Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug.

Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.

New technologies and reagents in lateral flow assay (LFA) designs for enhancing accuracy and sensitivity.

Lateral flow assays (LFAs) are a popular method for quick and affordable diagnostic testing because they are easy to use, portable, and user-friendly. However, LFA design has always faced challenges regarding sensitivity, accuracy, and complexity of the operation. By integrating new technologies and reagents, the sensitivity and accuracy of LFAs can be improved while minimizing the complexity and potential for false positives. Surface enhanced Raman spectroscopy (SERS), photoacoustic techniques, fluorescence resonance energy transfer (FRET), and the integration of smartphones and thermal readers can improve LFA accuracy and sensitivity. To ensure reliable and accurate results, careful assay design and validation, appropriate controls, and optimization of assay conditions are necessary. Continued innovation in LFA technology is crucial to improving the reliability and accuracy of rapid diagnostic testing and expanding its applications to various areas, such as food testing, water quality monitoring, and environmental testing.

Effect of gamma irradiation on the physical properties of MoS2 monolayer.

Two-dimensional transition metal dichalcogenides (2D-TMDs) have been proposed as novel optoelectronic materials for space applications due to their relatively light weight. MoS2 has been shown to have excellent semiconducting and photonic properties. Although the strong interaction of ionizing gamma radiation with bulk materials has been demonstrated, understanding its effect on atomically thin materials has scarcely been investigated. Here, we report the effect of gamma irradiation on the structural and electronic properties of a monolayer of MoS2. We perform Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) studies of MoS2, before and after gamma ray irradiation with varying doses and density functional theory (DFT) calculations. The Raman spectra and XPS results demonstrate that point defects dominate after the gamma irradiation of MoS2. DFT calculations elucidate the electronic properties of MoS2 before and after irradiation. Our work makes several contributions to the field of 2D materials research. First, our study of the electronic density of states and the electronic properties of a MoS2 monolayer irradiated by gamma rays sheds light on the properties of a MoS2 monolayer under gamma irradiation. Second, our study confirms that point defects are formed as a result of gamma irradiation. And third, our DFT calculations qualitatively suggest that the conductivity of the MoS2 monolayer may increase after gamma irradiation due to the creation of additional defect states.

Surface Plasmon Resonance (SPR) Sensor for Cancer Biomarker Detection.

A biomarker is a physiological observable marker that acts as a stand-in and, in the best-case scenario, forecasts a clinically significant outcome. Diagnostic biomarkers are more convenient and cost-effective than directly measuring the ultimate clinical outcome. Cancer is among the most prominent global health problems and a major cause of morbidity and death globally. Therefore, cancer biomarker assays that are trustworthy, consistent, precise, and verified are desperately needed. Biomarker-based tumor detection holds a lot of promise for improving disease knowledge at the molecular scale and early detection and surveillance. In contrast to conventional approaches, surface plasmon resonance (SPR) allows for the quick and less invasive screening of a variety of circulating indicators, such as circulating tumor DNA (ctDNA), microRNA (miRNA), circulating tumor cells (CTCs), lipids, and proteins. With several advantages, the SPR technique is a particularly beneficial choice for the point-of-care identification of biomarkers. As a result, it enables the timely detection of tumor markers, which could be used to track cancer development and suppress the relapse of malignant tumors. This review emphasizes advancements in SPR biosensing technologies for cancer detection.

Biomarkers in Cancer Detection, Diagnosis, and Prognosis.

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Hyperspectral dark field optical microscopy for orientational imaging of a single plasmonic nanocube using a physics-based learning method.

