Raman Chemical Imaging Research Articles

Correlative Quantitative Raman Chemical Imaging and MCR-ALS in Mouse NASH Model Reveals Direct Relationships between Diet and Resultant Liver Pathology.

Raman imaging has the capability to provide unlabeled, spatially aware analysis of chemical components, with no a priori assumptions. Several lifestyle diseases such as nonalcoholic steatohepatitis (NASH) can appear in the liver as changes in the nature, abundance, and distribution of lipids, proteins, and other biomolecules and are detectable by Raman imaging. In order to identify which of these liver-associated changes occur as a direct result of the diet and which are secondary effects, we developed correlative imaging and analysis of diet and liver samples. Oleic acid was found to be a direct contributor to NASH liver composition, whereas protein and collagen distributions were found to be affected in a manner consistent with early fibrotic transformation, as a secondary consequence of the high-fat diet.

Raman spectroscopic imaging – practical applications in industrial pharmacy and medical diagnostics: a review.

Currently, Raman imaging as a non-destructive technique providing knowledge on structural characterization of the investigated samples is gaining more and more interest among industrial formulation and process scientists. In the pharmaceutical sector, the creation of novel products and formulations necessitates flexible and adaptable analysis, and increasingly also imaging capabilities. In this paper, the attention is directed toward the research progress, that has occurred in the application of non-invasive Raman chemical imaging techniques in industrial pharmacy and medical diagnostics in recent years. This work aims to explore the potential of Raman imaging as a knowledge discovery method for the non-destructive evaluation of pharmaceutical formulations as well as human cells to support medical diagnostics in clinical practice. A review of the latest scientific papers indicates that the applications of Raman imaging in pharmaceutical sciences and medical diagnostics are diverse and far-reaching from ensuring the quality of pharmaceutical formulations to studying complex drug delivery systems, monitoring manufacturing processes, and medical diagnostics of cancers and other diseases. As technology continues to evolve, the integration of Raman imaging into pharmaceutical and clinical practices is expected to expand, providing new insights and solutions to the challenges faced by the pharmaceutical industry and medical diagnostics.

UV–VIS imaging-based investigation of API concentration fluctuation caused by the sticking behaviour of pharmaceutical powder blends

Surface powder sticking in pharmaceutical mixing vessels poses a risk to the uniformity and quality of drug formulations. This study explores methods for evaluating the amount of pharmaceutical powder mixtures adhering to the metallic surfaces. Binary powder blends consisting of amlodipine and microcrystalline cellulose (MCC) were used to investigate the effect of the mixing order on the adherence to the vessel wall. Elevated API concentrations were measured on the wall and within the dislodged material from the surface, regardless of the mixing order of the components. UV imaging was used to determine the particle size and the distribution of the API on the metallic surface. The results were compared to chemical maps obtained by Raman chemical imaging. The combination of UV and VIS imaging enabled the rapid acquisition of chemical maps, covering a substantially large area representative of the analysed sample. UV imaging was also applied in tablet inspection to detect tablets that fail to meet the content uniformity criteria. The results present powder adherence as a possible source of poor content uniformity, highlighting the need for 100% inspection of pharmaceutical products to ensure product quality and safety.

Evaluation of the Effect of Disintegrant Distribution on the Dissolution Behavior of Pharmaceutical Tablets Using Raman Chemical Imaging.

In pharmaceutics, substandard drug manufacturing can sometimes occur. Usually, end-product release tests are conducted to detect defective products, but in many cases, they are not able to identify the root causes of quality defects. In recent years, chemical imaging techniques have been widely used to study quality defects by visualizing the distribution of components in solid dosage forms. However, in most studies, the causes are predicted from images of ingredients, and the impact of each factor is unclear. In this study, we prepared model tablets and intentionally changed only the distribution of disintegrants, and visualized this distribution using the Raman chemical imaging technique to evaluate the effect on the dissolution behavior of the tablets. We found that tablet disintegration occurs completely when the amount of disintegrant is sufficient to disintegrate the tablet and is distributed throughout the tablet, even if the distribution is not uniform. In contrast, if there was a large area where the disintegrant was not present, the tablet did not disintegrate sufficiently. This suggests that it is more important that a sufficient amount of disintegrant is present throughout the tablet rather than the degree of deviation of disintegrant distribution.

