Raman Spectroscopy Cell-based Biosensors

Raman and resonance Raman spectroscopic study of red blood cells in patients diagnosed with malaria

Raman spectroscopy was used to study erythrocytes collected from patients diagnosed with malaria at the University Hospital in Kraków and from healthy volunteers. A laser line with a wavelength of 442 nm was used to induce the Raman resonance of haem, while a laser with a wavelength of 785 nm was used for the normal Raman effect. The results were analysed using Principal Component Analysis. For the 442 nm laser line, analysis of the entire spectral range (3200 cm-1 to 300 cm-1) showed satisfactory separation of Raman spectra for healthy cells from infected cells, which was significantly improved in the 1500 cm-1-1200 cm-1 spectral range. For the 785 nm laser line, some separation was observed in each range studied, but the best results were achieved over the full spectral range. Plasmodium-derived nucleic acids and phosphodiester vibrations were observed at excitation lines of 442 nm and 785 nm, respectively.

Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification

Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data. Here, we introduced novel 2D image-like dual signal and component aggregated representations by restructuring Raman spectra and principal components, which enables spectroscopic DL for enhanced cell phenotype and signature identification. New ConvNet models DSCARNets significantly outperformed the state-of-the-art (SOTA) ML and DL models on six benchmark datasets, mostly with >2% improvement over the SOTA performance of 85-97% accuracies. DSCARNets also performed well on four additional datasets against SOTA models of extremely high performances (>98%) and two datasets without a published supervised phenotype classification model. Explainable DSCARNets identified Raman signatures consistent with experimental indications.

Isotope labeled 3D-Raman confocal imaging and atomic force microscopy study on epithelial cells interacting with the fungus Candida albicans

The human pathogenic fungus Candida albicans damages epithelial cells during superficial infections. Here we use three-dimensional-sequential-confocal Raman spectroscopic imaging and atomic force microscopy to investigate the interaction of C. albicans wild type cells, the secreted C. albicans peptide toxin candidalysin and mutant cells lacking candidalysin with epithelial cells. The candidalysin is responsible for epithelial cell damage and exhibits in its deuterated form an identifiable Raman signal in a frequency region distinct from the cellular frequency region. Vibration modes at 2100-2200 cm-1 attributed to carbon‑deuterium bending and at 477 cm-1, attributed to the nitrogen‑deuterium out-of-plane bending, found around the nucleus, can be assigned to deuterated candidalysin. Atomic force microscopy visualized 100 nm deep lesions on the cell and force-distance curves indicate the higher adhesion on pore surrounding after incubation with candidalysin. Candidalysin targets the plasma membrane, but is also found inside of the cytosol of epithelial cells during C. albicans infection.

Correlative Raman Imaging: Development and Cancer Applications

Despite extensive research efforts, cancer continues to stand as one of the leading causes of death on a global scale. To gain profound insights into the intricate mechanisms underlying cancer onset and progression, it is imperative to possess methodologies that allow the study of cancer cells at the single-cell level, focusing on critical parameters such as cell morphology, metabolism, and molecular characteristics. These insights are essential for effectively discerning between healthy and cancerous cells and comprehending tumoral progression. Recent advancements in microscopy techniques have significantly advanced the study of cancer cells, with Raman microspectroscopy (RM) emerging as a particularly powerful tool. Indeed, RM can provide both biochemical and spatial details at the single-cell level without the need for labels or causing disruptions to cell integrity. Moreover, RM can be correlated with other microscopy techniques, creating a synergy that offers a spectrum of complementary insights into cancer cell morphology and biology. This review aims to explore the correlation between RM and other microscopy techniques such as confocal fluoresce microscopy (CFM), atomic force microscopy (AFM), digital holography microscopy (DHM), and mass spectrometry imaging (MSI). Each of these techniques has their own strengths, providing different perspectives and parameters about cancer cell features. The correlation between information from these various analysis methods is a valuable tool for physicians and researchers, aiding in the comprehension of cancer cell morphology and biology, unraveling mechanisms underlying cancer progression, and facilitating the development of early diagnosis and/or monitoring cancer progression.

Cytotoxic effects on human dental pulp stem Cells after exposure to adhesive bonding agents

Studies regarding cytotoxic effects attributed to the use of adhesive bonding agents on pulp tissue are not conclusive. To point out whether these materials are safe for clinical use, in vivo exposure of dental pulp to adhesive bonding agents was simulated using an experimental setup in which Human Dental Pulp Stem Cells (hDPSC) are exposed to the action of two kinds of adhesives: self-etching adhesives and two-step bonding agents through a dentine barrier. Cytotoxic effects on these cells were evaluated by MTT assay protocol and fluorescence microscopy, and their results were contrasted to those obtained through Raman spectra taken on single hDPSCs. Overall, no significant cytotoxic effects were observed by combining all the techniques, and cell viability close to 90% was achieved for a dentine barrier of at least 1 mm thick. Moreover, Raman spectroscopy was able to detect structural DNA damage in some dental pulp cells when exposed to two-step bonding agents, suggesting that this technique could be considered a complementary tool with the potential to evaluate cell toxicity beyond cell viability.

