Tissue Raman spectroscopy for the study of radiation damage: brain irradiation of mice

Cycloalkane degradation by an uncultivated novel genus of Gammaproteobacteria derived from China's marginal seas

The consumption of cycloalkanes is prevalent in low-temperature marine environments, likely influenced by psychrophilic microorganisms. Despite their significance, the primary active species responsible for marine cycloalkane degradation remain largely unidentified due to cultivation challenges. In this study, we provide compelling evidence indicating that the uncultured genus C1-B045 of Gammaproteobacteria is a pivotal participant in cycloalkane decomposition within China's marginal seas. Notably, the relative abundance of C1-B045 surged from 15.9% in the methylcyclohexane (MCH)-consuming starter culture to as high as 97.5% in MCH-utilizing extinction cultures following successive dilution-to-extinction and incubation cycles. We used stable isotope probing, Raman-activated gravity-driven encapsulation, and 16 S rRNA gene sequencing to link cycloalkane-metabolizing phenotype to genotype at the single-cell level. By annotating key enzymes (e.g., alkane monooxygenase, cyclohexanone monooxygenase, and 6-hexanolactone hydrolase) involved in MCH metabolism within C1-B045's representative metagenome-assembled genome, we developed a putative MCH degradation pathway.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Human tissue-resident peritoneal macrophages reveal resistance towards oxidative cell stress induced by non-invasive physical plasma

In the context of multimodal treatments for abdominal cancer, including procedures such as cytoreductive surgery and intraperitoneal chemotherapy, recurrence rates remain high, and long-term survival benefits are uncertain due to post-operative complications. Notably, treatment-limiting side effects often arise from an uncontrolled activation of the immune system, particularly peritoneally localized macrophages, leading to massive cytokine secretion and phenotype changes. Exploring alternatives, an increasing number of studies investigated the potential of plasma-activated liquids (PAL) for adjuvant peritoneal cancer treatment, aiming to mitigate side effects, preserve healthy tissue, and reduce cytotoxicity towards non-cancer cells. To assess the non-toxicity of PAL, we isolated primary human macrophages from the peritoneum and subjected them to PAL exposure. Employing an extensive methodological spectrum, including flow cytometry, Raman microspectroscopy, and DigiWest protein analysis, we observed a pronounced resistance of macrophages towards PAL. This resistance was characterized by an upregulation of proliferation and anti-oxidative pathways, countering PAL-derived oxidative stress-induced cell death. The observed cellular effects of PAL treatment on human tissue-resident peritoneal macrophages unveil a potential avenue for PAL-derived immunomodulatory effects within the human peritoneal cavity. Our findings contribute to understanding the intricate interplay between PAL and macrophages, shedding light on the promising prospects for PAL in the adjuvant treatment of peritoneal cancer.

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm-1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm ([Formula: see text]) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.

Use of Raman spectroscopy to study rat lung tissues for distinguishing asphyxia from sudden cardiac death

Determining asphyxia as the cause of death is crucial but is based on an exclusive strategy because it lacks sensitive and specific morphological characteristics in forensic practice. In some cases where the deceased has underlying heart disease, differentiation between asphyxia and sudden cardiac death (SCD) as the primary cause of death can be challenging. Herein, Raman spectroscopy was employed to detect pulmonary biochemical differences to discriminate asphyxia from SCD in rat models. Thirty-two rats were used to build asphyxia and SCD models, with lung samples collected immediately or 24 h after death. Twenty Raman spectra were collected for each lung sample, and 640 spectra were obtained for further data preprocessing and analysis. The results showed that different biochemical alterations existed in the lung tissues of the rats that died from asphyxia and SCD and could be used to distinguish between the two causes of death. Moreover, we screened and used 8 of the 11 main differential spectral features that maintained their significant differences at 24 h after death to successfully determine the cause of death, even with decomposition and autolysis. Eventually, seven prevalent machine learning classification algorithms were employed to establish classification models, among which the support vector machine exhibited the best performance, with an area under the curve value of 0.9851 in external validation. This study shows the promise of Raman spectroscopy combined with machine learning algorithms to investigate differential biochemical alterations originating from different deaths to aid determining the cause of death in forensic practice.

Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy

Periodontitis is a chronic inflammation of the periodontium caused by a persistent bacterial infection, resulting in destruction of the supporting structures of teeth. Analysis of microbial composition in saliva can inform periodontal status. Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), and Streptococcus mutans (Sm) are among reported periodontal pathogens, and were used as model systems in this study. Our atomic force microscopic (AFM) study revealed that these pathogens are biological nanorods with dimensions of 0.6-1.1 μm in length and 500-700 nm in width. Current bacterial detection methods often involve complex preparation steps and require labeled reporting motifs. Employing surface-enhanced Raman spectroscopy (SERS), we revealed cell-type specific Raman signatures of these pathogens for label-free detection. It overcame the complexity associated with spectral overlaps among different bacterial species, relying on high signal-to-noise ratio (SNR) spectra carefully collected from pure species samples. To enable simple, rapid, and multiplexed detection, we harnessed advanced machine learning techniques to establish predictive models based on a large set of raw spectra of each bacterial species and their mixtures. Using these models, given a raw spectrum collected from a bacterial suspension, simultaneous identification of all three species in the test sample was achieved at 95.6 % accuracy. This sensing modality can be applied to multiplex detection of a broader range and a larger set of periodontal pathogens, paving the way for hassle-free detection of oral bacteria in saliva with little to no sample preparation.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks

IMPORTANCE

The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.