Rotational dynamics at the molecular level could provide additional data regarding protein diffusion and cytoskeleton formation at the cellular level. Due to the isotropic emission pattern of fluorescence molecules, it is challenging to extract rotational information from them during imaging. Metal nanoparticles show a polarization-dependent response and could be used for sensing rotational motion. Nanoparticles as an orientation sensing probe offer bio-compatibility and robustness against photo-blinking and photo-bleaching compared to conventional fluorescent molecules. Previously, asymmetric geometrical structures such as nanorods have been used for orientational imaging. Here, we show orientational imaging of symmetric geometrical structures such as 100 nm isolated silver nanocubes by coupling a hyperspectral detector and a focused ion beam (FIB)-fabricated correlating substrate. More than 100 nanocubes are analyzed to confirm spectral shifts in the scattering spectra due to variations in the orientation of the nanocubes with respect to the incoming light. Results are further validated using finite-difference time-domain simulations. Our observations suggest a novel strategy for high-throughput orientation imaging of nanoparticles.

Labelfree mapping and profiling of altered lipid homeostasis in the rat hippocampus after traumatic stress: Role of oxidative homeostasis.

Oxidative and lipid homeostasis are altered by stress and trauma and post-traumatic stress disorder (PTSD) is associated with alterations to lipid species in plasma. Stress-induced alterations to lipid oxidative and homeostasis may exacerbate PTSD pathology, but few preclinical investigations of stress-induced lipidomic changes in the brain exist. Currently available techniques for the quantification of lipid species in biological samples require tissue extraction and are limited in their ability to retrieve spatial information. Raman imaging can overcome this limitation through the quantification of lipid species in situ in minimally processed tissue slices. Here, we utilized a predator exposure and psychosocial stress (PE/PSS) model of traumatic stress to standardize Raman imaging of lipid species in the hippocampus using LC-MS based lipidomics and these data were confirmed with qRT-PCR measures of mRNA expression of relevant enzymes and transporters. Electron Paramagnetic Resonance Spectroscopy (EPR) was used to measure free radical production and an MDA assay to measure oxidized polyunsaturated fatty acids. We observed that PE/PSS is associated with increased cholesterol, altered lipid concentrations, increased free radical production and reduced oxidized polyunsaturated fats (PUFAs) in the hippocampus (HPC), indicating shifts in lipid and oxidative homeostasis in the HPC after traumatic stress.

A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering.

In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model's uncertainty is unacceptably high.

Probing the mutation independent interaction of DNA probes with SARS-CoV-2 variants through a combination of surface-enhanced Raman scattering and machine learning.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution has been characterized by the emergence of sets of mutations impacting the virus characteristics, such as transmissibility and antigenicity, presumably in response to the changing immune profile of the human population. The presence of mutations in the SARS-CoV-2 virus can potentially impact therapeutic and diagnostic test performances. We design and develop here a unique set of DNA probes i.e., antisense oligonucleotides (ASOs) which can interact with genetic sequences of the virus irrespective of its ongoing mutations. The probes, developed herein, target a specific segment of the nucleocapsid phosphoprotein (N) gene of SARS-CoV-2 with high binding efficiency which do not mutate among the known variants. Further probing into the interaction profile of the ASOs reveals that the ASO-RNA hybridization remains unaltered even for a hypothetical single point mutation at the target RNA site and diminished only in case of the hypothetical double or triple point mutations. The mechanism of interaction among the ASOs and SARS-CoV-2 RNA is then explored with a combination of surface-enhanced Raman scattering (SERS) and machine learning techniques. It has been observed that the technique, described herein, could efficiently discriminate between clinically positive and negative samples with ∼100% sensitivity and ∼90% specificity up to 63 copies/mL of SARS-CoV-2 RNA concentration. Thus, this study establishes N gene targeted ASOs as the fundamental machinery to efficiently detect all the current SARS-CoV-2 variants regardless of their mutations.

Rapid and Direct Perfluorooctanoic Acid Sensing with Selective Ionomer Coatings on Screen-Printed Electrodes under Environmentally Relevant Concentrations.

Per- and polyfluoroalkyl substances (PFASs) pose a significant health threat to humans at trace levels. Because of its ubiquity across the globe, there have been intense efforts to rapidly quantify PFASs in the environment while also mitigating their release. This work reports an electrochemical sensor with a selective perfluorinated anion exchange ionomer (PFAEI) coating for direct sensing of perfluorooctanoic acid (PFOA)-a type of PFAS. Notably, the sensor operates without the need of redox probes and has a limit of detection around 6.51 ± 0.2 ppb (15 nM) in buffered deionized water and drinking water. By testing the sensor with different ionomer electrode coatings, it was inferred that the PFAEI favors PFOA anions over other competing anions in solution through a combination of electrostatic and van der Waal interactions.

