor in real time the molecular trafficking of a specific molecular species (isotope-labeled phenylalanine (L-Phe(D8)) at the single cell level. This approach enables simultaneous measurement of the chemical composition of human cerebrovascular endothelial cells and the protozoan parasite Toxoplasma gondii in isolation at the very start of the infection process. Using a model to decouple measurement contributions from host and pathogen sampling in the excitation volume, the data indicate that manipulating parasites with HOT coupled with RMS chemical readout was an effective method for measurement of L-Phe(D8) transfer from host cells to parasites in real-time, from the moment the parasite enters the host cell.

Applications of Spatial Light Modulators in Raman Spectroscopy

Advances in consumer display screen technologies have historically been adapted by researchers across the fields of optics as they can be used as electronically controlled spatial light modulators (SLMs) for a variety of uses. The performance characteristics of such SLM devices based on liquid crystal (LC) and digital micromirror device (DMD) technologies, in particular, has developed to the point where they are compatible with increasingly sensitive instrumental applications, for example, Raman spectroscopy. Spatial light modulators provide additional flexibility, from modulation of the laser excitation (including multiple laser foci patterns), manipulation of microscopic samples (optical trapping), or selection of sampling volume (adaptive optics or spatially offset Raman spectroscopy), to modulation in the spectral domain for high-resolution spectral filtering or multiplexed/compressive fast detection. Here, we introduce the benefits of different SLM devices as a part of Raman instrumentation and provide a variety of recent example applications which have benefited from their incorporation into a Raman system.

Dynamic Sparse Sampling for Confocal Raman Microscopy

The total number of data points required for image generation in Raman microscopy was greatly reduced using sparse sampling strategies, in which the preceding set of measurements informed the next most information-rich sampling location. Using this approach, chemical images of pharmaceutical materials were obtained with >99% accuracy from 15.8% sampling, representing an ∼6-fold reduction in measurement time relative to full field of view rastering with comparable image quality. This supervised learning approach to dynamic sampling (SLADS) has the distinct advantage of being directly compatible with standard confocal Raman instrumentation. Furthermore, SLADS is not limited to Raman imaging, potentially providing time-savings in image reconstruction whenever the single-pixel measurement time is the limiting factor in image generation.

Raman Spectroscopy: An Emerging Tool in Neurodegenerative Disease Research and Diagnosis

The pathogenesis underlining many neurodegenerative diseases remains incompletely understood. The lack of effective biomarkers and disease preventative medicine demands the development of new techniques to efficiently probe the mechanisms of disease and to detect early biomarkers predictive of disease onset. Raman spectroscopy is an established technique that allows the label-free fingerprinting and imaging of molecules based on their chemical constitution and structure. While analysis of isolated biological molecules has been widespread in the chemical community, applications of Raman spectroscopy to study clinically relevant biological species, disease pathogenesis, and diagnosis have been rapidly increasing since the past decade. The growing number of biomedical applications has shown the potential of Raman spectroscopy for detection of novel biomarkers that could enable the rapid and accurate screening of disease susceptibility and onset. Here we provide an overview of Raman spectroscopy and related techniques and their application to neurodegenerative diseases. We further discuss their potential utility in research, biomarker detection, and diagnosis. Challenges to routine use of Raman spectroscopy in the context of neuroscience research are also presented.

Raman spectroscopy: an evolving technique for live cell studies

One of the most exciting developments in Raman spectroscopy in the last decade has been its application to cells and tissues for diagnostic and pharmaceutical applications, and in particular its use in the analysis of cellular dynamics. Raman spectroscopy is rapidly advancing as a cell imaging method that overcomes many of the limitations of current techniques and is earning its place as a routine tool in cell biology. In this review we focus on important developments in Raman spectroscopy that have evolved into the exciting technique of live-cell Raman microscopy and highlight some of the most recent and significant applications to cell biology.