Precise diagnosis of breast phyllodes tumors using Raman spectroscopy: Biochemical fingerprint, tumor metabolism and possible mechanism

BACKGROUND

Breast fibroadenomas and phyllodes tumors are both fibroepithelial tumors with comparable histological characteristics. However, rapid and precise differential diagnosis is a tough point in clinical pathology. Given the tendency of phyllodes tumors to recur, the difficulty in differential diagnosis with fibroadenomas leads to the difficulty in optimal management for these patients.

METHOD

In this study, we used Raman spectroscopy to differentiate phyllodes tumors from breast fibroadenomas based on the biochemical and metabolic composition and develop a classification model. The model was validated by 5-fold cross-validation in the training set and tested in an independent test set. The potential metabolic differences between the two types of tumors observed in Raman spectroscopy were confirmed by targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

RESULTS

A total of 204 patients with formalin-fixed paraffin-embedded (FFPE) tissue samples, including 100 fibroadenomas and 104 phyllodes tumors were recruited from April 2014 to August 2021. All patients were randomly divided into the training cohort (n = 153) and the test cohort (n = 51). The Raman classification model could differentiate phyllodes tumor versus fibroadenoma with cross-validation accuracy, sensitivity, precision, and area under curve (AUC) of 85.58 % ± 1.77 %, 83.82 % ± 1.01 %, 87.65 % ± 4.22 %, and 93.18 % ± 1.98 %, respectively. When tested in the independent test set, it performed well with the test accuracy, sensitivity, specificity, and AUC of 83.50 %, 86.54 %, 80.39 %, and 90.71 %. Furthermore, the AUC was significantly higher for the Raman model than that for ultrasound (P = 0.0017) and frozen section diagnosis (P < 0.0001). When it came to much more difficult diagnosis between fibroadenoma and benign or small-size phyllodes tumor for pathological examination, the Raman model was capable of differentiating with AUC up to 97.45 % and 95.61 %, respectively. On the other hand, targeted metabolomic analysis, based on fresh-frozen tissue samples, confirmed the differential metabolites (including thymine, dihydrothymine, trans-4-hydroxy-l-proline, etc.) identified from Raman spectra between phyllodes tumor and fibroadenoma.

SIGNIFICANCE AND NOVELTY

In this study, we obtained the molecular information map of breast phyllodes tumors provided by Raman spectroscopy for the first time. We identified a novel Raman fingerprint signature with the potential to precisely characterize and distinguish phyllodes tumors from fibroadenoma as a quick and accurate diagnostic tool. Raman spectroscopy is expected to further guide the precise diagnosis and optimal treatment of breast fibroepithelial tumors in the future.

Surface-enhanced Raman spectroscopy for the characterization of different anatomical subtypes of oral cavity cancer

BACKGROUND

The prognosis for oral cancer patients is still very poor worldwide. Early detection and treatment therapy remain the key issue to be addressed for improved patient survival. The characteristic Raman spectral features associated with the biochemical changes in the blood serum samples can be used for the diagnosis of diseases, particularly for oral cancer. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for non-invasive and early detection of oral cancer by analyzing molecular changes in body fluids.

OBJECTIVES

To detect oral cavity anatomical subsites (buccal mucosa, cheek, hard palate, lips, mandible, maxilla, tongue and tonsillar region) cancers by using blood serum samples, SERS with principal component analysis is used.

MATERIAL AND METHOD

SERS is employed with silver nanoparticles for the analysis and detection of oral cancer serum samples by comparing with healthy serum samples. SERS spectra are recorded by Raman instrument and preprocessed using the statistical tool. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) are used to discriminate between oral cancer serum samples and control serum samples.

RESULTS

Some major SERS peaks are observed at 1136 cm^-1 (Phospholipids) and 1006 cm^-1 (Phenylalanine) remain higher in intensities for oral cancer spectra as compared to healthy spectra. The peak at 1241 cm^-1 (amide III) is observed only in oral cancer serum samples while absent in healthy serum samples. Higher protein and DNA contents were detected in SERS mean spectra of oral cancer. Moreover, PCA is used to identify the biochemical differences in the form of SERS features which is used to differentiate between oral cancer and healthy blood serum samples, while PLS-DA is used to build differentiation model of oral cancer serum samples and healthy control serum samples. PLS-DA provides successful differentiation with 94% specificity and 95.5% sensitivity.

CONCLUSIONS

SERS can be used for the diagnosis of oral cancer and to identify metabolic changes that occur during disease development.

Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus.

OBJECTIVES

The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA).

METHODOLOGY

Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS.

RESULTS

HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy.

CONCLUSIONS

Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.