Tip-Enhanced Raman Chemical and Chemical Reaction Imaging in H2O with Sub-3-nm Spatial Resolution

Reproducible chemical and chemical reaction nanoimaging at solid-liquid interfaces remains challenging, particularly when resolutions on the order of a few nanometers are sought. In this work, we demonstrate the latter through liquid-tip-enhanced Raman (TER) measurements that target gold nanoplates functionalized with 4-mercaptobenzonitrile (MBN). In addition to chemical imaging and local optical field nanovisualization with high spatial resolution, we observe the signatures of 4-mercaptobenzoic acid, which forms as a result of plasmon-induced hydrolysis of MBN. Evidently, the solvent leads to distinct plasmon-induced/enhanced chemical reaction pathways that have not been documented. This work shows that such reactions that take place at solid-liquid interfaces can be tracked with a record sub-3-nm spatial resolution via TER spectral nanoimaging in liquids.

Contribution of Raman chemical imaging in the analysis of falsified and substandard medical products

Contribution of Raman chemical imaging in the analysis of falsified and substandard medical products

Heat production and volatile biosynthesis are linked via alternative respiration in Magnolia denudata during floral thermogenesis.

Floral thermogenesis is coupled with odor emission in known thermogenic plants. It is widely accepted that elevation in floral temperature can help release of volatile organic compounds (VOCs). However, no information is available about whether floral thermogenesis is associated with VOC biosynthesis. Here, we used RNA-Sequencing (RNA-Seq) to draw a gene expression atlas of floral thermogenesis in Magnolia denudata and captured an upregulation of Alternative Oxidase (AOX) during floral thermogenesis. Western blot analyses also suggested upregulation of AOX during floral thermogenesis. Moreover, oxygen consumption analyses revealed increased activity of the AOX respiration pathway during floral thermogenesis. Using HPLC analyses, we further found that increased AOX respiration substantially promoted production of citric acid by 1.35 folds, which provided fundamental metabolite skeletons for biosynthesis of VOCs. RNA-Seq also showed upregulation of genes regulating lignin catabolism, which was in agreement with in situ Raman chemical imaging of lignin. Taken together, our results suggest the central role of AOX by coupling heat production and VOC biosynthesis in floral thermogenesis of M. denudata.

Raman mapping-based non-destructive dissolution prediction of sustained-release tablets

In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f2 similarity value is higher than 59 with both methods). The presented methodology lays the foundations of the analysis of far more extensive datasets acquired with the emerging fast Raman imaging technology.

SEM/EDX and Raman chemical imaging of pharmaceutical tablets: A comparison of tablet surface preparation and analysis methods

A better understanding of a pharmaceutical tablet’s microstructure has the potential to unlock the black box between material attributes, process parameters and the critical quality attributes. Microstructure determination requires measuring the spatial-, particle size-distributions (absolute and relative) of the ingredients, and the void space, which is the overt goal of chemical Imaging (CI). Reliable quantitative results can be obtained by imaging multiple layers per tablet, with each layer having a minimal surface roughness. This study utilized scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDX) and Raman chemical imaging (RCI) to provide a comparative discussion of results obtained when determining the microstructure of commercial zinc sulfate tablets, using three methods of tablet surface preparation: scoring & hand-fracturing, microtoming, and grating. A description of the working principles of the measurement and surface preparation methods is followed by a comparison of microstructure (particle size distribution and homogeneity of distribution) using chemical images. A comparison of the general advantages and disadvantages of SEM/EDX and RCI and the common errors in analyzing microstructure are also discussed. The results indicate that in addition to selecting the correct tablet surface preparation method, chemical imaging method, and the subsequent microstructural analyses method, correct problem formulation is also critical.

Particle Size Measurements from Orally Inhaled and Nasal Drug Products.

