Limitations and challenges of using Raman spectroscopy to detect the abiotic plant stress response

Multimodal detection and analysis of microplastics in human thrombi from multiple anatomically distinct sites

BACKGROUND

Microplastic (MP) pollution has emerged as a significant environmental concern worldwide. While extensive research has focused on their presence in marine organisms and ecosystems, their potential impact on human health, particularly on the circulatory system, remains understudied. This project aimed to identify and quantify the mass concentrations, polymer types, and physical properties of MPs in human thrombi surgically retrieved from both arterial and venous systems at three anatomically distinct sites, namely, cerebral arteries in the brain, coronary arteries in the heart, and deep veins in the lower extremities. Furthermore, this study aimed to investigate the potential association between the levels of MPs and disease severity.

METHODS

Thrombus samples were collected from 30 patients who underwent thrombectomy procedures due to ischaemic stroke (IS), myocardial infarction (MI), or deep vein thrombosis (DVT). Pyrolysis-gas chromatography mass spectrometry (Py-GC/MS) was employed to identify and quantify the mass concentrations of the MPs. Laser direct infrared (LDIR) spectroscopy and scanning electron microscopy (SEM) were used to analyse the physical properties of the MPs. Demographic and clinical information were also examined. A rigorous quality control system was used to eliminate potential environmental contamination.

FINDINGS

MPs were detected by Py-GC/MS in 80% (24/30) of the thrombi obtained from patients with IS, MI, or DVT, with median concentrations of 61.75 μg/g, 141.80 μg/g, and 69.62 μg/g, respectively. Among the 10 target types of MP polymers, polyamide 66 (PA66), polyvinyl chloride (PVC), and polyethylene (PE) were identified. Further analyses suggested that higher concentrations of MPs may be associated with greater disease severity (adjusted β = 7.72, 95% CI: 2.01-13.43, p < 0.05). The level of D-dimer in the MP-detected group was significantly higher than that in the MP-undetected group (8.3 ± 1.5 μg/L vs 6.6 ± 0.5 μg/L, p < 0.001). Additionally, LDIR analysis showed that PE was dominant among the 15 types of identified MPs, accounting for 53.6% of all MPs, with a mean diameter of 35.6 μm. The shapes of the polymers detected using LDIR and SEM were found to be heterogeneous.

INTERPRETATION

This study presents both qualitative and quantitative evidence of the presence of MPs, and their mass concentrations, polymer types, and physical properties in thrombotic diseases through the use of multimodal detection methods. Higher concentrations of MPs may be associated with increased disease severity. Future research with a larger sample size is urgently needed to identify the sources of exposure and validate the observed trends in the study.

FUNDING

This study was funded by the SUMC Scientific Research Initiation Grant (SRIG, No. 009-510858038), Postdoctoral Research Initiation Grant (No. 202205230031-3), and the 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (No. 2020LKSFG02C).

Label-free SERS for rapid identification of interleukin 6 based on intrinsic SERS fingerprint of antibody‑gold nanoparticles conjugate

A label-free surface-enhanced Raman scattering (SERS) was designed for sensitive detection of interleukin-6 (IL-6). The sensing element composed of anti-IL-6 antibodies adsorbed on the surface of spherical gold nanoparticles (AuNPs) as SERS-active surface. The principle of detection was probing antibody conformational changes using its intrinsic SERS fingerprint after binding to IL-6. Comparison of SERS spectra of antibody before and after binding to IL-6 showed that secondary structure of antibody does not change upon binding to IL-6. Vibrational information from disulfide bonds ν(SS) in antibody structure indicated some changes of geometry around SS bridges as a consequence of the immunocomplex formation. Transmission electron microscopy (TEM) and UV-Vis spectroscopy were used to confirm AuNPs conjugation with antibody as well as IL-6 binding to antibody on the surface of AuNPs. The SERS-based immunoassay showed a wide linear range (2.0-1000 pg mL-1) and a high sensitivity with a limit of detection (LOD) as low as 0.91 pg mL-1 (0.04 pM) without using any extrinsic Raman label. UV-Vis spectroscopy was employed as a conventional method for IL-6 detection based on observation of any change in the position of localized surface plasmon resonance (LSPR) band of AuNPs-antibody conjugates with LOD of 10 ng mL-1.

