Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategies to improve clinical outcomes. Raman spectroscopy (RS) can monitor post-irradiation biomolecular changes and signatures of radiation response in tumour cells in a label-free manner. Convolutional Neural Networks (CNN) perform feature extraction directly from data in an end-to-end learning manner, with high classification performance. Furthermore, recently developed CNN explainability techniques help visualize the critical discriminative features captured by the model. In this work, a CNN is developed to characterize tumour response to radiotherapy based on its degree of radioresistance. The model was trained to classify Raman spectra of three human tumour cell lines as radiosensitive (LNCaP) or radioresistant (MCF7, H460) over a range of treatment doses and data collection time points. Additionally, a method based on Gradient-Weighted Class Activation Mapping (Grad-CAM) was used to determine response-specific salient Raman peaks influencing the CNN predictions. The CNN effectively classified the cell spectra, with accuracy, sensitivity, specificity, and F1 score exceeding 99.8%. Grad-CAM heatmaps of H460 and MCF7 cell spectra (radioresistant) exhibited high contributions from Raman bands tentatively assigned to glycogen, amino acids, and nucleic acids. Conversely, heatmaps of LNCaP cells (radiosensitive) revealed activations at lipid and phospholipid bands. Finally, Grad-CAM variable importance scores were derived for glycogen, asparagine, and phosphatidylcholine, and we show that their trends over cell line, dose, and acquisition time agreed with previously established models. Thus, the CNN can accurately detect biomolecular differences in the Raman spectra of tumour cells of varying radiosensitivity without requiring manual feature extraction. Finally, Grad-CAM may help identify metabolic signatures associated with the observed categories, offering the potential for automated clinical tumour radiation response characterization.

Synthesis of Graphene and Graphene Films with Minimal Structural Defects.

Graphene is a carbon material with extraordinary properties that has been drawing a significant amount of attention in the recent decade. High-quality graphene can be produced by different methods, such as epitaxial growth, chemical vapor deposition, and micromechanical exfoliation. The reduced graphene oxide route is, however, the only current approach that leads to the large-scale production of graphene materials at a reasonable cost. Unfortunately, graphene oxide reduction normally yields graphene materials with a high defect density. Here, we introduce a new route for the large-scale synthesis of graphene that minimizes the creation of structural defects. The method involves high-quality hydrogen functionalization of graphite followed by thermal dehydrogenation. We also demonstrated that the hydrogenated graphene synthesis route can be used for the preparation of high-quality graphene films on glass substrates. A reliable method for the preparation of these types of films is essential for the widespread implementation of graphene devices. The structural evolution from the hydrogenated form to graphene, as well as the quality of the materials and films, was carefully evaluated by Raman spectroscopy.

Detection of SARS-CoV-2 in saliva by a low-cost LSPR-based sensor.

The SARS-CoV-2 pandemic started more than 3 years ago, but the containment of the spread is still a challenge. Screening is imperative for informed decision making by government authorities to contain the spread of the virus locally. The access to screening tests is disproportional, due to the lack of access to reagents, equipment, finances or because of supply chain disruptions. Low and middle-income countries have especially suffered with the lack of these resources. Here, we propose a low cost and easily constructed biosensor device based on localized surface plasmon resonance, or LSPR, for the screening of SARS-CoV-2. The biosensor device, dubbed "sensor" for simplicity, was constructed in two modalities: (1) viral detection in saliva and (2) antibody against COVID in saliva. Saliva collected from 18 patients were tested in triplicates. Both sensors successfully classified all COVID positive patients (among hospitalized and non-hospitalized). From the COVID negative patients 7/8 patients were correctly classified. For both sensors, sensitivity was determined as 100% (95% CI 79.5-100) and specificity as 87.5% (95% CI 80.5-100). The reagents and equipment used for the construction and deployment of this sensor are ubiquitous and low-cost. This sensor technology can then add to the potential solution for challenges related to screening tests in underserved communities.

A Direct Immunoassay Based on Surface-Enhanced Spectroscopy Using AuNP/PS-b-P2VP Nanocomposites.