Particle size measurement of aerosolized particles from orally inhaled and nasal drug products (OINDPs) can be used to assess the likely deposition distribution in the human respiratory tract (HRT). Size is normally expressed in terms of aerodynamic diameter, since this scale directly relates to the mechanics of particle transport from inhaler to deposition locations. The multistage cascade impactor (CI) is the principal apparatus used to size fractionate aerosols in terms of their aerodynamic particle size distributions (APSDs). Clinically meaningful metrics, such as fine and coarse particle mass fractions, can be determined from the cumulative mass-weighted APSD. In effective data analysis (EDA), CI data are reduced to small and large particle mass. The sum and ratio of these metrics are used to characterize impactor-sized mass, without the need for stage groupings or other APSD interpretation. Aerosol characterization by full-resolution CI is complex, and so, an abbreviated impactor measurement has recently come to prominence. Here, multiple stages of the CI are reduced to just one or two size fractionating stages so that measures of fine (and extrafine) particle mass from a two-stage system can be directly determined without the need to group the mass of active pharmaceutical ingredient (API) on adjacent stages. Time-of-flight-based methods determine APSD more rapidly but require refinements such as single-particle mass spectroscopy to relate size measurements to API content. Alternatives for size characterizing OINDP aerosols are few; laser diffractometry is by far the most important, especially for nasal sprays and solution-based orally inhaled formulations in which there is no confounding of data from suspended excipient(s). Laser-phase Doppler anemometry (L-PDA) has also been shown to be useful for nasal sprays. If aerodynamic size-related information is not a priority, optical microscopy combined with Raman chemical imaging offers prospects for separate determination of API components in combination product-generated aerosols.

Development of distribution maps of spectrally similar degradation products by Raman chemical imaging microscope coupled with a new variable selection technique and SIMCA classifier

The ability to detect degradation products of active pharmaceutical ingredients (API) is an essential performance not only for conducting proper stability studies and subsequently gain regulatory approvals; but as well for detecting degradation products during the manufacturing process (In Process Control). Thus, this study aims to present the ability of using Raman Chemical Imaging (Raman-CI) microscope, with its optimum precision, in combination with appropriate chemometrics algorithms, to detect the spectrally similar Salicylic Acid (SA) in Acetylsalicylic Acid (ASA) powder mixture, and then create a chemical distribution map that reflects the distribution of ASA’s main degradation product. The generated Hyperspectral images were processed where, a supervised chemometrics soft classifier, Soft Independent Modeling of Class Analogy (SIMCA), is applied to classify pixels and construct the subsequent distribution maps. In addition, due to the challenge of the high structural and spectral similarity between both substances, this study presents a new variable selection and dimensionality reduction technique, called Variable Strength Coefficient (VSC) to maximize the spectral differences and enhance the model precision and selectivity. A High-performance liquid chromatographic (HPLC) method was applied as a reference separation method to assess the results obtained by the proposed technique. The proposed technique was validated, where the obtained results confirmed that Raman Chemical Imaging Microscope, when coupled with SIMCA and VSC, is a powerful tool with outstanding accuracy. In addition, this approach could be suitable in applications where constructing accurate distribution maps of spectrally similar API’s is required.

Comparative study of multiwall carbon nanotube nanocomposites by Raman, SEM, and XPS measurement techniques

Substantial research efforts are under way to optimize the production of composites enhanced by the incorporation of nanomaterial fillers such as multiwall carbon nanotubes (MWCNTs). It is therefore critical to develop robust methods to detect and characterize MWCNTs in nanocomposites to measure product performance and potential risks from release of the MWCNTs. In this study, the effectiveness of X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and scanning electron microscopy (SEM) methods is assessed on a series of MWCNT-epoxy nanocomposites samples. A general trend of positive correlation between signal intensity and MWCNT mass loading was observed by both Raman spectroscopy and XPS. Raman spectroscopy was capable of detecting MWCNTs at the lowest nominal concentration tested (0.01%), while for XPS, MWCNTs were detected down to a threshold of approximately 0.5%. Analysis of the same locations of two nanocomposite samples with nominal MWCNT mass fractions of 0.3% and 1% using Raman chemical imaging and SEM revealed a similar detectability of MWCNT clusters and with the higher mass loading sample having a greater number of MWCNT-rich domains. Overall, these results show a good comparability among the different techniques and therefore provide comprehensive, nondestructive microscopy methods to characterize nanocomposites.

Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection.

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.

