Harnessing Raman spectroscopy for the analysis of plant diversity.

Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.

Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy.

We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.

Species-independent analytical tools for next-generation agriculture.

Innovative approaches are urgently required to alleviate the growing pressure on agriculture to meet the rising demand for food. A key challenge for plant biology is to bridge the notable knowledge gap between our detailed understanding of model plants grown under laboratory conditions and the agriculturally important crops cultivated in fields or production facilities. This Perspective highlights the recent development of new analytical tools that are rapid and non-destructive and provide tissue-, cell- or organelle-specific information on living plants in real time, with the potential to extend across multiple species in field applications. We evaluate the utility of engineered plant nanosensors and portable Raman spectroscopy to detect biotic and abiotic stresses, monitor plant hormonal signalling as well as characterize the soil, phytobiome and crop health in a non- or minimally invasive manner. We propose leveraging these tools to bridge the aforementioned fundamental gap with new synthesis and integration of expertise from plant biology, engineering and data science. Lastly, we assess the economic potential and discuss implementation strategies that will ensure the acceptance and successful integration of these modern tools in future farming practices in traditional as well as urban agriculture.

Optical Detection of Degraded Therapeutic Proteins.

The quality of therapeutic proteins such as hormones, subunit and conjugate vaccines, and antibodies is critical to the safety and efficacy of modern medicine. Identifying malformed proteins at the point-of-care can prevent adverse immune reactions in patients; this is of special concern when there is an insecure supply chain resulting in the delivery of degraded, or even counterfeit, drug product. Identification of degraded protein, for example human growth hormone, is demonstrated by applying automated anomaly detection algorithms. Detection of the degraded protein differs from previous applications of machine-learning and classification to spectral analysis: only example spectra of genuine, high-quality drug products are used to construct the classifier. The algorithm is tested on Raman spectra acquired on protein dilutions typical of formulated drug product and at sample volumes of 25 µL, below the typical overfill (waste) volumes present in vials of injectable drug product. The algorithm is demonstrated to correctly classify anomalous recombinant human growth hormone (rhGH) with 92% sensitivity and 98% specificity even when the algorithm has only previously encountered high-quality drug product.

Single cell confocal Raman spectroscopy of human osteoarthritic chondrocytes: a preliminary study.

A great deal of effort has been focused on exploring the underlying molecular mechanism of osteoarthritis (OA) especially at the cellular level. We report a confocal Raman spectroscopic investigation on human osteoarthritic chondrocytes. The objective of this investigation is to identify molecular features and the stage of OA based on the spectral signatures corresponding to bio-molecular changes at the cellular level in chondrocytes. In this study, we isolated chondrocytes from human osteoarthritic cartilage and acquired Raman spectra from single cells. Major spectral differences between the cells obtained from different International Cartilage Repair Society (ICRS) grades of osteoarthritic cartilage were identified. During progression of OA, a decrease in protein content and an increase in cell death were observed from the vibrational spectra. Principal component analysis and subsequent cross-validation was able to associate osteoarthritic chondrocytes to ICRS Grade I, II and III with specificity 100.0%, 98.1%, and 90.7% respectively, while, sensitivity was 98.6%, 82.8%, and 97.5% respectively. The overall predictive efficiency was 92.2%. Our pilot study encourages further use of Raman spectroscopy as a noninvasive and label free technique for revealing molecular features associated with osteoarthritic chondrocytes.

A facile and real-time spectroscopic method for biofluid analysis in point-of-care diagnostics.

BACKGROUND: Accurate and real-time information is critical for decision making, especially in medical applications, where any delay in diagnosis due to collection, transport and storage of biofluids can have substantial ramifications for disease management. RESULTS: We present a facile method for point-of-care biofluid diagnostics based on the spectroscopic analysis of cotton-swab contents using a Raman probe. A PCA algorithm was developed in order to understand the clustering behavior of different off-the-shelf pharmaceutical formulations based on the recorded spectral data. Furthermore, we employed the Raman probe to detect antibiotics in a human urine sample. Our observations suggest that it is possible to provide quantitative concentration determination of Raman-active analytes by using cotton swabs as a sampling probe, which offers a wealth of possibility for real-time measurement in clinical situations. CONCLUSION: We envision that the intrinsic simplicity of the proposed approach in conjunction with its capability for accurate analyte determination in biofluids will lead to its clinical translation and application in point-of-care settings in the near future.

One-step synthesis of graphene via catalyst-free gas-phase hydrocarbon detonation.

A one-step, gas-phase, catalyst-free detonation of hydrocarbon (C2H2) method was developed to produce gram quantities of pristine graphene nanosheets (GNs). The detonation of C2H2 was carried out in the presence of O2. The molar ratios of O2/C2H2 were 0.4, 0.5, 0.6, 0.7 and 0.8. The obtained GNs were analyzed by XRD, TEM, XPS and Raman spectroscopy. The GNs are crystalline with a (002) peak centered at 26.05° (d = 0.341 nm). TEM shows that the GNs are stacked in two to three layers and sometimes single layers. An increase in the size of the GNs (35-250 nm) along with a reduction in defects (Raman I(D)/I(G) ~ 1.33-0.28) and specific surface area (187-23 m(2) g(-1)) was found with increasing O2 content. The high temperature of the detonation, ca. 4000 K, is proposed as the cause of graphene production rather than normal soot. The method allows for the control of the number of layers, shape and size of the graphene nanosheets. The process can be scaled up for industrial production.