A biosensor was developed for directly detecting human immunoglobulin G (IgG) and adenosine triphosphate (ATP) based on stable and reproducible gold nanoparticles/polystyrene-b-poly(2-vinylpyridine) (AuNP/PS-b-P2VP) nanocomposites. The substrates were functionalized with carboxylic acid groups for the covalent binding of anti-IgG and anti-ATP and the detection of IgG and ATP (1 to 150 µg/mL). SEM images of the nanocomposite show 17 ± 2 nm AuNP clusters adsorbed over a continuous porous PS-b-P2VP thin film. UV-VIS and SERS were used to characterize each step of the substrate functionalization and the specific interaction between anti-IgG and the targeted IgG analyte. The UV-VIS results show a redshift of the LSPR band as the AuNP surface was functionalized and SERS measurements showed consistent changes in the spectral features. Principal component analysis (PCA) was used to discriminate between samples before and after the affinity tests. Moreover, the designed biosensor proved to be sensitive to different concentrations of IgG with a limit-of-detection (LOD) down to 1 µg/mL. Moreover, the selectivity to IgG was confirmed using standard solutions of IgM as a control. Finally, ATP direct immunoassay (LOD = 1 µg/mL) has demonstrated that this nanocomposite platform can be used to detect different types of biomolecules after proper functionalization.

Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted Non-Negative Matrix Factorization.

Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, on the condition that all biochemicals in the mixture were included as bases chemicals in the model. We also report that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely due to similarity in the individual bases spectra.

High-Speed Spectral Characterization of Single-Molecule SERS Fluctuations.

The concept of plasmonic "hotspots" is central to the broad field of nanophotonics. In surface-enhanced Raman scattering (SERS), hotspots can increase Raman scattering efficiency by orders of magnitude. Hotspot dimensions may range from a few nanometers down to the atomic scale and are able to generate SERS signals from single molecules. However, these single-molecule SERS signals often show significant fluctuations, and the concept of intense, localized, yet static hotspots has come into question. Recent experiments have shown these SERS intensity fluctuations (SIFs) to occur over an extremely wide range of timescales, from seconds to microseconds, due to the various physical mechanisms causing SERS and the dynamic nature of light-matter interaction at the nanoscale. The underlying source of single-molecule SERS fluctuations is therefore likely to be a complex interplay of several different effects at different timescales. A high-speed acquisition system that captures a full SERS spectrum with microsecond time resolution can therefore provide information about these dynamic processes. Here, we show an acquisition system that collects at a rate of 100,000 SERS spectra per second, allowing high-speed characterization. We find that while each individual SIF event will enhance a different portion of the SERS spectrum, including a single peak, over 10s to 100s of microseconds, the SIF events overall do not favor one region of the spectrum over another. These high-speed SIF events can therefore occur with relatively equal probability over a broad spectral range, covering both the anti-Stokes and the Stokes sides of the spectrum, sometimes leading to anomalously large anti-Stokes peaks. This indicates that both temporally and spectrally transient hotspots drive the SERS fluctuations at high speeds.

Exploring Intensity Distributions and Sampling in SERS-Based Immunoassays.

Surface-enhanced Raman scattering (SERS) is a sensitive, widely used spectroscopic technique. However, SERS is perceived as poorly reproducible and insufficiently robust for standard applications in analytical chemistry. Here, we demonstrated that reliable SERS immunoassay quantification at low concentrations (pM range) can be achieved by careful experimental design and appropriate data analysis statistics. A SERS-based immunoassay for IgG in human serum (3.1-50.0 ng mL-1 or 20.6-333 pM) was developed as a proof of concept. Calibration curves were created using the population median of the band area, centered at 592 cm-1, of a SERS reporter (Nile Blue A). Histograms of 7200 SERS spectra show lognormal distributions. SEM images of the sensor platform confirm a correlation between the number of SERS probes (ERLs) at the surface and the SERS intensity response. The IgG immunosensor reported here presented a limit of detection of 1.11 ng mL-1 or 7.39 pM and a limit of quantification of 9.04 ng mL-1 or 60.30 pM, within a 95% confidence level. The % error of the predicted versus the actual response of a quality control (QC) sample was 0.13%. The percent error of the QC sample decreases exponentially with the number of measurements. Randomly selected spatially separated measurements provided lower QC % error than a larger number of measurements that were closely spaced. We propose that it is necessary to describe the measured populations using an appropriate sample size for good statistics and consider the interrogation of sufficiently large and well-separated areas of the sensor surface to achieve a reliable sampling.

