Lipid droplets (LDs) play a key role as the hub for lipid metabolism to maintain cellular metabolic homeostasis. Understanding the functions and changes of LDs in different pathological conditions is crucial for identifying new markers for diagnosis and discovering new targets for treatment. In recent years, coherent Raman scattering (CRS) microscopy has been popularized for the imaging and quantification of LDs in live cells. Compared to spontaneous Raman scattering microscopy, CRS microscopy offers a much higher imaging speed while maintaining similar chemical information. Due to the high lipid density, LDs usually have strong CRS signals and therefore are the most widely studied organelle in the CRS field. In this review, we discuss recent achievements using CRS to study the quantity, distribution, composition, and dynamics of LDs in various systems.

Raman spectroscopy shows promise as a biomarker for complex nerve and muscle (neuromuscular) diseases. To maximise its potential, several challenges remain. These include the sensitivity to different instrument configurations, translation across preclinical/human tissues and the development of multivariate analytics that can derive interpretable spectral outputs for disease identification. Nonnegative matrix factorisation (NMF) can extract features from high-dimensional data sets and the nonnegative constraint results in physically realistic outputs. In this study, we have undertaken NMF on Raman spectra of muscle obtained from different clinical and preclinical settings. First, we obtained and combined Raman spectra from human patients with mitochondrial disease and healthy volunteers, using both a commercial microscope and in-house fibre optic probe. NMF was applied across all data, and spectral patterns common to both equipment configurations were identified. Linear discriminant models utilising these patterns were able to accurately classify disease states (accuracy 70.2-84.5%). Next, we applied NMF to spectra obtained from the mouse model of a Duchenne muscular dystrophy and patients with dystrophic muscle conditions. Spectral fingerprints common to mouse/human were obtained and able to accurately identify disease (accuracy 79.5-98.8%). We conclude that NMF can be used to analyse Raman data across different equipment configurations and the preclinical/clinical divide. Thus, the application of NMF decomposition methods could enhance the potential of Raman spectroscopy for the study of fatal neuromuscular diseases.

The world is on the brink of facing coronavirus's (COVID-19) fourth wave as the mutant forms of viruses are escaping neutralizing antibodies in spite of being vaccinated. As we have already witnessed that it has encumbered our health system, with hospitals swamped with infected patients observed during the viral outbreak. Rapid triage of patients infected with SARS-CoV-2 is required during hospitalization to prioritize and provide the best point of care. Traditional diagnostics techniques such as RT-PCR and clinical parameters such as symptoms, comorbidities, sex and age are not enough to identify the severity of patients. Here, we investigated the potential of confocal Raman microspectroscopy as a powerful tool to generate an expeditious blood-based test for the classification of COVID-19 disease severity using 65 patients plasma samples from cohorts infected with SARS-CoV-2. We designed an easy manageable blood test where we used a small volume (8 μl) of inactivated whole plasma samples from infected patients without any extra solvent usage in plasma processing. Raman spectra of plasma samples were acquired and multivariate exploratory analysis PC-LDA (principal component based linear discriminant analysis) was used to build a model, which segregated the severe from the non-severe COVID-19 group with a sensitivity of 83.87%, specificity of 70.60% and classification efficiency of 76.92%. Among the bands expressed in both the cohorts, the study led to the identification of Raman fingerprint regions corresponding to lipids (1661, 1742), proteins amide I and amide III (1555, 1247), proteins (Phe) (1006, 1034), and nucleic acids (760) to be differentially expressed in severe COVID-19 patient's samples. In summary, the current study exhibits the potential of confocal Raman to generate simple, rapid, and less expensive blood tests to triage the severity of patients infected with SARS-CoV-2.

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS "spectral unmixing" from a multiplexed mixture of 7 SERS-active "nanorattles" loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSE = 6.42 × 10. These results demonstrate the potential of CNN-based ML to advance SERS-based diagnostics.

When performing computational modeling and machine learning experiments, it is imperative to follow a protocol that minimizes bias. In this communication, we share our concerns regarding the article "An efficient primary screening COVID-19 by serum Raman spectroscopy" published in this journal. We consider that the authors may have inadvertently biased their results by not guaranteeing complete independence of test samples from the training data. We corroborate our point by reproducing the experiment with the available data, showing that if full independence of the test set was ensured, the reported results should be lower. We ask the authors to provide more information regarding their article, as well as making available all code used to generate their results. Our experiments are available at https://doi.org/10.6084/m9.figshare.14124356.