Raman spectroscopy in crop quality assessment: focusing on sensing secondary metabolites: a review

As a crop quality sensor, Raman spectroscopy has been consistently proposed as one of the most promising and non-destructive methods for qualitative and quantitative analysis of plant substances, because it can measure molecular structures in a short time without requiring pretreatment along with simple usage. The sensitivity of the Raman spectrum to target chemicals depends largely on the wavelength, intensity of the laser power, and exposure time. Especially for plant samples, it is very likely that the peak of the target material is covered by strong fluorescence effects. Therefore, methods using lasers with low energy causing less fluorescence, such as 785 nm or near-infrared, are vigorously discussed. Furthermore, advanced techniques for obtaining more sensitive and clear spectra, like surface-enhanced Raman spectroscopy, time-gated Raman spectroscopy or combination with thin-layer chromatography, are being investigated. Numerous interpretations of plant quality can be represented not only by the measurement conditions but also by the spectral analysis methods. Up to date, there have been attempted to optimize and generalize analysis methods. This review summarizes the state of the art of micro-Raman spectroscopy in crop quality assessment focusing on secondary metabolites, from in vitro to in vivo and even in situ, and suggests future research to achieve universal application.

Raman imaging monitors the time-resolved response of A. thaliana to the artificial inhibition of PSII

The short-term (0-96 h) response of A. thaliana to the oxidative stress induced by PSII inhibitor metribuzin was examined using Raman spectroscopy. Whole leaves of wildtype (WT, Col-0) and ros1 mutant were scanned and changes in carotenoids were examined. Strong differences in Raman intensity distributions between WT and ros1 were observed. A stronger decrease of carotenoid v₁(C=C) band intensity across the leaf was observed in ros1 after 48 h of exposure to metribuzin. It can be assumed that higher sensitivity to oxidative stress in ros1 mutant results in significantly faster degradation of carotenoids.

Non-destructive insights into photosynthetic and photoprotective mechanisms in Arabidopsis thaliana grown under two light regimes

Probing insights into understanding photosynthetic processes via non-invasive means has an added advantage when used in phenotyping or precision agriculture. We employed Raman spectroscopy and fluorescence-based methods to investigate both the changes in the photosynthetic processes and the underlying protective mechanisms on Arabidopsis thaliana wild-type (WT), and ros1, which is a mutant of a repressor of transcriptional gene silencing, both grown under low light (LL: 100 μmol m^-2s^-1) and high light (HL: 400 μmol m^-2s^-1) regimes. Raman imaging detected a lower carotenoid intensity after two weeks in those plants grown under HL, compared to those grown under the LL regime; we interpret this as the result of oxidative damage of β-carotene molecules. Further, the data revealed a significant depletion in carotenoids with enhanced phenolics around the midrib and tip of the WT leaves, but not in the ros1. On the contrary, small necrotic zones appeared after two weeks of HL in the ros1 mutant, pointing to the starting oxidative damage. The lower maximum quantum yield of the photochemistry (F_v/F_m) in the WT as well as in the ros1 mutant grown in HL (compared to those in the LL two weeks post-exposure), indicates the HL partially inactivated photosystems. Chlorophyll a fluorescence imaging further showed high non-photochemical quenching (NPQ) in the plants grown under the HL regime for both the WT and the ros1 mutant, but the spatial heterogeneity of NPQ images was much higher in the HL-grown ros1 mutant. Fluorescence screening methods revealed significantly high values of chlorophyll proxies in the WT as well as in the ros1 mutant two weeks after in the HL compared to those under LL. The data generally revealed an increased accumulation of phenolics under HL in both the WT and ros1 mutant plants, but the proxies of anthocyanin and flavonols were significantly lower in the ros1 mutant than in the WT. The comparatively low accumulation of anthocyanin in the ros1 mutant compared to the WT supports the Raman data. We conclude that integrated use of these techniques can be efficiently applied for a better understanding of insights into photosynthetic mechanisms.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy

Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO3)2) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO3)2 molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an 84.6% balanced accuracy for the cross-batch testing task.

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

Raman Spectroscopy Can Distinguish Glyphosate-Susceptible and -Resistant Palmer Amaranth (Amaranthus palmeri)

The non-judicious use of herbicides has led to a widespread evolution of herbicide resistance in various weed species including Palmer amaranth, one of the most aggressive and troublesome weeds in the United States. Early detection of herbicide resistance in weed populations may help growers devise alternative management strategies before resistance spreads throughout the field. In this study, Raman spectroscopy was utilized as a rapid, non-destructive diagnostic tool to distinguish between three different glyphosate-resistant and four -susceptible Palmer amaranth populations. The glyphosate-resistant populations used in this study were 11-, 32-, and 36-fold more resistant compared to the susceptible standard. The 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene copy number for these resistant populations ranged from 86 to 116. We found that Raman spectroscopy could be used to differentiate herbicide-treated and non-treated susceptible populations based on changes in the intensity of vibrational bands at 1156, 1186, and 1525 cm-1 that originate from carotenoids. The partial least squares discriminant analysis (PLS-DA) model indicated that within 1 day of glyphosate treatment (D1), the average accuracy of detecting herbicide-treated and non-treated susceptible populations was 90 and 73.3%, respectively. We also found that glyphosate-resistant and -susceptible populations of Palmer amaranth can be easily detected with an accuracy of 84.7 and 71.9%, respectively, as early as D1. There were relative differences in the concentration of carotenoids in plants with different resistance levels, but these changes were not significant. The results of the study illustrate the utility of Raman spectra for evaluation of herbicide resistance and stress response in plants under field conditions.