An Economical and Scalable Method to Synthesize Graphitic-Like Films.

An economical and facile method to synthesize a precursor for carbon films and materials has been developed. This precursor can be easily coated onto substrates without binder reagents and then converted into a graphitic-like structure after mild thermal treatment. This approach potentially allows the coating of glass surfaces of different shapes and forms, such as the inside of a glass tube, for instance. The precursor consists of tetrahedral halocarbyne units which randomly combine through single electron transfer with organometallic compounds to create a poly(carbyne)-like polymeric material. Advanced characterization tools reveal that the synthesized product (poly(halocarbyne) or PXC, where X indicate the presence of halogens, is composed mostly of carbon, hydrogen, and a variable percentage of residual halocarbon groups. Therefore, it possesses good solubility in organic solvents and can be coated on any complex substrate. The coated PXC material produced here was annealed under mild conditions, leading to the production of a graphitic-like film on a glass substrate. The chemical homogeneity of the carbon material of the film was confirmed by Raman spectroscopy.

Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues.

Recent advancements in anatomical imaging of tumours as treatment targets have led to improvements in RT. However, it is unlikely that improved anatomical imaging alone will be the sole driver for new advances in personalised RT. Biochemically based radiobiological information is likely to be required for next-generation improvements in the personalisation of radiotherapy dose prescriptions to individual patients. In this paper, we use Raman spectroscopy (RS), an optical technique, to monitor individual biochemical response to radiation within a tumour microenvironment. We spatially correlate individual biochemical responses to augmentatively derived hypoxic maps within the tumour microenvironment. Furthermore, we pair RS with a data analytical framework combining (i) group and basis restricted non-negative matrix factorization (GBR-NMF), (ii) a random forest (RF) classifier, (iii) and a feature metric importance calculation method, Shapley Additive exPlanations (SHAP), in order to ascertain the relative importance of individual biochemicals in describing the overall biological response as observed with RS. The current study found that the GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators (e.g., glycogen, lipids, DNA, amino acids) in H460 human lung cancer cells and H460 xenografts. Correlations between the hypoxic regions and Raman chemical biomarkers (e.g., glycogen, alanine, and arginine) were also identified in H460 xenografts. To summarize, GBR-NMF-RF-SHAP combined with RS can be applied to monitor the RT-induced biochemical response within cellular and tissue environments. Individual biochemicals were identified that (i) contributed to overall biological response to radiation, and (ii) spatially correlated with hypoxic regions of the tumour. RS combined with our analytical pipeline shows promise for further understanding of individual biochemical dynamics in radiation response for use in cancer therapy.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study.

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer.

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Nanotechnology Driven Cancer Chemoradiation: Exploiting the Full Potential of Radiotherapy with a Unique Combination of Gold Nanoparticles and Bleomycin.

One of the major issues in current radiotherapy (RT) is the associated normal tissue toxicity. Enhancement of the RT effect with novel radiosensitizers can address this need. In this study, gold nanoparticles (GNPs) and bleomycin (BLM) were used as a unique combination of radiosensitizers. GNPs offer a two-fold promise as a delivery vehicle for BLM and as a radiosensitizing agent. In this study, GNPs were functionalized and complexed with BLM using a gold-thiol bond (denoted GNP-BLM). Our results show that there was a 40% and 10% decrease in cell growth with GNP-BLM vs. free BLM for the MIA PaCa-2 and PC-3 cell lines, respectively. Testing the GNP-BLM platform with RT showed an 84% and 13% reduction in cell growth in MIA PaCa-2 cells treated with GNP-BLM and GNPs, respectively. Similar results were seen with PC-3 cells. The efficacy of this approach was verified by mapping DNA double-strand breaks (DSBs) as well. Therefore, this proposed incorporation of nanomedicine with RT is promising in achieving a significantly higher therapeutic ratio which is necessary to make a paradigm change to the current clinical approach.

Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer.