The actinobacterial coproheme decarboxylase from catalyzes the final reaction to generate heme via the "coproporphyrin-dependent" heme biosynthesis pathway in the presence of hydrogen peroxide. The enzyme has a high reactivity toward HO used for the catalytic reaction and in the presence of an excess of HO new species are generated. Resonance Raman data, together with electronic absorption spectroscopy and mass spectrometry, indicate that an excess of hydrogen peroxide for both the substrate (coproheme) and product (heme ) complexes of this enzyme causes a porphyrin hydroxylation of ring C or D, which is compatible with the formation of an iron chlorin-type heme species. A similar effect has been previously observed for other heme-containing proteins, but this is the first time that a similar mechanism is reported for a coproheme enzyme. The hydroxylation determines a symmetry lowering of the porphyrin macrocycle, which causes the activation of A modes upon Soret excitation with a significant change in their polarization ratios, the enhancement and splitting into two components of many E bands, and an intensity decrease of the non-totally symmetric modes B, which become polarized. This latter effect is clearly observed for the isolated ν mode upon either Soret or Q-band excitations. The distal His118 is shown to be an absolute requirement for the conversion to heme . This residue also plays an important role in the oxidative decarboxylation, because it acts as a base for deprotonation and subsequent heterolytic cleavage of hydrogen peroxide.

The severe COVID-19 pandemic requires the development of novel, rapid, accurate, and label-free techniques that facilitate the detection and discrimination of SARS-CoV-2 infected subjects. Raman spectroscopy has been used to diagnose COVID-19 in serum samples of suspected patients without clinical symptoms of COVID-19 but presented positive immunoglobulins M and G (IgM and IgG) assays versus Control (negative IgM and IgG). A dispersive Raman spectrometer (830 nm, 350 mW) was employed, and triplicate spectra were obtained. A total of 278 spectra were used from 94 serum samples (54 Control and 40 COVID-19). The main spectral differences between the positive IgM and IgG versus Control, evaluated by principal component analysis (PCA), were features assigned to proteins including albumin (lower in the group COVID-19 and in the group IgM/IgG and IgG positive) and features assigned to lipids, phospholipids, and carotenoids (higher the group COVID-19 and in the group IgM/IgG positive). Features referred to nucleic acids, tryptophan, and immunoglobulins were also seen (higher the group COVID-19). A discriminant model based on partial least squares regression (PLS-DA) found sensitivity of 84.0%, specificity of 95.0%, and accuracy of 90.3% for discriminating positive Ig groups versus Control. When considering individual Ig group versus Control, it was found sensitivity of 77.3%, specificity of 97.5%, and accuracy of 88.8%. The higher classification error was found for the IgM group (no success classification). Raman spectroscopy may become a technique of choice for rapid serological evaluation aiming COVID-19 diagnosis, mainly detecting the presence of IgM/IgG and IgG after COVID-19 infection.

Lipid droplets are dynamic organelles that play important cellular roles. They are composed of a phospholipid membrane and a core of triglycerides and sterol esters. Fatty acids have important roles in phospholipid membrane formation, signaling, and synthesis of triglycerides as energy storage. Better non-invasive tools for profiling and measuring cellular lipids are needed. Here we demonstrate the potential of Raman spectroscopy to determine with high accuracy the composition changes of the fatty acids and cholesterol found in the lipid droplets of prostate cancer cells treated with various fatty acids. The methodology uses a modified least squares fitting (LSF) routine that uses highly discriminatory wavenumbers between the fatty acids present in the sample using a support vector machine algorithm. Using this new LSF routine, Raman micro-spectroscopy can become a better non-invasive tool for profiling and measuring fatty acids and cholesterol for cancer biology.