Evaluation of Ilex guayusa and Piper marginatum Extract Cytotoxicity on Human Dental Pulp Mesenchymal Stem Cells

BACKGROUND

Amelogenesis imperfecta is a hereditary disorder affecting dental enamel. Among its phenotypes, hypocalcified AI is characterized by mineral deficiency, leading to tissue wear and, consequently, dental sensitivity. Excessive fluoride intake (through drinking water, fluoride supplements, toothpaste, or by ingesting products such as pesticides or insecticides) can lead to a condition known as dental fluorosis, which manifests as stains and teeth discoloration affecting their structure. Our recent studies have shown that extracts from Colombian native plants, Ilex guayusa and Piper marginatum, deposit mineral ions such as phosphate and orthophosphate into the dental enamel structure; however, it is unknown whether these extracts produce toxic effects on the dental pulp.

OBJECTIVE

To assess cytotoxicity effects on human dental pulp stem cells (hDPSCs) exposed to extracts isolated from I. guayusa and P. marginatum and, hence, their safety for clinical use.

METHODS

Raman spectroscopy, fluorescence microscopy, and flow cytometry techniques were employed. For Raman spectroscopy, hDPSCs were seeded onto nanobiochips designed to provide surface-enhanced Raman spectroscopy (SERS effect), which enhances their Raman signal by several orders of magnitude. After eight days in culture, I. guayusa and P. marginatum extracts at different concentrations (10, 50, and 100 ppm) were added. Raman measurements were performed at 0, 12, and 24 h following extract application. Fluorescence microscopy was conducted using an OLIMPUS fv1000 microscope, a live-dead assay was performed using a kit employing a BD FACS Canto TM II flow cytometer, and data analysis was determined using a FlowJo program.

RESULTS

The Raman spectroscopy results showed spectra consistent with viable cells. These findings were corroborated using fluorescence microscopy and flow cytometry techniques, confirming high cellular viability.

CONCLUSIONS

The analyzed extracts exhibited low cytotoxicity, suggesting that they could be safely applied on enamel for remineralization purposes. The use of nanobiochips for SERS effect improved the cell viability assessment.

Raman spectroscopy of lymphocytes from patients with the Epstein-Barr virus infection

In this study, Raman spectroscopy is applied to trace lymphocytes activation following contact with the Epstein-Barr virus (EBV) of the herpesvirus family. The biomarker of cell activation is found to be the 520 cm-1 band, indicating formation of immunoglobulins. The blood samples are obtained from patients diagnosed with infectious mononucleosis and treated at the University Hospital in Kraków. The lymphocytes' Raman spectra are collected using a mapping technique, exciting samples with a 514.5 nm line of Ar + laser. Measurements are performed on the 1st, 4th, 6th, 12th and 30th day of hospitalization, until the patient has recovered. The highest intensity of the immunoglobulin marker is observed on the 4th day of hospitalization, while the results of the blood count of patients show the greatest increase in the number of lymphocytes at the beginning of hospitalization. No activated lymphocytes were observed in the blood of healthy volunteers. Some information is provided by the evaluation of B-cell activation by estimating the activated areas in the cells, which are determined by the presence of the Ig marker. The 900 cm-1 band and band around 1450 cm-1 are also analyzed as markers of the presence of the latent membrane protein, LMP2A (and 2B), of the EBV viral protein. The anomalous degree of depolarization observed in B-cells in the course of EBV infection appears to be due to the influence of a virus protein, disrupting BCR signal transduction.

Photodynamic antimicrobial chemotherapy activities of phthalocyanine-antibiotic conjugates against bacterial biofilms and interactions with extracellular polymeric substances

This study sheds light on how to rationally design efficient photodynamic antimicrobial chemotherapy (PACT) agents by covalently linking phthalocyanines (Pcs) as photosensitizers with an antibiotic: Ciprofloxacin (CIP). Pcs used are zinc (II) 3-(4-((3,17,23-tris(4-(Benzo(d)thiazol-2-yl] thiol) phthalocyanine-9-yl) oxy) phenyl) propanoic acid (1) and zinc (II) 3-(4-(3,17,23-tris(3-(4-(triphenylphosphine) butyl) benzo[d]thiazol-3-ium bromide phthalocyanine-9-yl) oxy) phenyl) propanoic acid (2). High singlet oxygen quantum yields are observed in the presence of CIP. Square wave voltammetry was used to analyse the Pc-CIP uptake by bacteria biofilms of Streptococcus pneumoniae (S. pneumonia) and Escherichia coli (E. coli). Electrochemical impedance spectroscopy and scanning electron spectroscopy were used to study the stability of the biofilms in the presence Pc-CIP complexes and when exposed to light. Raman and time of flight-secondary ion mass spectrometry (TOF-SIMS) are used to identify the breakdown of cellular components of the biofilm and penetration of the Pc-CIP into the biofilms, respectively.