Multimodal Label-Free Monitoring of Adipogenic Stem Cell Differentiation Using Endogenous Optical Biomarkers.

Stem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping-based machine learning analysis is reported to distinguish differentiating human adipose-derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single-cell resolution. The label-free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton-based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix-assisted laser desorption/ionization-mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label-free identification of stem cell differentiation with high spatial and chemical resolution.

Mmp12 Is Upregulated by in utero Second-Hand Smoke Exposures and Is a Key Factor Contributing to Aggravated Lung Responses in Adult Emphysema, Asthma, and Lung Cancer Mouse Models.

Matrix metalloproteinase-12 (Mmp12) is upregulated by cigarette smoke (CS) and plays a critical role in extracellular matrix remodeling, a key mechanism involved in physiological repair processes, and in the pathogenesis of emphysema, asthma, and lung cancer. While cigarette smoking is associated with the development of chronic obstructive pulmonary diseases (COPD) and lung cancer, in utero exposures to CS and second-hand smoke (SHS) are associated with asthma development in the offspring. SHS is an indoor air pollutant that causes known adverse health effects; however, the mechanisms by which in utero SHS exposures predispose to adult lung diseases, including COPD, asthma, and lung cancer, are poorly understood. In this study, we tested the hypothesis that in utero SHS exposure aggravates adult-induced emphysema, asthma, and lung cancer. Methods: Pregnant BALB/c mice were exposed from gestational days 6-19 to either 3 or 10mg/m3 of SHS or filtered air. At 10, 11, 16, or 17weeks of age, female offspring were treated with either saline for controls, elastase to induce emphysema, house-dust mite (HDM) to initiate asthma, or urethane to promote lung cancer. At sacrifice, specific disease-related lung responses including lung function, inflammation, gene, and protein expression were assessed. Results: In the elastase-induced emphysema model, in utero SHS-exposed mice had significantly enlarged airspaces and up-regulated expression of Mmp12 (10.3-fold compared to air-elastase controls). In the HDM-induced asthma model, in utero exposures to SHS produced eosinophilic lung inflammation and potentiated Mmp12 gene expression (5.7-fold compared to air-HDM controls). In the lung cancer model, in utero exposures to SHS significantly increased the number of intrapulmonary metastases at 58weeks of age and up-regulated Mmp12 (9.3-fold compared to air-urethane controls). In all lung disease models, Mmp12 upregulation was supported at the protein level. Conclusion: Our findings revealed that in utero SHS exposures exacerbate lung responses to adult-induced emphysema, asthma, and lung cancer. Our data show that MMP12 is up-regulated at the gene and protein levels in three distinct adult lung disease models following in utero SHS exposures, suggesting that MMP12 is central to in utero SHS-aggravated lung responses.

Printed Electrode for Measuring Phosphate in Environmental Water.

Phosphate is a major nonpoint source pollutant in both the Louisiana local streams as well as in the Gulf of Mexico coastal waters. Phosphates from agricultural run-off have contributed to the eutrophication of global surface waters. Phosphate environmental dissemination and eutrophication problems are not yet well understood. Thus, this study aimed to monitor phosphate in the local watershed to help identify potential hot spots in the local community (Mississippi River, Louisiana) that may contribute to nutrient loading downstream (in the Gulf of Mexico). An electrochemical method using a physical vapor deposited cobalt microelectrode was utilized for phosphate detection using cyclic voltammetry and amperometry. The testing results were utilized to evaluate the phosphate distribution in river water and characterize the performance of the microsensor. Various characterizations, including the limit of detection, sensitivity, and reliability, were conducted by measuring the effect of interferences, including dissolved oxygen, pH, and common ions. The electrochemical sensor performance was validated by comparing the results with the standard colorimetry phosphate detection method. X-ray photoelectron spectroscopy (XPS) measurements were performed to understand the phosphate sensing mechanism on the cobalt electrode. This proof-of-concept sensor chip could be utilized for on-field monitoring using a portable, hand-held potentiostat.