Deuterium labelling is increasingly used in coherent Raman imaging of complex systems, such as biological cells and tissues, to improve chemical specificity. Nevertheless, quantitative coherent Raman susceptibility spectra for deuterated compounds have not been previously reported. Interestingly, it is expected theoretically that -D stretch vibrations have a Raman susceptibility lower than -H stretch vibrations, with the area of their imaginary part scaling with their wavenumber, which is shifted from around 2900 cm for C-H into the silent region around 2100 cm for C-D. Here, we report quantitative measurements of the nonlinear susceptibility of water, succinic acid, oleic acid, linoleic acid and deuterated isoforms. We show that the -D stretch vibration has indeed a lower area, consistent with the frequency reduction due to the doubling of atomic mass from hydrogen to deuterium. This finding elucidates an important trade-off between chemical specificity and signal strength in the adoption of deuterium labelling as an imaging strategy for coherent Raman microscopy.

Carbonate rocks record the oldest forms of life on Earth, and their geologic reconstruction requires multiple methods to determine physical and chemical processes before conclusions of ancient biosignatures are made. Since crystal orientation within rock fabric may be used to infer geologic settings, we present here a complementary Raman method to study the orientation of calcite (CaCO) and dolomite [CaMg (CO)] minerals. The relative peak intensity ratio of the carbonate lattice E modes T and L reveals the crystallographic orientation of calcite and dolomite with respect to the incident light polarization. Our results for calcite show that when the incident laser light propagates down the crystallographic axis: (1) the L mode is always greater in intensity than the T mode ( < ), and (2) the spectra are most intense at 45° and least intense at 90° polarization angles measured from around the axis. Our results for dolomite show that (1) > when the incident light propagation is down the crystallographic axis and (2) < when the incident light propagation is down the crystallographic axis. This study reveals mineral orientation variation related to deposition and paragenesis within limestone and dolostone samples. The method presented yields information related to growth and deformation during diagenetic and metamorphic alteration and may be used in research seeking to identify the fabric parameters of any calcite or dolomite containing rock. The compositional and structural data obtained from Raman mapping is useful in structural geology, materials science, and biosignature research.

The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as sample-dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID-19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support-vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID-19 patients and 5 symptomatic COVID-19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups-confirmed COVID-19, suspected, and healthy individuals-the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID-19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85-0.88), and the accuracy between the COVID-19 and the healthy controls is 0.90 (95% CI: 0.89-0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67-0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum-level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID-19 screening.

The impact of tunable morphologies and plasmonic properties of gold nanostars are evaluated for the surface enhanced Raman scattering (SERS) detection of uranyl. To do so, gold nanostars are synthesized with varying concentrations of the Good's buffer reagent, 2-[4-(2-hydroxyethyl)-1-piperazinyl]propanesulfonic acid (EPPS). EPPS plays three roles including as a reducing agent for nanostar nucleation and growth, as a nanostar-stabilizing agent for solution phase stability, and as a coordinating ligand for the capture of uranyl. The resulting nanostructures exhibit localized surface plasmon resonance (LSPR) spectra that contain two visible and one near-infrared plasmonic modes. All three optical features arise from synergistic coupling between the nanostar core and branches. The tunability of these optical resonances are correlated with nanostar morphology through careful transmission electron microscopy (TEM) analysis. As the EPPS concentration used during synthesis increases, both the length and aspect ratio of the branches increase. This causes the two lower energy extinction features to grow in magnitude and become ideal for the SERS detection of uranyl. Finally, uranyl binds to the gold nanostar surface directly and via sulfonate coordination. Changes in the uranyl signal are directly correlated to the plasmonic properties associated with the nanostar branches. Overall, this work highlights the synergistic importance of nanostar morphology and plasmonic properties for the SERS detection of small molecules.

We introduce a high-sensitivity broadband stimulated Raman scattering (SRS) setup featuring wide spectral coverage (up to 500 cm) and high-frequency resolution (≈20 cm). The system combines a narrowband Stokes pulse, obtained by spectral filtering an Yb laser, with a broadband pump pulse generated by a home-built optical parametric oscillator. A single-channel lock-in amplifier connected to a single-pixel photodiode measures the stimulated Raman loss signal, whose spectrum is scanned rapidly using a galvanometric mirror after the sample. We use the in-line balanced detection approach to suppress laser fluctuations and achieve close to shot-noise-limited sensitivity. The setup is capable of measuring accurately the SRS spectra of several solvents and of obtaining hyperspectral data cubes consisting in the broadband SRS microscopy images of polymer beads test samples as well as of the distribution of different biological substances within plant cell walls.