Raman microspectroscopy identifies fibrotic tissues in collagen-related disorders via deconvoluted collagen type I spectra

Fibrosis is a consequence of the pathological remodeling of extracellular matrix (ECM) structures in the connective tissue of an organ. It is often caused by chronic inflammation, which over time, progressively leads to an excess deposition of collagen type I (COL I) that replaces healthy tissue structures, in many cases leaving a stiff scar. Increasing fibrosis can lead to organ failure and death; therefore, developing methods that potentially allow real-time monitoring of early onset or progression of fibrosis are highly valuable. In this study, the ECM structures of diseased and healthy human tissue from multiple organs were investigated for the presence of fibrosis using routine histology and marker-independent Raman microspectroscopy and Raman imaging. Spectral deconvolution of COL I Raman spectra allowed the discrimination of fibrotic and non-fibrotic COL I fibers. Statistically significant differences were identified in the amide I region of the spectral subpeak at 1608 cm^-1, which was deemed to be representative for structural changes in COL I fibers in all examined fibrotic tissues. Raman spectroscopy-based methods in combination with this newly discovered spectroscopic biomarker potentially offer a diagnostic approach to non-invasively track and monitor the progression of fibrosis. STATEMENT OF SIGNIFICANCE: Current diagnosis of fibrosis still relies on histopathological examination with invasive biopsy procedures. Although, several non-invasive imaging techniques such as positron emission tomography, single-photon emission computed tomography and second harmonic generation are gradually employed in preclinical or clinical studies, these techniques are limited in spatial resolution and the morphological interpretation highly relies on individual experience and knowledge. In this study, we propose a non-destructive technique, Raman microspectroscopy, to discriminate fibrotic changes of collagen type I based on a molecular biomarker. The changes of the secondary structure of collagen type I can be identified by spectral deconvolution, which potentially can provide an automatic diagnosis for fibrotic tissues in the clinical applicaion.

Coupling annealed silver nanoparticles with a porous silicon Bragg mirror SERS substrate and machine learning for rapid non-invasive disease diagnosis

Ag₂O-Ag-porous silicon Bragg mirror (PSB) composite SERS substrates were successfully synthesized by using a combination of electrochemical and thermochemical methods. Test results showed that the SERS signal increased and decreased as the annealing temperature used for the substrate increased, where the most intense SERS signal was obtained using a substrate annealed at 300 °C. Stability test results showed substantial enhancement of the SERS signal intensity of the Ag₂O-Ag-PSB composite one month after preparation compared with that of conventional Ag-PSB. We conclude that Ag₂O nanoshells play an essential role in SERS signal enhancement. Ag₂O prevents natural oxidation of Ag nanoparticles (AgNPs) and has a solid localized surface plasmon resonance (LSPR). SERS signal enhancement was tested using this substrate for serum from patients with Sjögren's syndrome (SS) and Diabetic nephropathy (DN), as well as from healthy controls (HC). SERS feature extraction was performed using principal component analysis (PCA). The extracted features were analyzed by a support vector machine (SVM) algorithm. Finally, a rapid screening model for SS and HC, as well as DN and HC, was developed and used to perform controlled experiments. The results showed that the diagnostic accuracy, sensitivity and selectivity for SERS technology combined with machine learning algorithms reached 90.7%, 93.4% and 86.7% for SS/HC and 89.3%, 95.6% and 80% for DN/HC, respectively. The results of this study show that the composite substrate has excellent potential to be developed into a commercially available SERS chip for medical testing.

Bioactivation of an orthodontic wire using multifunctional nanomaterials to prevent plaque accumulation

Controlling the growth of biofilm on orthodontic material has become a difficult challenge in modern dentistry. The antibacterial efficacy of currently used orthodontic material becomes limited due to the higher affinity of oral microbial flora for plaque formation on the material surface. Thus it is crutial to device an efficient strategy to prevent plaque buildup caused by pathogenic microbiota. In this work, we have fabricated a bioactive orthodontic wire using titanium nanoparticles (TiO₂NPs) and silver nanoparticles (AgNPs). AgNPs were synthesized from the extracts of Ocimum sanctum, Ocimum tenuiflorum, Solanum surattense, and Syzygium aromaticum, while the TiO₂NPs were synthesized by the Sol-Gel method. The nanoparticles were characterized by various biophysical techniques. The surface of the dental wire was molded by functionalizing these AgNPs followed by an additional coating of TiO₂NPs. Functionalized dental wires were found to counteract the formation of tenacious intraoral biofilm, and showed an enhanced anti-bacterial effect against Multi-Drug Resistant (MDR) bacteria isolated from patients with various dental ailments. Data revealed that such surface coating counteracts the bacterial pathogens by inducing the leakage of Ag ions which eventually disrupts the cell membrane as confirmed from TEM micrographs. The results offer a significant opportunity for innovations in developing nanoparticle-based formulations to modify or fabricate an effective orthodontic material.

Metabolic Reprogramming in Colon Cancer Cells Persistently Infected with Newcastle Disease Virus

Newcastle disease virus (NDV) is an oncolytic agent against various types of mammalian cancers. As with all cancer therapies, the development of cancer resistance, both innate and acquired, is becoming a challenge. In this study, we investigated persistently NDV-infected Caco-2 colon cancer cells, designated as virus-resistant (VR) Caco-2 cells, which were then able to resist NDV-mediated oncolysis. We applied single-cell Raman spectroscopy, combined with deuterium isotope probing (Raman-DIP) techniques, to investigate the metabolic adaptations and dynamics in VR Caco-2 cells. A linear discriminant analysis (LDA) model demonstrated excellent performance in differentiating VR Caco-2 from Caco-2 cells at single-cell level. By comparing the metabolic profiles in a time-resolved manner, the de novo synthesis of proteins and lipids was found upregulated, along with decreased DNA synthesis in VR Caco-2. The results suggest that VR Caco-2 cells might reprogram their metabolism and divert energy from proliferation to protein synthesis and lipidic modulation. The ability to identify and characterise single resistant cells among a population of cancer cells would help develop a deeper understanding of the resistance mechanisms and better tactics for developing effective cancer treatment.