Toward the development of Raman spectroscopy as a nonperturbative online monitoring tool for gasoline adulteration.

There is a critical need for a real-time, nonperturbative probe for monitoring the adulteration of automotive gasoline. Running on adulterated fuel leads to a substantive increase in air pollution, because of increased tailpipe emissions of harmful pollutants, as well as a reduction in engine performance. Consequently, both classification of the gasoline type and quantification of the adulteration content are of great significance for quality control. Gasoline adulteration detection is currently carried out in the laboratory with gas chromatography, which is time-consuming and costly. Here, we propose the application of Raman spectroscopic measurements for on-site rapid detection of gasoline adulteration. In this proof-of-principle report, we demonstrate the effectiveness of Raman spectra, in conjunction with multivariate analysis methods, in classifying the base oil types and simultaneously detecting the adulteration content in a wide range of commercial gasoline mixtures, both in their native states and spiked with different adulterants. In particular, we show that Raman spectra acquired with an inexpensive noncooled detector provides adequate specificity to clearly discriminate between the gasoline samples and simultaneously characterize the specific adulterant content with a limit of detection below 5%. Our promising results in this study illustrate, for the first time, the capability and the potential of Raman spectroscopy, together with multivariate analysis, as a low-cost, powerful tool for on-site rapid detection of gasoline adulteration and opens substantive avenues for applications in related fields of quality control in the oil industry.

Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements.

Although several in vivo blood glucose measurement studies have been performed by different research groups using near-infrared (NIR) absorption and Raman spectroscopic techniques, prospective prediction has proven to be a challenging problem. An important issue in this case is the demonstration of causality of glucose concentration to the spectral information, especially as the intrinsic glucose signal is smaller compared with that of the other analytes in the blood-tissue matrix. Furthermore, time-dependent physiological processes make the relation between glucose concentration and spectral data more complex. In this article, chance correlations in Raman spectroscopy-based calibration model for glucose measurements are investigated for both in vitro (physical tissue models) and in vivo (animal model and human subject) cases. Different spurious glucose concentration profiles are assigned to the Raman spectra acquired from physical tissue models, where the glucose concentration is intentionally held constant. Analogous concentration profiles, in addition to the true concentration profile, are also assigned to the datasets acquired from an animal model during a glucose clamping study as well as a human subject during an oral glucose tolerance test. We demonstrate that the spurious concentration profile-based calibration models are unable to provide prospective predictions, in contrast to those based on actual concentration profiles, especially for the physical tissue models. We also show that chance correlations incorporated by the calibration models are significantly less in Raman as compared to NIR absorption spectroscopy, even for the in vivo studies. Finally, our results suggest that the incorporation of chance correlations for in vivo cases can be largely attributed to the uncontrolled physiological sources of variations. Such uncontrolled physiological variations could either be intrinsic to the subject or stem from changes in the measurement conditions.

Effect of photobleaching on calibration model development in biological Raman spectroscopy.

A major challenge in performing quantitative biological studies using Raman spectroscopy lies in overcoming the influence of the dominant sample fluorescence background. Moreover, the prediction accuracy of a calibration model can be severely compromised by the quenching of the endogenous fluorophores due to the introduction of spurious correlations between analyte concentrations and fluorescence levels. Apparently, functional models can be obtained from such correlated samples, which cannot be used successfully for prospective prediction. This work investigates the deleterious effects of photobleaching on prediction accuracy of implicit calibration algorithms, particularly for transcutaneous glucose detection using Raman spectroscopy. Using numerical simulations and experiments on physical tissue models, we show that the prospective prediction error can be substantially larger when the calibration model is developed on a photobleaching correlated dataset compared to an uncorrelated one. Furthermore, we demonstrate that the application of shifted subtracted Raman spectroscopy (SSRS) reduces the prediction errors obtained with photobleaching correlated calibration datasets compared to those obtained with uncorrelated ones.

Waveguide confined Raman spectroscopy for microfluidic interrogation.