Characterization of fibrillar collagen isoforms in infarcted mouse hearts using second harmonic generation imaging.

We utilized collagen specific second harmonic generation (SHG) signatures coupled with correlative immunofluorescence imaging techniques to characterize collagen structural isoforms (type I and type III) in a murine model of myocardial infarction (MI). Tissue samples were imaged over a four week period using SHG, transmitted light microscopy and immunofluorescence imaging using fluorescently-labeled collagen antibodies. The post-mortem cardiac tissue imaging using SHG demonstrated a progressive increase in collagen deposition in the left ventricle (LV) post-MI. We were able to monitor structural morphology and LV remodeling parameters in terms of extent of LV dilation, stiffness and fiber dimensions in the infarcted myocardium.

Label-free lipidome study of paraventricular thalamic nucleus (PVT) of rat brain with post-traumatic stress injury by Raman imaging.

Post-traumatic stress disorder (PTSD) is a widespread psychiatric injury that develops serious life-threatening symptoms like substance abuse, severe depression, cognitive impairments, and persistent anxiety. However, the mechanisms of post-traumatic stress injury in brain are poorly understood due to the lack of practical methods to reveal biochemical alterations in various brain regions affected by this type of injury. Here, we introduce a novel method that provides quantitative results from Raman maps in the paraventricular nucleus of the thalamus (PVT) region. By means of this approach, we have shown a lipidome comparison in PVT regions of control and PTSD rat brains. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry was also employed for validation of the Raman results. Lipid alterations can reveal invaluable information regarding the PTSD mechanisms in affected regions of brain. We have showed that the concentration of cholesterol, cholesteryl palmitate, phosphatidylinositol, phosphatidylserine, phosphatidylethanolamine, sphingomyelin, ganglioside, glyceryl tripalmitate and sulfatide changes in the PVT region of PTSD compared to control rats. A higher concentration of cholesterol suggests a higher level of corticosterone in the brain. Moreover, concentration changes of phospholipids and sphingolipids suggest the alteration of phospholipase A2 (PLA2) which is associated with inflammatory processes in the brain. Our results have broadened the understanding of biomolecular mechanisms for PTSD in the PVT region of the brain. This is the first report regarding the application of Raman spectroscopy for PTSD studies. This method has a wide spectrum of applications and can be applied to various other brain related disorders or other regions of the brain.

Dark-field hyperspectral imaging for label free detection of nano-bio-materials.

Nanomaterials are playing an increasingly important role in cancer diagnosis and treatment. Nanoparticle (NP)-based technologies have been utilized for targeted drug delivery during chemotherapies, photodynamic therapy, and immunotherapy. Another active area of research is the toxicity studies of these nanomaterials to understand the cellular uptake and transport of these materials in cells, tissues, and environment. Traditional techniques such as transmission electron microscopy, and mass spectrometry to analyze NP-based cellular transport or toxicity effect are expensive, require extensive sample preparation, and are low-throughput. Dark-field hyperspectral imaging (DF-HSI), an integration of spectroscopy and microscopy/imaging, provides the ability to investigate cellular transport of these NPs and to quantify the distribution of them within bio-materials. DF-HSI also offers versatility in non-invasively monitoring microorganisms, single cell, and proteins. DF-HSI is a low-cost, label-free technique that is minimally invasive and is a viable choice for obtaining high-throughput quantitative molecular analyses. Multimodal imaging modalities such as Fourier transform infrared and Raman spectroscopy are also being integrated with HSI systems to enable chemical imaging of the samples. HSI technology is being applied in surgeries to obtain molecular information about the tissues in real-time. This article provides brief overview of fundamental principles of DF-HSI and its application for nanomaterials, protein-detection, single-cell analysis, microbiology, surgical procedures along with technical challenges and future integrative approach with other imaging and measurement modalities. This article is categorized under: Diagnostic Tools > in vitro Nanoparticle-Based Sensing Diagnostic Tools > in vivo Nanodiagnostics and Imaging Implantable Materials and Surgical Technologies > Nanoscale Tools and Techniques in Surgery.