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

Decorin improves human pancreatic β-cell function and regulates ECM expression in vitro

Transplantation of islets of Langerhans is a promising alternative treatment strategy in severe cases of type 1 diabetes mellitus; however, the success rate is limited by the survival rate of the cells post-transplantation. Restoration of the native pancreatic niche during transplantation potentially can help to improve cell viability and function. Here, we assessed for the first time the regulatory role of the small leucine-rich proteoglycan decorin (DCN) in insulin secretion in human β-cells, and its impact on pancreatic extracellular matrix (ECM) protein expression in vitro. In depth analyses utilizing next-generation sequencing as well as Raman microspectroscopy and Raman imaging identified pathways related to glucose metabolism to be upregulated in DCN-treated cells, including oxidative phosphorylation within the mitochondria as well as proteins and lipids of the endoplasmic reticulum. We further showed the effectiveness of DCN in a transplantation setting by treating collagen type 1-encapsulated β-cell-containing pseudo-islets with DCN. Taken together, in this study, we demonstrate the potential of DCN to improve the function of insulin-secreting β-cells while reducing the expression of ECM proteins affiliated with fibrotic capsule formation, making DCN a highly promising therapeutic agent for islet transplantation.

Nanomechanical and Vibrational Signature of Chikungunya Viral Particles

Chikungunya virus (CHIKV) belongs to the genus Alphaviridae, with a single-stranded positive-sense RNA genome of 11.8 kbp encoding a polyprotein that generates both non-structural proteins and structural proteins. The virus is transmitted by the Aedes aegypti and A. albopictus mosquitoes, depending on the location. CHIKV infection leads to dengue-like musculoskeletal symptoms and has been responsible for several outbreaks worldwide since its discovery in 1952. Patients often experience fever, headache, muscle pain, joint swelling, and skin rashes. However, the ultrastructural and mechanical properties of CHIKV have not been fully characterized. Thus, this study aims to apply a physical approach to investigate CHIKV's ultrastructural morphology and mechanical properties, using atomic force microscopy and Raman spectroscopy as the main tools. Using nanomechanical assays of AFM and a gold nanoparticles substrate for Raman signal enhancement, we explored the conformational plasticity, morphology, vibrational signature, and nanomechanical properties of the chikungunya virus, providing new information on its ultrastructure at the nanoscale and offering a novel understanding of the virus' behavior upon mechanical disruptions besides its molecular composition.

Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

Surface-enhanced Raman spectroscopy for characterization of filtrate portions of blood serum samples of typhoid patients

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is explored to design a rapid screening method for the characterization and diagnosis of typhoid fever by employing filtrate fractions of blood serum samples obtained by centrifugal filtration with 50 KDa filters.

OBJECTIVES

The purpose of this study, to separate the filtrate portions of blood serum samples in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire the SERS spectral features of smaller proteins more effectively which are probably associated with typhoid disease. Disease caused by Salmonella typhi diagnose more effectively by using surface-enhanced Raman spectroscopy (SERS) and multivariate data analysis tools.

METHODS

SERS was used as a diagnostic tool for typhoid fever by comparison between healthy and diseased samples. For this purpose, all the samples were analyzed by comparing their SERS spectral features. Over the spectral range of 400-1800cm^-1, multivariate data analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) are applied to diagnose and differentiate different filtrate fractions of blood serum samples of patients of typhoid fever and healthy ones.

RESULTS

By comparing SERS spectra of healthy filtrate with that of filtrate of typhoid sample, the SERS spectral features associated with disease development are identified including PCA is found to be efficient for the qualitative differentiation of all of the samples analyzed. Moreover, PLS-DA successfully identified and classified healthy and typhoid positive blood serum samples with 97 % accuracy, 99 % specificity, 91 % sensitivity and 0.78 area under the receiver operating characteristic (AUROC) curve.

CONCLUSIONS

Surface enhanced Raman spectroscopy using silver nanoparticles SERS substrate, is found to be useful technique for the quick identification and evaluation of filtrate fractions of the blood serum samples of healthy and typhoid samples for disease diagnosis.

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.

Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning

Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it helps correct administration of antimicrobial treatment. The laborious procedure and low sensitivity results related to conventional GS have resulted in reluctance among clinicians. In this study, we integrate confocal Raman spectroscopy and machine learning techniques to distinguish Gram-negative (GN) or Gram-positive (GP) bacteria. A single-cell Raman database including seven most common clinical pathogens (three GP strains and four GN strains) was constructed. Machine learning algorithms including the support-vector machine (SVM), k-nearest neighbors' algorithm (k-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.

Surface-enhanced Raman spectroscopy (SERS) for monitoring colistin-resistant and susceptible E. coli strains

The emergence of drug-resistant bacteria is a precarious global health concern. In this study, surface-enhanced Raman spectroscopy (SERS) is used to characterize colistin-resistant and susceptible E. coli strains based on their distinguished SERS spectral features for the development of rapid and cost-effective detection and differentiation methods. For this purpose, three colistin-resistant and three colistin susceptible E. coli strains were analyzed by comparing their SERS spectral signatures. Moreover, multivariate data analysis techniques including Principal component analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were used to examine the SERS spectral data of colistin-resistant and susceptible strains. PCA technique was employed for differentiating colistin susceptible and resistant E.coli strains due to alteration in biochemical compositions of the bacterial cell. PLS-DA is employed on SERS spectral data sets for discrimination of these resistant and susceptible E. coli strains with 100% specificity, 100% accuracy, 99.8% sensitivity, and 86% area under receiver operating characteristics (AUROC) curve.