Quinine copolymer reporters promote efficient intracellular DNA delivery and illuminate a protein-induced unpackaging mechanism

Polymeric vehicles that efficiently package and controllably release nucleic acids enable the development of safer and more efficacious strategies in genetic and polynucleotide therapies. Developing delivery platforms that endogenously monitor the molecular interactions, which facilitate binding and release of nucleic acids in cells, would aid in the rational design of more effective vectors for clinical applications. Here, we report the facile synthesis of a copolymer containing quinine and 2-hydroxyethyl acrylate that effectively compacts plasmid DNA (pDNA) through electrostatic binding and intercalation. This polymer system poly(quinine-co-HEA) packages pDNA and shows exceptional cellular internalization, transgene expression, and low cytotoxicity compared to commercial controls for several human cell lines, including HeLa, HEK 293T, K562, and keratinocytes (N/TERTs). Using quinine as an endogenous reporter for pDNA intercalation, Raman imaging revealed that proteins inside cells facilitate the unpackaging of polymer-DNA complexes (polyplexes) and the release of their cargo. Our work showcases the ability of this quinine copolymer reporter to not only facilitate effective gene delivery but also enable diagnostic monitoring of polymer-pDNA binding interactions on the molecular scale via Raman imaging. The use of Raman chemical imaging in the field of gene delivery yields unprecedented insight into the unpackaging behavior of polyplexes in cells and provides a methodology to assess and design more efficient delivery vehicles for gene-based therapies.

Spatiotemporal Distribution of Pseudomonas aeruginosa Alkyl Quinolones under Metabolic and Competitive Stress.

Pseudomonas aeruginosa is an opportunistic human pathogen important to diseases such as cystic fibrosis. P. aeruginosa has multiple quorum-sensing (QS) systems, one of which utilizes the signaling molecule 2-heptyl-3-hydroxy-4-quinolone (Pseudomonas quinolone signal [PQS]). Here, we use hyperspectral Raman imaging to elucidate the spatiotemporal PQS distributions that determine how P. aeruginosa regulates surface colonization and its response to both metabolic stress and competition from other bacterial strains. These chemical imaging experiments illustrate the strong link between environmental challenges, such as metabolic stress caused by nutritional limitations or the presence of another bacterial species, and PQS signaling. Metabolic stress elicits a complex response in which limited nutrients induce the bacteria to produce PQS earlier, but the bacteria may also pause PQS production entirely if the nutrient concentration is too low. Separately, coculturing P. aeruginosa in the proximity of another bacterial species, or its culture supernatant, results in earlier production of PQS. However, these differences in PQS appearance are not observed for all alkyl quinolones (AQs) measured; the spatiotemporal response of 2-heptyl-4-hydroxyquinoline N-oxide (HQNO) is highly uniform for most conditions. These insights on the spatiotemporal distributions of quinolones provide additional perspective on the behavior of P. aeruginosa in response to different environmental cues.IMPORTANCE Alkyl quinolones (AQs), including Pseudomonas quinolone signal (PQS), made by the opportunistic pathogen Pseudomonas aeruginosa have been associated with both population density and stress. The regulation of AQ production is known to be complex, and the stimuli that modulate AQ responses are not fully clear. Here, we have used hyperspectral Raman chemical imaging to examine the temporal and spatial profiles of AQs exhibited by P. aeruginosa under several potentially stressful conditions. We found that metabolic stress, effected by carbon limitation, or competition stress, effected by proximity to other species, resulted in accelerated PQS production. This competition effect did not require cell-to-cell interaction, as evidenced by the fact that the addition of supernatants from either Escherichia coli or Staphylococcus aureus led to early appearance of PQS. Lastly, the fact that these modulations were observed for PQS but not for all AQs suggests a high level of complexity in AQ regulation that remains to be discerned.

Volumetric Raman chemical imaging of drug delivery systems

AbstractThe ability to image drug distribution inside a several hundred micron thick polymer matrix, encapsulated by a protective coating, would greatly contribute to the understanding of the performance and shortcomings of drug delivery devices. Here, we present an experimental framework for deep volumetric Raman imaging (dVRI), where common challenges such as low Raman cross section, fluorescence, and low transparency of samples are overcome. We apply dVRI to a selection of drug delivery forms, tablets with thin protective coatings and drug‐loaded microdevices. We demonstrate three‐dimensional visualization of the different drug/polymeric materials, constituting a drug delivery device, with imaging depth of 225 μm.