Analysis of interaction between the apicomplexan protozoan Toxoplasma gondii and host cells using label-free Raman spectroscopy

Label-free imaging using Raman micro-spectroscopy (RMS) was used to characterize the spatio-temporal molecular changes of T. gondii tachyzoites and their host cell microenvironment. Raman spectral maps were recorded from isolated T. gondii tachyzoites and T. gondii-infected human retinal cells at 6 h, 24 h and 48 h post-infection. Principal component analysis (PCA) of the Raman spectra of paraformaldehyde-fixed infected cells indicated a significant increase in the amount of lipids and proteins in the T. gondii tachyzoites as the infection progresses within host cells. These results were confirmed by experiments carried out on live T. gondii-infected cells and were correlated with an increase in the concentration of proteins and lipids required for the replication of this intracellular pathogen. These findings demonstrate the potential of RMS to characterize time- and spatially-dependent molecular interactions between intracellular pathogens and the host cells. Such information may be useful for discovery of pharmacological targets or screening compounds with potential neuro-protective activity for eminent effects of changes in brain infection control practices.

Raman spectroscopy for medical diagnostics--From in-vitro biofluid assays to in-vivo cancer detection

Raman spectroscopy is an optical technique based on inelastic scattering of light by vibrating molecules and can provide chemical fingerprints of cells, tissues or biofluids. The high chemical specificity, minimal or lack of sample preparation and the ability to use advanced optical technologies in the visible or near-infrared spectral range (lasers, microscopes, fibre-optics) have recently led to an increase in medical diagnostic applications of Raman spectroscopy. The key hypothesis underpinning this field is that molecular changes in cells, tissues or biofluids, that are either the cause or the effect of diseases, can be detected and quantified by Raman spectroscopy. Furthermore, multivariate calibration and classification models based on Raman spectra can be developed on large "training" datasets and used subsequently on samples from new patients to obtain quantitative and objective diagnosis. Historically, spontaneous Raman spectroscopy has been known as a low signal technique requiring relatively long acquisition times. Nevertheless, new strategies have been developed recently to overcome these issues: non-linear optical effects and metallic nanoparticles can be used to enhance the Raman signals, optimised fibre-optic Raman probes can be used for real-time in-vivo single-point measurements, while multimodal integration with other optical techniques can guide the Raman measurements to increase the acquisition speed and spatial accuracy of diagnosis. These recent efforts have advanced Raman spectroscopy to the point where the diagnostic accuracy and speed are compatible with clinical use. This paper reviews the main Raman spectroscopy techniques used in medical diagnostics and provides an overview of various applications.

Near-real-time analysis of the phenotypic responses of Escherichia coli to 1-butanol exposure using Raman Spectroscopy

Raman spectroscopy was used to study the time course of phenotypic responses of Escherichia coli (DH5α) to 1-butanol exposure (1.2% [vol/vol]). Raman spectroscopy is of interest for bacterial phenotyping because it can be performed (i) in near real time, (ii) with minimal sample preparation (label-free), and (iii) with minimal spectral interference from water. Traditional off-line analytical methodologies were applied to both 1-butanol-treated and control cells to draw correlations with Raman data. Here, distinct sets of Raman bands are presented that characterize phenotypic traits of E. coli with maximized correlation to off-line measurements. In addition, the observed time course phenotypic responses of E. coli to 1.2% (vol/vol) 1-butanol exposure included the following: (i) decreased saturated fatty acids levels, (ii) retention of unsaturated fatty acids and low levels of cyclopropane fatty acids, (iii) increased membrane fluidity following the initial response of increased rigidity, and (iv) no changes in total protein content or protein-derived amino acid composition. For most phenotypic traits, correlation coefficients between Raman spectroscopy and traditional off-line analytical approaches exceeded 0.75, and major trends were captured. The results suggest that near-real-time Raman spectroscopy is suitable for approximating metabolic and physiological phenotyping of bacterial cells subjected to toxic environmental conditions.