Rapid diagnosis of diabetes based on ResNet and Raman spectroscopy

Diabetes mellitus is a global public health problem, and the epidemic situation in China is particularly serious. The prevalence of the disease has been increasing in recent years, and the number of patients is the highest in the world. Diabetes has become another chronic non-communicable disease that seriously endangers the health of our people after cardiovascular and cerebrovascular diseases and tumors. In this study, urine sample data were collected from 37 diabetic patients and 37 healthy volunteers using Raman spectroscopy. The collected data were preprocessed using an adaptive iterative reweighted penalized least squares (airPLS) algorithm and a polynomial Savitzky-Golay smoothing algorithm. After extracting features using principal component analysis (PCA) dimensionality reduction algorithm, ResNet, support vector machine (SVM) and linear discriminant analysis (LDA) classification models were selected to classify and identify diabetic patients and healthy controls. The results show that ResNet has the best discrimination effect, and the average accuracy, recall and F1-score can reach 84.28%, 86.20% and 84.02% respectively after five cross-validations, and the area under the subject working characteristic (ROC) curve is 0.93. The experimental results show that the model established in this paper is simple to operate, highly accurate and has good reference value for rapid screening of diabetes.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Data-Driven Identification of Biomarkers for In Situ Monitoring of Drug Treatment in Bladder Cancer Organoids

Three-dimensional (3D) organoid culture recapitulating patient-specific histopathological and molecular diversity offers great promise for precision medicine in cancer. In this study, we established label-free imaging procedures, including Raman microspectroscopy (RMS) and fluorescence lifetime imaging microscopy (FLIM), for in situ cellular analysis and metabolic monitoring of drug treatment efficacy. Primary tumor and urine specimens were utilized to generate bladder cancer organoids, which were further treated with various concentrations of pharmaceutical agents relevant for the treatment of bladder cancer (i.e., cisplatin, venetoclax). Direct cellular response upon drug treatment was monitored by RMS. Raman spectra of treated and untreated bladder cancer organoids were compared using multivariate data analysis to monitor the impact of drugs on subcellular structures such as nuclei and mitochondria based on shifts and intensity changes of specific molecular vibrations. The effects of different drugs on cell metabolism were assessed by the local autofluorophore environment of NADH and FAD, determined by multiexponential fitting of lifetime decays. Data-driven neural network and data validation analyses (k-means clustering) were performed to retrieve additional and non-biased biomarkers for the classification of drug-specific responsiveness. Together, FLIM and RMS allowed for non-invasive and molecular-sensitive monitoring of tumor-drug interactions, providing the potential to determine and optimize patient-specific treatment efficacy.

Surface-enhanced Raman spectroscopic analysis of the centrifugally filtered blood serum samples of the hepatitis C patients

BACKGROUND

Previously Raman spectroscopy technique is a use to analyze non-invasive disease related to body fluids.

OBJECTIVES

For the qualitative and quantitative analysis of HCV serum samples surface-enhanced Raman spectroscopy (SERS) based method is developed.

METHOD

Surface-enhanced Raman spectroscopy (SERS) technique is employed for analysis of filtrate portions of blood serum samples of hepatitis C virus (HCV) infected patients and healthy ones by using 50 kDa centrifugal filter device. The filtrate portions of the serum obtained in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire SERS spectral features of smaller proteins more effectively which are probably associated with Hepatitis C infection. Moreover, SERS spectral features of the filtrates of different level of viral load including low, medium and high viral loads are compared with SERS spectral features of the filtrate portions of healthy/control serum samples. SERS spectral data sets of different samples are further analyzed by using multivariate data analysis techniques such as principal component analysis (PCA) and partial least square regression (PLSR). Some SERS spectral features are solely observed in the filtrate portions of the serum samples of hepatitis C and their intensities are increased as the level of viral load increases and might be used for HCV diagnosis.

RESULTS

PCA was found helpful for differentiation of SERS spectral data sets of filtrate portions of the serum samples of hepatitis C and healthy persons. The PLSR model helped for the quantification of viral loads in the unknown serum samples with 99 % accuracy.

Atrial fibrillation designation with micro-Raman spectroscopy and scanning acoustic microscope

Atrial fibrillation (AF) is diagnosed with the electrocardiogram, which is the gold standard in clinics. However, sufficient arrhythmia monitoring takes a long time, and many of the tests are made in only a few seconds, which can lead arrhythmia to be missed. Here, we propose a combined method to detect the effects of AF on atrial tissue. We characterize tissues obtained from patients with or without AF by scanning acoustic microscopy (SAM) and by Raman spectroscopy (RS) to construct a mechano-chemical profile. We classify the Raman spectral measurements of the tissue samples with an unsupervised clustering method, k-means and compare their chemical properties. Besides, we utilize scanning acoustic microscopy to compare and determine differences in acoustic impedance maps of the groups. We compared the clinical outcomes with our findings using a neural network classification for Raman measurements and ANOVA for SAM measurements. Consequently, we show that the stiffness profiles of the tissues, corresponding to the patients with chronic AF, without AF or who experienced postoperative AF, are in agreement with the lipid-collagen profiles obtained by the Raman spectral characterization.