Raman imaging of membrane fouling

Membrane processes are widely used in industrial applications such water purification, food processing and pharmaceutical manufacturing. During their operation, the accumulation of foulants in membrane pores and on membrane surfaces leadto the reduction in flux, membrane lifetime and increase in operational cost, and the understanding of the fouling phenomenon is important for mitigating these problems. In this paper we report the application of Raman chemical imaging as a means of identify and map foulants on a membrane surface.The surface of a Polytetrafluoroethylene (PTFE) membrane was studied by Raman chemical imaging before and after fouling during desalination via membrane distillation. Information about location and concentration of three different salts namely CaSO4, BaSO4and CaCO3was studied. The three salts showed different distribution patterns, and their distribution was analyzed by correlation mapping and multivariate curve resolution. It was observed that CaSO4agglomerated in specific places while the BaSO4and CaCO3were more distributed. Raman imaging appears to be a powerful tool for studying membrane foulants and can be effective in identifying the distribution of different species on a membrane surface.

Quantitative visualization of subcellular lignocellulose revealing the mechanism of alkali pretreatment to promote methane production of rice straw

BackgroundAs a renewable carbon source, biomass energy not only helps in resolving the management problems of lignocellulosic wastes, but also helps to alleviate the global climate change by controlling environmental pollution raised by their generation on a large scale. However, the bottleneck problem of extensive production of biofuels lies in the filamentous crystal structure of cellulose and the embedded connection with lignin in biomass that leads to poor accessibility, weak degradation and digestion by microorganisms. Some pretreatment methods have shown significant improvement of methane yield and production rate, but the promotion mechanism has not been thoroughly studied. Revealing the temporal and spatial effects of pretreatment on lignocellulose will greatly help deepen our understanding of the optimization mechanism of pretreatment, and promote efficient utilization of lignocellulosic biomass. Here, we propose an approach for qualitative, quantitative, and location analysis of subcellular lignocellulosic changes induced by alkali treatment based on label-free Raman microspectroscopy combined with chemometrics.ResultsFirstly, the variations of rice straw induced by alkali treatment were characterized by the Raman spectra, and the Raman fingerprint characteristics for classification of rice straw were captured. Then, a label-free Raman chemical imaging strategy was executed to obtain subcellular distribution of the lignocellulose, in the strategy a serious interference of plant tissues’ fluorescence background was effectively removed. Finally, the effects of alkali pretreatment on the subcellular spatial distribution of lignocellulose in different types of cells were discovered.ConclusionsThe results demonstrated the mechanism of alkali treatment that promotes methane production in rice straw through anaerobic digestion by means of a systemic study of the evidence from the macroscopic measurement and Raman microscopic quantitative and localization two-angle views. Raman chemical imaging combined with chemometrics could nondestructively realize qualitative, quantitative, and location analysis of the lignocellulose of rice straw at a subcellular level in a label-free way, which was beneficial to optimize pretreatment for the improvement of biomass conversion efficiency and promote extensive utilization of biofuel.

Infrared and Raman chemical imaging and spectroscopy at the nanoscale.

The advent of nanotechnology, and the need to understand the chemical composition at the nanoscale, has stimulated the convergence of IR and Raman spectroscopy with scanning probe methods, resulting in new nanospectroscopy paradigms. Here we review two such methods, namely photothermal induced resonance (PTIR), also known as AFM-IR and tip-enhanced Raman spectroscopy (TERS). AFM-IR and TERS fundamentals will be reviewed in detail together with their recent crucial advances. The most recent applications, now spanning across materials science, nanotechnology, biology, medicine, geology, optics, catalysis, art conservation and other fields are also discussed. Even though AFM-IR and TERS have developed independently and have initially targeted different applications, rapid innovation in the last 5 years has pushed the performance of these, in principle spectroscopically complimentary, techniques well beyond initial expectations, thus opening new opportunities for their convergence. Therefore, subtle differences and complementarity will be highlighted together with emerging trends and opportunities.

Raman chemical imaging of intact non-flat tablets in regular and high-confocal mode

Curved and embossed surface of a tablet is Raman-imaged with API images produced by a variety of methods.