Raman spectroscopy is a non-invasive optical technique that can be used to investigate biochemical information embedded in cells and tissues exposed to ionizing radiation used in cancer therapy. Raman spectroscopy could potentially be incorporated in personalized radiation treatment design as a tool to monitor radiation response in at the metabolic level. However, tracking biochemical dynamics remains challenging for Raman spectroscopy. Here we developed a novel analytical framework by combining group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF). This framework can monitor radiation response profiles in different molecular histotypes and biochemical dynamics in irradiated breast cancer cells. Five subtypes of; human breast cancer (MCF-7, BT-474, MDA-MB-230, and SK-BR-3) and normal cells derived from human breast tissue (MCF10A) which had been exposed to ionizing radiation were tested in this framework. Reference Raman spectra of 20 biochemicals were collected and used as the constrained Raman biomarkers in the GBR-NMF-RF framework. We obtained scores for individual biochemicals corresponding to the contribution of each Raman reference spectrum to each spectrum obtained from the five cell types. A random forest classifier was then fitted to the chemical scores for performing molecular histotype classifications (HER2, PR, ER, Ki67, and cancer versus non-cancer) and assessing the importance of the Raman biochemical basis spectra for each classification test. Overall, the GBR-NMF-RF framework yields classification results with high accuracy (>97%), high sensitivity (>97%), and high specificity (>97%). Variable importance calculated in the random forest model indicated high contributions from glycogen and lipids (cholesterol, phosphatidylserine, and stearic acid) in molecular histotype classifications.

Digital plasmonic holography with iterative phase retrieval for sensing.

Propagating surface plasmon waves have been used for many applications including imaging and sensing. However, direct in-plane imaging of micro-objects with surface plasmon waves suffers from the lack of simple, two-dimensional lenses, mirrors, and other optical elements. In this paper, we apply lensless digital holographic techniques and leakage radiation microscopy to achieve in-plane surface imaging with propagating surface plasmon waves. As plasmons propagate in two-dimensions and scatter from various objects, a hologram is formed over the surface. Iterative phase retrieval techniques applied to this hologram remove twin image interference for high-resolution in-plane imaging and enable further applications in real-time plasmonic phase sensing.

Raman spectroscopy and group and basis-restricted non negative matrix factorisation identifies radiation induced metabolic changes in human cancer cells.

This work combines single cell Raman spectroscopy (RS) with group and basis restricted non-negative matrix factorisation (GBR-NMF) to identify individual biochemical changes associated with radiation exposure in three human cancer cell lines. The cell lines analysed were derived from lung (H460), breast (MCF7) and prostate (LNCaP) tissue and are known to display varying degrees of radio sensitivity due to the inherent properties of each cell type. The GBR-NMF approach involves the deconstruction of Raman spectra into component biochemical bases using a library of Raman spectra of known biochemicals present in the cells. Subsequently, scores are obtained on each of these bases which can be directly correlated with the contribution of each chemical to the overall Raman spectrum. We validated GBR-NMF through the correlation of GBR-NMF-derived glycogen scores with scores that were previously observed using principal component analysis (PCA). Phosphatidylcholine, glucose, arginine and asparagine showed a distinct differential score pattern between radio-resistant and radio-sensitive cell types. In summary, the GBR-NMF approach allows for the monitoring of individual biochemical radiation-response dynamics previously unattainable with more traditional PCA-based approaches.

Selective suppression of {112} anatase facets by fluorination for enhanced TiO2 particle size and phase stability at elevated temperatures.

Generally, anatase is the most desirable TiO2 polymorphic phase for photovoltaic and photocatalytic applications due to its higher photoconductivity and lower recombination rates compared to the rutile phase. However, in applications where temperatures above 500 °C are required, growing pure anatase phase nanoparticles is still a challenge, as above this temperature TiO2 crystallite sizes are larger than 35 nm which thermodynamically favors the growth of rutile crystallites. In this work, we show strong evidence, for the first time, that achieving a specific fraction (50%) of the {112} facets on the TiO2 surface is the key limiting step for anatase-to-rutile phase transition, rather than the crystallite size. By using a fluorinated ionic liquid (IL) we have obtained pure anatase phase crystallites at temperatures up to 800 °C, even after the crystallites have grown beyond their thermodynamic size limit of ca. 35 nm. While fluorination by the IL did not affect {001} growth, it stabilized the pure anatase TiO2 by suppressing the formation of {112} facets on anatase particles. By suppressing the {112} facets, using specific concentrations of fluorinated ionic liquid in the TiO2 synthesis, we controlled the anatase-to-rutile phase transition over a wide range of temperatures. This information shall help synthetic researchers to determine the appropriate material conditions for specific applications.