Cell Type-Specific Anti-Adhesion Properties of Peritoneal Cell Treatment with Plasma-Activated Media (PAM)

Postoperative abdominal adhesions are responsible for serious clinical disorders. Administration of plasma-activated media (PAM) to cell type-specific modulated proliferation and protein biosynthesis is a promising therapeutic strategy to prevent pathological cell responses in the context of wound healing disorders. We analyzed PAM as a therapeutic option based on cell type-specific anti-adhesive responses. Primary human peritoneal fibroblasts and mesothelial cells were isolated, characterized and exposed to different PAM dosages. Cell type-specific PAM effects on different cell components were identified by contact- and marker-independent Raman imaging, followed by thorough validation by specific molecular biological methods. The investigation revealed cell type-specific molecular responses after PAM treatment, including significant cell growth retardation in peritoneal fibroblasts due to transient DNA damage, cell cycle arrest and apoptosis. We identified a therapeutic dose window wherein specifically pro-adhesive peritoneal fibroblasts were targeted, whereas peritoneal mesothelial cells retained their anti-adhesive potential of epithelial wound closure. Finally, we demonstrate that PAM treatment of peritoneal fibroblasts reduced the expression and secretion of pro-adhesive cytokines and extracellular matrix proteins. Altogether, we provide insights into biochemical PAM mechanisms which lead to cell type-specific pro-therapeutic cell responses. This may open the door for the prevention of pro-adhesive clinical disorders.

Raman Spectroscopy as a Neuromonitoring Tool in Traumatic Brain Injury: A Systematic Review and Clinical Perspectives

Traumatic brain injury (TBI) is a significant global health problem, for which no disease-modifying therapeutics are currently available to improve survival and outcomes. Current neuromonitoring modalities are unable to reflect the complex and changing pathophysiological processes of the acute changes that occur after TBI. Raman spectroscopy (RS) is a powerful, label-free, optical tool which can provide detailed biochemical data in vivo. A systematic review of the literature is presented of available evidence for the use of RS in TBI. Seven research studies met the inclusion/exclusion criteria with all studies being performed in pre-clinical models. None of the studies reported the in vivo application of RS, with spectral acquisition performed ex vivo and one performed in vitro. Four further studies were included that related to the use of RS in analogous brain injury models, and a further five utilised RS in ex vivo biofluid studies for diagnosis or monitoring of TBI. RS is identified as a potential means to identify injury severity and metabolic dysfunction which may hold translational value. In relation to the available evidence, the translational potentials and barriers are discussed. This systematic review supports the further translational development of RS in TBI to fully ascertain its potential for enhancing patient care.

Ex vivo confocal Raman microspectroscopy of porcine dura mater supported by optical clearing

The effect of tissue optical clearing (TOC) to increase the probing depth and observe in-depth structure of the ex vivo porcine dura mater was studied by confocal Raman microspectroscopy (CRM). Raman intensities were significantly increased at the depth of 250 μm for all collagen bands after treatment with glycerol. The influence of glycerol on collagen hydration was also investigated. The results indicate that the process of TOC can be divided into three main steps. The first one is a fast process of tissue dehydration accompanied by collagen shrinkage while the second relatively slow process is related to the glycerol penetration into the interfibrillar space of collagen combined with swelling of tissue. The third step is collagen dissociation caused by the high concentration of glycerol. To the best of our knowledge, this study is the first example to introduce the TOC technique in assisting CRM of ex vivo dura mater in-depth probing.

Raman Research on Bleomycin-Induced DNA Strand Breaks and Repair Processes in Living Cells

Even several thousands of DNA lesions are induced in one cell within one day. DNA damage may lead to mutations, formation of chromosomal aberrations, or cellular death. A particularly cytotoxic type of DNA damage is single- and double-strand breaks (SSBs and DSBs, respectively). In this work, we followed DNA conformational transitions induced by the disruption of DNA backbone. Conformational changes of chromatin in living cells were induced by a bleomycin (BLM), an anticancer drug, which generates SSBs and DSBs. Raman micro-spectroscopy enabled to observe chemical changes at the level of single cell and to collect hyperspectral images of molecular structure and composition with sub-micrometer resolution. We applied multivariate data analysis methods to extract key information from registered data, particularly to probe DNA conformational changes. Applied methodology enabled to track conformational transition from B-DNA to A-DNA upon cellular response to BLM treatment. Additionally, increased expression of proteins within the cell nucleus resulting from the activation of repair processes was demonstrated. The ongoing DNA repair process under the BLM action was also confirmed with confocal laser scanning fluorescent microscopy.

Diagnosis of Lung Cancer by FTIR Spectroscopy Combined With Raman Spectroscopy Based on Data Fusion and Wavelet Transform

Lung cancer is a fatal tumor threatening human health. It is of great significance to explore a diagnostic method with wide application range, high specificity, and high sensitivity for the detection of lung cancer. In this study, data fusion and wavelet transform were used in combination with Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy to study the serum samples of patients with lung cancer and healthy people. The Raman spectra of serum samples can provide more biological information than the FTIR spectra of serum samples. After selecting the optimal wavelet parameters for wavelet threshold denoising (WTD) of spectral data, the partial least squares–discriminant analysis (PLS-DA) model showed 93.41% accuracy, 96.08% specificity, and 90% sensitivity for the fusion data processed by WTD in the prediction set. The results showed that the combination of FTIR spectroscopy and Raman spectroscopy based on data fusion and wavelet transform can effectively diagnose patients with lung cancer, and it is expected to be applied to clinical screening and diagnosis in the future.