We report the first implementation of the fiber based microfluidic Raman spectroscopic detection scheme, which can be scaled down to micrometre dimensions, allowing it to be combined with other microfluidic functional devices. This novel Raman spectroscopic detection scheme, which we termed as Waveguide Confined Raman Spectroscopy (WCRS), is achieved through embedding fibers on-chip in a geometry that confines the Raman excitation and collection region which ensures maximum Raman signal collection. This results in a microfluidic chip with completely alignment-free Raman spectroscopic detection scheme, which does not give any background from the substrate of the chip. These features allow a WCRS based microfluidic chip to be fabricated in polydimethylsiloxane (PDMS) which is a relatively cheap material but has inherent Raman signatures in fingerprint region. The effects of length, collection angle, and fiber core size on the collection efficiency and fluorescence background of WCRS were investigated. The ability of the device to predict the concentration was studied using urea as a model analyte. A major advantage of WCRS is its scalability that allows it to be combined with many existing microfluidic functional devices. The applicability of WCRS is demonstrated through two microfluidic applications: reaction monitoring in a microreactor and detection of analyte in a microdroplet based microfluidic system. The WCRS approach may lead to wider use of Raman spectroscopy based detection in microfluidics, and the development of portable, alignment-free microfluidic devices.

Accurate spectroscopic calibration for noninvasive glucose monitoring by modeling the physiological glucose dynamics.

The physiological lag between blood and interstitial fluid (ISF) glucose is a major challenge for noninvasive glucose concentration measurements. This is a particular problem for spectroscopic techniques, which predominantly probe ISF glucose, creating inconsistencies in calibration, where blood glucose measurements are used as a reference. To overcome this problem, we present a dynamic concentration correction (DCC) scheme, based on the mass transfer of glucose between blood and ISF, to ensure consistency with the spectral measurements. The proposed formalism allows the transformation of glucose in the concentration domain, ensuring consistency with the acquired spectra in the calibration model. Taking Raman spectroscopy as a specific example, we demonstrate that the predicted glucose concentrations using the DCC-based calibration model closely match the measured glucose concentrations, while those generated with the conventional calibration methods show significantly larger deviations from the measured values. In addition, we provide an analytical formula for a previously unidentified source of limiting uncertainty arising in spectroscopic glucose monitoring from a lack of knowledge of glucose kinetics in prediction samples. A study with human volunteers undergoing glucose tolerance tests indicates that this lag uncertainty, which is comparable in magnitude to the uncertainty arising from noise and nonorthogonality in the spectral data set, can be reduced substantially by employing the DCC scheme in spectroscopic calibration.

Turbidity-corrected Raman spectroscopy for blood analyte detection.

A major challenge in quantitative biological Raman spectroscopy, particularly as applied to transcutaneous Raman spectroscopy measurements, is overcoming the deleterious effects of scattering and absorption (turbidity). The Raman spectral information is distorted by multiple scattering and absorption events in the surrounding medium, thereby diminishing the prediction capability of the calibration model. To account for these distortions, we present a novel analytical method, that we call turbidity-corrected Raman spectroscopy (TCRS), which is based on the photon migration approach and employs alternate acquisition of diffuse reflectance and Raman spectra. We demonstrate that, upon application of TCRS, the widely varying Raman spectra observed from a set of tissue phantoms having the same concentration of Raman scatterers but different turbidities has a tendency to collapse onto a single spectral profile. Furthermore, in a prospective study that employs physical tissue models with varying turbidities and randomized concentrations of Raman scatterers and interfering agents, a 20% reduction in prediction error is obtained by applying the turbidity correction procedure to the observed Raman spectra.

Comparative genomic study of spo0E family genes and elucidation of the role of Spo0E in Bacillus anthracis.

The propensity of bacterium to sporulate or retain the vegetative form depends on the amount of phosphorylated Spo0A (Spo0A(-P)), regulated by Spo0E multigene family of phosphatases (Spo0E, YisI and YnzD). Phylogenetic analysis revealed that Spo0E multigene family of phosphatases (SMFP) descends in two distinct clades of aerobic (Bacillus cluster) and anaerobic (Clostridia cluster) sporulating bacteria. High sequence conservation within species gives a notion that these members could have evolved through lineage and species-specific duplication event. Of the five genes in Bacillus cereus group, three are pathogen specific, and their synteny suggests that these paralogs could be involved in the regulation of amino acid metabolism and its transport. Overexpression of B. subtilis Spo0E, an ortholog of SMFP members in B. anthracis (BAS1251), resulted in sporulation deficient phenotype in B. anthracis. B. anthracis Spo0A(-P) binds to a consensus DNA sequence 5'-TGNCGAA-3' ('0A-like box') and loses its DNA binding ability following treatment with B. subtilis Spo0E. Thus, B. subtilis Spo0E acts on B. anthracis Spo0A(-P) and, therefore could complement the function of BAS1251. Further, since '0A-like box' are present in the promoter region of abrB gene, a known regulator of anthrax toxin gene expression, cross talk among SMFP members and Spo0A(-P)-AbrB could regulate the expression of anthrax toxin genes.

Real-time detection of hyperosmotic stress response in optically trapped single yeast cells using Raman microspectroscopy.

Living cells survive environmentally stressful conditions by initiating a stress response. We monitored changes in the Raman spectra of optically trapped Saccharomyces cerevisiae yeast cell under normal, heat-treated, and hyperosmotic stress conditions. It is shown that when glucose was used to exert hyperosmotic stress, two chemical substances-glycerol and ethanol-can be monitored in real time in a single cell.