Detection of Buried Explosives Using a Surface-Enhanced Raman Scattering (SERS) Substrate Tailored for Miniaturized Spectrometers.

The advent of miniaturized, fiber-based, Raman spectrometers provides a clear path for the wide implementation of surface-enhanced Raman scattering (SERS) in analytical chemistry. For instance, miniaturized systems are especially useful in field applications due to their simplicity and low cost. However, traditional SERS substrates are generally developed and optimized using expensive Raman microscope systems equipped with high numerical aperture (NA) objective lenses. Here, we introduced a new type of SERS substrate with intrinsic Raman photon directing capability that compensates the relatively low signal collection power of fiber-based Raman spectrometers. The substrate was tested for the detection of buried 2,4-dinitrotoluene in simulated field conditions. A linear calibration curve (R2 = 0.98) for 2,4-dinitrotoluene spanning 3 orders of magnitude (from µg kg-1 to mg kg-1) was obtained with a limit of detection of 10 µg kg-1 within a total volume of 10 µL. This detection level is 2 orders of magnitude lower than that possible with the current state-of-the-art technologies, such as ion mobility spectrometry-mass spectrometry. The approach reported here demonstrated a high-performance detection of 2,4-dinitrotoluene in field conditions by a SERS platform optimized for miniaturized Raman systems that can be deployed for a routine inspection of landmine-contaminated sites and homeland security applications.

High-Speed Fluctuations in Surface-Enhanced Raman Scattering Intensities from Various Nanostructures.

The observation of single molecule events using surface-enhanced Raman scattering (SERS) is a well-established phenomenon. These events are characterized by strong fluctuations in SERS intensities. High-speed SERS intensity fluctuations (in the microsecond time scale) have been reported for experiments involving single metallic particles. In this work, the high-speed SERS behavior of six different types of nanostructured metal systems (Ag nanoshells, Ag nanostars, Ag aggregated spheres, Au aggregated spheres, particle-on-mirror, and Ag deposited on microspheres) was investigated. All systems demonstrated high-speed SERS intensity fluctuations. Statistical analysis of the duration of the SERS fluctuations yielded tailed distributions with average event durations around 100 µs. Although the characteristics of the fluctuations seem to be random, the results suggest interesting differences between the system that might be associated with the strength distribution and density of the localized SERS hotspots. For instance, systems with more localized fields, such as nanostars, present faster fluctuation bursts compared to metallic aggregates that support spread-out fields. The results presented here appear to confirm that high-speed SERS intensity fluctuations are a fundamental characteristic of the SERS effect.

Monitor Ionizing Radiation-Induced Cellular Responses with Raman Spectroscopy, Non-Negative Matrix Factorization, and Non-Negative Least Squares.

Radiation therapy (RT) is one of the most commonly prescribed cancer treatments. New tools that can accurately monitor and evaluate individual patient responses would be a major advantage and lend to the implementation of personalized treatment plans. In this study, Raman spectroscopy (RS) was applied to examine radiation-induced cellular responses in H460, MCF7, and LNCaP cancer cell lines across different dose levels and times post-irradiation. Previous Raman data analysis was conducted using principal component analysis (PCA), which showed the ability to extract biological information of glycogen. In the current studies, the use of non-negative matrix factorization (NMF) allowed for the discovery of multiplexed biological information, specifically uncovering glycogen-like and lipid-like component bases. The corresponding scores of glycogen and previously unidentified lipids revealed the content variations of these two chemicals in the cellular data. The NMF decomposed glycogen and lipid-like bases were able to separate the cancer cell lines into radiosensitive and radioresistant groups. A further lipid phenotype investigation was also attempted by applying non-negative least squares (NNLS) to the lipid-like bases decomposed individually from three cell lines. Qualitative differences found in lipid weights for each lipid-like basis suggest the lipid phenotype differences in the three tested cancer cell lines. Collectively, this study demonstrates that the application of NMF and NNLS on RS data analysis to monitor ionizing radiation-induced cellular responses can yield multiplexed biological information on bio-response to RT not revealed by conventional chemometric approaches.