Raman spectroscopic insight into osteoarthritic cartilage regeneration by mRNA therapeutics encoding cartilage-anabolic transcription factor Runx1

While joint arthroplasty remains nowadays the most popular option available to repair chronically degenerated osteoarthritic joints, possibilities are recently emerging for regeneration of damaged cartilage rather than its replacement with artificial biomaterials. This latter strategy could allow avoiding the quite intrusive surgical procedures associated with total joint replacement. Building upon this notion, we first apply Raman spectroscopy to characterize diseased cartilage in a mice model of instability-induced knee osteoarthritis (OA) upon medial collateral ligament (MCL) and medial meniscus (MM) transections. Then, we examine the same OA model after cartilage regeneration by means of messenger RNA (mRNA) delivery of a cartilage-anabolic runt-related transcription factor 1 (RUNX1). Raman spectroscopy is shown to substantiate at the molecular scale the therapeutic effect of the Runx1 mRNA cartilage regeneration approach. This study demonstrates how the Raman spectroscopic method could support and accelerate the development of new therapies for cartilage diseases.

A multivariate analysis of Multiple Myeloma subtype plasma cells

Trusted methods for identifying different Multiple Myeloma (MM) cells and their biological diversity due to their immunophenotypic variety are often little detailed and difficult to find in literature. In this work, we show that micro-Raman spectroscopy can be used to highlight if there is a certain degree of distinction or correlation between the MM subtype plasmacells in relation to the cluster of differentiation (CD45+/CD38+/CD138-) and (CD45-/CD38+/CD138+). After taking samples from the bone marrow of patients with Multiple Myeloma, the PCs were sorted by flow cytometry, selecting the most common CD of the disease, i.e. CD 45, CD38 and CD138. Some spectral differences are observed comparing the Raman spectra of the two set of samples investigated. To better define in which spectral regions there are greater differences and, therefore, to which biological contributions the changes refers, we also explored the principal component analysis (PCA) of the collected Raman data. The spectral variations between the different sorted cells have been highlighted by plotting loading vectors PC1 and PC2, which shows a net differentiation between the two set of cells. Ultimately, the differences shown by PCA have been associated with the spectral variations observed and explained in terms of changes of proteins and lipid contributions. Thus, the differentiation of Multiple Myeloma subtype plasma cells by confocal micro-Raman spectroscopy can be proposed as a diagnostic tool in the speeding up of cell identification, assessing the intracellular biochemical changes that take place in cancer cells.

Raman and Fluorescence Profiles Modifications of Muscular and Adipose Tissues Exposed to Low Energy X-ray Beams

This work aims to investigate changes induced by low-energy radiation in adipose and muscular tissues employing autofluorescence and Raman spectroscopic techniques. X-ray beams expositions with 25 and 35 kV at 0.11, 1.1, and 2.1 Gy radiation dose levels were applied. Changes in Raman line intensities at specific bands assigned to collagen, proteins, and lipids were observed. Autofluorescent analysis exhibit variations in the collagen and nicotinamide adenine dinucleotide emission (NADH), resulting from the structural modifications, variations on the reduced/oxidized fluorophores equilibrium followed by radiation exposure. Results show that Raman and fluorescence spectroscopy are suitable techniques to evaluate radiation effects on biomolecules even at low radiation doses and energies.

Biomolecular changes and subsequent time-dependent recovery in hippocampal tissue after experimental mild traumatic brain injury

Traumatic brain injury (TBI) is the main cause of disability and mortality in individuals under the age of 45 years. Elucidation of the molecular and structural alterations in brain tissue due to TBI is crucial to understand secondary and long-term effects after traumatic brain injury, and to develop and apply the correct therapies. In the current study, the molecular effects of TBI were investigated in rat brain at 24 h and 1 month after the injury to determine acute and chronic effects, respectively by Fourier transform infrared imaging. This study reports the time-dependent contextual and structural effects of TBI on hippocampal brain tissue. A mild form of TBI was induced in 11-week old male Sprague Dawley rats by weight drop. Band area and intensity ratios, band frequency and bandwidth values of specific spectral bands showed that TBI causes significant structural and contextual global changes including decrease in carbonyl content, unsaturated lipid content, lipid acyl chain length, membrane lipid order, total protein content, lipid/protein ratio, besides increase in membrane fluidity with an altered protein secondary structure and metabolic activity in hippocampus 24 h after injury. However, improvement and/or recovery effects in these parameters were observed at one month after TBI.

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer

This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2-94.6% accuracy, 89.8-91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600-1800 cm-1) and global loss of high wavenumber signal (2800-3200 cm-1) are particularly sensitive LRS warning signs of tumor. The stochastic nature of NNs made it possible to rapidly generate multiple models of target tissue classification and calculate the inherent error in the probabilistic estimates for each target.

Applicability of Raman spectroscopy on porcine parvovirus and porcine circovirus type 2 detection