Environmentally-Controlled Near Infrared Spectroscopic Imaging of Bone Water

We have designed an environmentally-controlled chamber for near infrared spectroscopic imaging (NIRSI) to monitor changes in cortical bone water content, an emerging biomarker related to bone quality assessment. The chamber is required to ensure repeatable spectroscopic measurements of tissues without the influence of atmospheric moisture. A calibration curve to predict gravimetric water content from human cadaveric cortical bone was created using NIRSI data obtained at six different lyophilization time points. Partial least squares (PLS) models successfully predicted bone water content that ranged from 0–10% (R = 0.96, p < 0.05, root mean square error of prediction (RMSEP) = 7.39%), as well as in the physiologic range of 4–10% of wet tissue weight (R = 0.87, p < 0.05, RMSEP = 14.5%). Similar results were obtained with univariate and bivariate regression models for prediction of water in the 0–10% range. Further, we identified two new NIR bone absorbances, at 6560 cm⁻¹ and 6688 cm⁻¹, associated with water and collagen respectively. Such data will be useful in pre-clinical studies that investigate changes in bone quality with disease, aging and with therapeutic use.

Label-Free SERS Quantum Semiconductor Probe for Molecular-Level and in Vitro Cellular Detection: A Noble-Metal-Free Methodology

Accurate in vitro molecular-level analysis is an essential step prior to in vivo and clinical application for early diagnosis and cancer treatment. Among the diagnostic techniques, surface-enhanced Raman scattering (SERS) biosensing has shown growing potential due to its noninvasive and real-time characterization of the biomolecules. However, the application of SERS biosensing is mostly limited to the plasmonic noble metals, in the form of either nanoparticles or tips and substrates (fixed probe), on which surface plasmon resonance (SPR) is the prominent enhancement principle. The semiconductor quantum particles have been explored in several optoelectronics applications, but have never been reported to be exploited as a means of surface-enhanced Raman scattering (SERS) for molecular-level and intracellular sensing. Here, we report on the new generation of noble-metal-free SERS probe; Si@SiO₂ quantum probe (Si@SiO₂ Q-probe) whose affinity to functional groups not only imitates a self-driven labeling attribution that enables charge transfer (CT) as an augmented enhancement principle but also its mobile nature in miniaturized scale facilitates endocytosis for in situ live cell biosensing. Moreover, a significant enhancement factor of 10⁶ of rhodamine 6G (R6G) and 10⁷ of glutathione (GSH) at ∼5 × 10^-12 pM concentration has been achieved that is comparable to inherently plasmonic noble metals. Our results showed a capability of the Si@SiO₂ Q-probe to unveil the "biochemical fingerprint" of substantial components of mammalian and cancerous cervical cells, which leads to diagnosis of cervical cancer. These unique attributions of the Si@SiO₂ Q-probe can provide better insight into cell mutation and malignancy.

Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli

To be able to predict antibiotic resistance in bacteria from fast label-free microscopic observations would benefit a broad range of applications in the biological and biomedical fields. Here, we demonstrate the utility of label-free Raman spectroscopy in monitoring the type of resistance and the mode of action of acquired resistance in a bacterial population of Escherichia coli, in the absence of antibiotics. Our findings are reproducible. Moreover, we identified spectral regions that best predicted the modes of action and explored whether the Raman signatures could be linked to the genetic basis of acquired resistance. Spectral peak intensities significantly correlated (False Discovery Rate, p < 0.05) with the gene expression of some genes contributing to antibiotic resistance genes. These results suggest that the acquisition of antibiotic resistance leads to broad metabolic effects reflected through Raman spectral signatures and gene expression changes, hinting at a possible relation between these two layers of complementary information.

Techniques for characterizing the mode of action of antibiotic resistance are crucial for developing new antimicrobial drugs. Arno Germond et al. have used Raman spectroscopy combined with gene expression to investigate large metabolic changes that occur when bacteria acquire antibiotic resistance.