Water plays a critical role in dental tissues including enamel and dentin. The characterization of water structure analysis was primarily conducted by nuclear magnetic resonance. Raman spectroscopy is a powerful analytic technology with capability for structure analysis in materials. However, acquiring high wavenumber Raman signals from dental tissues was challenging due to either the fluorescence interference under laser illumination or reduced sensitivity of CCD detectors. In this study, we demonstrate a pilot research on high wavenumber Raman analysis in dental tissues using a customized Raman spectrometer based on an InGaAs detector. A signal located at 3570 cm is found dominating the O-H region Raman spectra of enamel but is barely detectable from dentin. The profiles of the high wavenumber region Raman spectra changes with the locations in enamel, as well as the polarization of the excitation laser beam. The results suggest that the size or crystallinity differences of hydroxyapatite crystals are the main cause of the spectral variation from dentin to enamel, and could be partially responsible for the variation among different locations in enamel.

Vibrational spectroscopy is a very suitable tool for investigating the plant cell wall in situ with almost no sample preparation. The structural information of all different constituents is contained in a single spectrum. Interpretation therefore heavily relies on reference spectra and understanding of the vibrational behavior of the components under study. For the first time, we show infrared (IR) and Raman spectra of dibenzodioxocin (DBDO), an important lignin substructure. A detailed vibrational assignment of the molecule, based on quantum chemical computations, is given in the Supporting Information; the main results are found in the paper. Furthermore, we show IR and Raman spectra of synthetic guaiacyl lignin (dehydrogenation polymer-G-DHP). Raman spectra of DBDO and G-DHP both differ with respect to the excitation wavelength and therefore reveal different features of the substructure/polymer. This study confirms the idea previously put forward that Raman at 532 nm selectively probes end groups of lignin, whereas Raman at 785 nm and IR seem to represent the majority of lignin substructures.

Surface enhanced Raman spectroscopy (SERS) holds great promise in biosensing because of its single-molecule, label-free sensitivity. We describe here the use of a graphene-gold hybrid plasmonic platform that enables quantitative SERS measurement. Quantification is enabled by normalizing analyte peak intensities to that of the graphene G peak. We show that two complementary quantification modes are intrinsic features of the platform, and that through their combined use, the platform enables accurate determination of analyte concentration over a concentration range spanning seven orders of magnitude. We demonstrate, using a biologically relevant test analyte, the amyloid β-protein (Aβ), a seminal pathologic agent of Alzheimer's disease (AD), that linear relationships exist between (a) peak intensity and concentration at a single plasmonic hot spot smaller than 100 nm, and (b) frequency of hot spots with observable protein signals, i.e. the co-location of an Aβ protein and a hot spot. We demonstrate the detection of Aβ at a concentration as low as 10 M after a single 20 μl aliquot of the analyte onto the hybrid platform. This detection sensitivity can be improved further through multiple applications of analyte to the platform and by rastering the laser beam with smaller step sizes.

Raman and Brillouin spectroscopy enable non-invasive assessment of chemical and elastic properties of biomaterials, respectively. In this report, Brillouin micro-spectroscopy was used for the time-resolved analysis of elastic properties of and leaves, while Raman micro-spectroscopy was employed for the assessment of their chemical variation during drying. Spectroscopic assessment of elastic and chemical properties can improve our understanding of mechano-chemical changes of plants in response to environmental stress and pathogens at the microscopic cellular level. This report demonstrates the potential of multimodal optical sensing and imaging of plants as an emerging technique for the quantitative assessment of agricultural crops.

Stand-off Raman spectroscopy offers a highly selective technique to probe unknown substances from a safe distance. Often, it is necessary to scan large areas of interest. This can be done by pointwise imaging (PI), that is, spectra are sequentially acquired from an array of points over the region of interest (point-by-point mapping). Alternatively, in this paper a direct hyperspectral Raman imager is presented, where a defocused laser beam illuminates a wide area of the sample and the Raman scattered light is collected from the whole field of view (FOV) at once as a spectral snapshot filtered by a liquid crystal tunable filter to select a specific Raman shift. Both techniques are compared in terms of achievable FOV, spectral resolution, signal-to-noise performance, and time consumption during a measurement at stand-off distance of 15 m. The HSRI showed superior spectral resolution and signal-to-noise ratio, while more than doubling the FOV of the PI at laser power densities reduced by a factor of 277 at the target. Further, the output hyperspectral image data cube can be processed with state of the art chemometric algorithms like vertex component analysis in order to get a simple deterministic false color image showing the chemical composition of the target. This is shown for an artificial polymer sample, measured at a distance of 15 m.