Porcine parvovirus (PPV) is one of the major infectious causes of reproductive failure of swine. This disease is characterized by embryonic and fetal infection and death, responsible for important economic losses. PPV is also implicated as a trigger in the development of post-weaning multisystemic wasting syndrome (PMWS) caused by Porcine circovirus type 2 (PCV2). Their detection is PCR-based, which is quite sensitive and specific, but laborious, costly and time-demanding. Therefore, this study aimed to assess Raman spectroscopy (RS) as a diagnostic tool for PPV and PCV2 due to its label-free properties and unique ability to search and identify molecular fingerprints. Briefly, swine testis (ST) cells were inoculated with PPV or PCV2 and in vitro cultured (37 °C, 5% CO₂) for four days. Fixed cells were then submitted to RS investigation using a 633 nm laser. A total of 225 spectra centered at 1300 cm^-1 was obtained for each sample (5 spectra/cell; 15 cells/replicate; 3 replicates) of PPV-, PCV2-infected and uninfected (control) ST cells. Clear statistical discrimination between samples from both virus-infected cells was achieved with a Principal Component - Linear Discriminant Analysis (PCA-LDA) model, reaching sensitivity rates from 95.55% to 97.77%, respectively to PCV2- and PPV-infected cells. These results were then submitted to a Leave-One-Out (LOO) validation algorithm resulting in 99.97% of accuracy. Extensive band assignment was analyzed and compiled for better understanding of PPV and PCV2 virus-cell interaction, demonstrating that specific protein, lipids and DNA/RNA bands are the most important assignments related to discrimination of virus-infected from uninfected cells. In conclusion, these results represent promising bases for RS application on PCV2 and PPV detection for future diagnostic applications.

A Raman algorithm to estimate human age from protein structural variations in autopsy skin samples: a protein biological clock

The recent increase of the number of unidentified cadavers has become a serious problem throughout the world. As a simple and objective method for age estimation, we attempted to utilize Raman spectrometry for forensic identification. Raman spectroscopy is an optical-based vibrational spectroscopic technique that provides detailed information regarding a sample’s molecular composition and structures. Building upon our previous proof-of-concept study, we measured the Raman spectra of abdominal skin samples from 132 autopsy cases and the protein-folding intensity ratio, R_PF, defined as the ratio between the Raman signals from a random coil an α-helix. There was a strong negative correlation between age and R_PF with a Pearson correlation coefficient of r = 0.878. Four models, based on linear (R_PF), squared (R_PF²), sex, and R_PF by sex interaction terms, were examined. The results of cross validation suggested that the second model including linear and squared terms was the best model with the lowest root mean squared error (11.3 years of age) and the highest coefficient of determination (0.743). Our results indicate that the there was a high correlation between the age and R_PF and the Raman biological clock of protein folding can be used as a simple and objective forensic age estimation method for unidentified cadavers.

Development and Characterization of a Probe Device toward Intracranial Spectroscopy of Traumatic Brain Injury

Traumatic brain injury is a leading cause of mortality worldwide, often affecting individuals at their most economically active yet no primary disease-modifying interventions exist for their treatment. Real-time direct spectroscopic examination of the brain tissue within the context of traumatic brain injury has the potential to improve the understanding of injury heterogeneity and subtypes, better target management strategies and organ penetrance of pharmacological agents, identify novel targets for intervention, and allow a clearer understanding of fundamental biochemistry evolution. Here, a novel device is designed and engineered, delivering Raman spectroscopy-based measurements from the brain through clinically established cranial access techniques. Device prototyping is undertaken within the constraints imposed by the acquisition and site dimensions (standard intracranial access holes, probe’s dimensions), and an artificial skull anatomical model with cortical impact is developed. The device shows a good agreement with the data acquired via a standard commercial Raman, and the spectra measured are comparable in terms of quality and detectable bands to the established traumatic brain injury model. The developed proof-of-concept device demonstrates the feasibility for real-time optical brain spectroscopic interface while removing the noise of extracranial tissue and with further optimization and in vivo validation, such technology will be directly translatable for integration into currently available standards of neurological care.

Laparoscopic Peritoneal Wash Cytology-Derived Primary Human Mesothelial Cells for In Vitro Cell Culture and Simulation of Human Peritoneum

Peritoneal mucosa of mesothelial cells line the abdominal cavity, surround intestinal organs and the female reproductive organs and are responsible for immunological integrity, organ functionality and regeneration. Peritoneal diseases range from inflammation, adhesions, endometriosis, and cancer. Efficient technologies to isolate and cultivate healthy patient-derived mesothelial cells with maximal purity enable the generation of capable 2D and 3D as well as in vivo-like microfluidic cell culture models to investigate pathomechanisms and treatment strategies. Here, we describe a new and easily reproducible technique for the isolation and culture of primary human mesothelial cells from laparoscopic peritoneal wash cytology. We established a protocol containing multiple washing and centrifugation steps, followed by cell culture at the highest purity and over multiple passages. Isolated peritoneal mesothelial cells were characterized in detail, utilizing brightfield and immunofluorescence microscopy, flow cytometry as well as Raman microspectroscopy and multivariate data analysis. Thereby, cytokeratin expression enabled specific discrimination from primary peritoneal human fibroblasts. Raman microspectroscopy and imaging were used to study morphology and biochemical properties of primary mesothelial cell culture compared to cryo-fixed and cryo-sectioned peritoneal tissue.

Raman Microspectroscopic Investigation and Classification of Breast Cancer Pathological Characteristics

Breast cancer is one of the major cancers of women in the world. Despite significant progress in its treatment, an early diagnosis can effectively reduce its incidence rate and mortality. To improve the reliability of Raman-based tumor detection and analysis methods, we conducted an ex vivo study to unveil the compositional features of healthy control (HC), solid papillary carcinoma (SPC), mucinous carcinoma (MC), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) tissue samples. Following the identification of biological variations occurring as a result of cancer invasion, principal component analysis followed by linear discriminate analysis (PCA-LDA) algorithm were adopted to distinguish spectral variations among different breast tissue groups. The achieved results confirmed that after training, the constructed classification model combined with the leave-one-out cross-validation (LOOCV) method was able to distinguish the different breast tissue types with 100% overall accuracy. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.