Nanomaterial-Based CO2 Sensors.

The detection of carbon dioxide (CO2) is critical for environmental monitoring, chemical safety control, and many industrial applications. The manifold application fields as well as the huge range of CO2 concentration to be measured make CO2 sensing a challenging task. Thus, the ability to reliably and quantitatively detect carbon dioxide requires vastly improved materials and approaches that can work under different environmental conditions. Due to their unique favorable chemical, optical, physical, and electrical properties, nanomaterials are considered state-of-the-art sensing materials. This mini-review documents the advancement of nanomaterial-based CO2 sensors in the last two decades and discusses their strengths, weaknesses, and major applications. The use of nanomaterials for CO2 sensing offers several improvements in terms of selectivity, sensitivity, response time, and detection, demonstrating the advantage of using nanomaterials for developing high-performance CO2 sensors. Anticipated future trends in the area of nanomaterial-based CO2 sensors are also discussed in light of the existing limitations.

Optical Identification of Middle Ear Infection.

Ear infection is one of the most commonly occurring inflammation diseases in the world, especially for children. Almost every child encounters at least one episode of ear infection before he/she reaches the age of seven. The typical treatment currently followed by physicians is visual inspection and antibiotic prescription. In most cases, a lack of improper treatment results in severe bacterial infection. Therefore, it is necessary to design and explore advanced practices for effective diagnosis. In this review paper, we present the various types of ear infection and the related pathogens responsible for middle ear infection. We outline the conventional techniques along with clinical trials using those techniques to detect ear infections. Further, we highlight the need for emerging techniques to reduce ear infection complications. Finally, we emphasize the utility of Raman spectroscopy as a prospective non-invasive technique for the identification of middle ear infection.

Ultrasensitive Three-Dimensional Orientation Imaging of Single Molecules on Plasmonic Nanohole Arrays Using Second Harmonic Generation.

Recently, fluorescence-based super-resolution techniques such as stimulated emission depletion (STED) and stochastic optical reconstruction microscopy (STORM) have been developed to achieve near molecular-scale resolution. However, such a super-resolution technique for nonlinear label-free microscopy based on second harmonic generation (SHG) is lacking. Since SHG is label-free and does not involve real-energy level transitions, fluorescence-based super-resolution techniques such as STED cannot be applied to improve the resolution. In addition, due to the coherent and non-isotropic emission nature of SHG, single-molecule localization techniques based on isotropic emission of fluorescent molecule such as STORM will not be appropriate. Single molecule SHG microscopy is largely hindered due to the very weak nonlinear optical scattering cross sections of SHG scattering processes. Thus, enhancing SHG using plasmonic nanostructures and nanoantennas has recently gained much attention owing to the potential of various nanoscale geometries to tightly confine electromagnetic fields into small volumes. This confinement provides substantial enhancement of electromagnetic field in nanoscale regions of interest, which can significantly boost the nonlinear signal produced by molecules located in the plasmonic hotspots. However, to date, plasmon-enhanced SHG has been primarily applied for the measurement of bulk properties of the materials/molecules, and single molecule SHG imaging along with its orientation information has not been realized yet. Herein, we achieved simultaneous visualization and three-dimensional (3D) orientation imaging of individual rhodamine 6G (R6G) molecules in the presence of plasmonic silver nanohole arrays. SHG and two-photon fluorescence microscopy experiments together with finite-difference time-domain (FDTD) simulations revealed a ∼106-fold nonlinear enhancement factor at the hot spots on the plasmonic silver nanohole substrate, enabling detection of single molecules using SHG. The position and 3D orientation of R6G molecules were determined using the template matching algorithm by comparing the experimental data with the calculated dipole emission images. These findings could enable SHG-based single molecule detection and orientation imaging of molecules which could lead to a wide range of applications from nanophotonics to super-resolution SHG imaging of biological cells and tissues.