Anatomical and chemical information can be linked by Raman imaging. Behind every pixel of the image is a Raman spectrum, which contains all the information as a molecular fingerprint. Yet to understand the spectra, the bands have to be assigned to components and their molecular structures. Although the lignin distribution is easily tracked in plant tissues, the assignment of the spectra is not good enough to allow in-depth analysis of the composition. Assignments of three lignin model compounds were derived from polarization measurements and quantum-chemical computations. Raman spectra of coniferyl alcohol crystals showed orientation dependence, which helped in band assignment. Abietin showed a Raman spectrum that was very similar to the spectrum of coniferyl alcohol, whereas its IR spectrum was very different due to bands of the sugar moiety. The Raman spectrum of coniferyl aldehyde is affected by the crystal order of molecules. All three compounds show much stronger band intensities than unconjugated single aromatic rings, indicating that the bulk of the lignin structure has significantly reduced contribution to Raman band intensities. Therefore, it is possible to highlight certain structures of lignin with Raman spectroscopy, because low amounts of a compound do not necessarily mean weak features in the spectrum.

The use of autologous stem/progenitor cells represents a promising approach to the repair of craniofacial bone defects. The calvarium is recognized as a viable source of stem/progenitor cells that can be transplanted in vitro to form bone. However, it is unclear if bone formed in cell culture is similar in quality to that found in native bone. In this study, the quality of bone mineral formed in osteogenic cell cultures were compared against calvarial bone from postnatal mice. Given the spectroscopic resemblance that exists between cell and collagen spectra, the feasibility of extracting information on cell activity and bone matrix quality were also examined. Stem/progenitor cells isolated from fetal mouse calvaria were cultured onto fused-quartz slides under osteogenic differentiation conditions for 28 days. At specific time intervals, slides were removed and analyzed by Raman microscopy and mineral staining techniques. We show that bone formed in culture at Day 28 resembled calvarial bone from 1-day-old postnatal mice with comparable mineralization, mineral crystallinity, and collagen crosslinks ratios. In contrast, bone formed at Day 28 contained a lower degree of ordered collagen fibrils compared with 1-day-old postnatal bone. Taken together, bone formed in osteogenic cell culture exhibited progressive matrix maturation and mineralization but could not fully replicate the high degree of collagen fibril order found in native bone.

Label-free live single-cell Raman spectroscopy was used to obtain a chemical fingerprint of colorectal cancer cells including the classification of the SW480 and SW620 cell line model system, derived from primary and secondary tumour cells from the same patient. High-quality Raman spectra were acquired from hundreds of live cells, showing high reproducibility between experiments. Principal component analysis with linear discriminant analysis yielded the best cell classification, with an accuracy of 98.7 ± 0.3% (standard error) when compared with discrimination trees or support vector machines. SW480 showed higher content of the disordered secondary protein structure Amide III band, whereas SW620 showed larger α-helix and β-sheet band content. The SW620 cell line also displayed higher nucleic acid, phosphates, saccharide, and CH content. HL60, HT29, HCT116, SW620, and SW480 live single-cell spectra were classified using principal component analysis or linear discriminant analysis with an accuracy of 92.4 ± 0.4% (standard error), showing differences mainly in the β-sheet content, the cytochrome C bands, the CH-stretching regions, the lactate contributions, and the DNA content. The lipids contributions above 2,900 cm and the lactate contributions at 1,785 cm appeared to be dependent on the colorectal adenocarcinoma stage, the advanced stage cell lines showing lower lipid, and higher lactate content. The results demonstrate that these cell lines can be distinguished with high confidence, suggesting that Raman spectroscopy on live cells can distinguish between different disease stages, and could play an important role clinically as a diagnostic tool for cell phenotyping.