Sub-Cellular Dynamic Analysis of BGC823 Cells after Treatment with the Multi-Component Drug CKI Using Raman Spectroscopy

Multi-component drugs (MCDs) can induce various cellular changes covering multiple levels, from molecular and subcellular structure to cell morphology. A "non-invasive" method for comprehensively detecting the dynamic changes of cellular fine structure and chemical components on the subcellular level is highly desirable for MCD studies. In this study, the subcellular dynamic processes of gastric cancer BGC823 cells after treatment with a multi-component drug, Compound Kushen Injection (CKI), were investigated using a homemade, high-resolution, confocal Raman spectroscopy (RS) device combined with bright-field imaging. The Raman spectra of the nucleus, cytoplasm and intracellular vesicles (0.4-1 μm) were collected simultaneously for each cell treated with CKI at different times and doses. The RS measurements showed that CKI decreased the DNA signatures, which the drug is known to inhibit. Meanwhile, the CKI-induced subcellular dynamic changes in the appearance of numerous intracellular vesicles and the deconstruction of cytoplasm components were observed and discussed. The results demonstrated that high-resolution subcellular micro-Raman spectroscopy has potential for detecting fine cellular dynamic variation induced by drugs and the screening of MCDs in cancer therapy.

Progress in Nano-Biosensors for Non-Invasive Monitoring of Stem Cell Differentiation

Non-invasive, non-destructive, and label-free sensing techniques are required to monitor real-time stem cell differentiation. However, conventional analysis methods, such as immunocytochemistry, polymerase chain reaction, and Western blot, involve invasive processes and are complicated and time-consuming. Unlike traditional cellular sensing methods, electrochemical and optical sensing techniques allow non-invasive qualitative identification of cellular phenotypes and quantitative analysis of stem cell differentiation. In addition, various nano- and micromaterials with cell-friendly properties can greatly improve the performance of existing sensors. This review focuses on nano- and micromaterials that have been reported to improve sensing capabilities, including sensitivity and selectivity, of biosensors towards target analytes associated with specific stem cell differentiation. The information presented aims to motivate further research into nano-and micromaterials with advantageous properties for developing or improving existing nano-biosensors to achieve the practical evaluation of stem cell differentiation and efficient stem cell-based therapies.

Evaluation of the antibacterial activity of gallic acid anchored phthalocyanine-doped silica nanoparticles towards Escherichia coli and Staphylococcus aureus biofilms and planktonic cells

In this work, we have described the synthesis of phthalocyanine complexes Zn(II) tetrakis 4-(5-formylpyridin-2-yl)oxy) phthalocyanine (2), Zn(II) tetrakis-1-butyl-4-(2-(6- (tetra-phenoxy)pyridin-3-yl) vinyl)pyridin-1-ium phthalocyanine (3) and Zn(II) tetrakis 1-butyl-5-(2-(1-butylpyridin-1-ium-4-yl)vinyl)-2-(tetra-phenoxy)pyridin-1-ium phthalocyanine (4). The effect of a varying number of charges when the Pc complexes are alone or grafted in gallic acid (GA) tagged silica nanoparticles on photodynamic antimicrobial chemotherapy (PACT) is investigated toward Staphylococcus aureus (S.aureus) and Escherichia coli (E.coli) in both planktonic and biofilm forms. Complex 4, bearing a total of 8 cationic charges, displayed the highest activity with log CFU values of 8.60 and 6.42 against E.coli and S.aureus biofilms, respectively. The surface stability of E.coli and S.aureus biofilms in the presence of 4 and its conjugate was analysed using cyclic voltammetry. Scanning electron microscopy (SEM) and Raman spectra are also used to study the conformational and biochemical changes within biofilm upon subjecting them to PACT.

A Handheld Visible Resonance Raman Analyzer Used in Intraoperative Detection of Human Glioma

There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Raman Spectroscopy as a Tool to Study the Pathophysiology of Brain Diseases

The Raman phenomenon is based on the spontaneous inelastic scattering of light, which depends on the molecular characteristics of the dispersant. Therefore, Raman spectroscopy and imaging allow us to obtain direct information, in a label-free manner, from the chemical composition of the sample. Since it is well established that the development of many brain diseases is associated with biochemical alterations of the affected tissue, Raman spectroscopy and imaging have emerged as promising tools for the diagnosis of ailments. A combination of Raman spectroscopy and/or imaging with tagged molecules could also help in drug delivery and tracing for treatment of brain diseases. In this review, we first describe the basics of the Raman phenomenon and spectroscopy. Then, we delve into the Raman spectroscopy and imaging modes and the Raman-compatible tags. Finally, we center on the application of Raman in the study, diagnosis, and treatment of brain diseases, by focusing on traumatic brain injury and ischemia, neurodegenerative disorders, and brain cancer.

Non-invasive real-time monitoring of cell concentration and viability using Doppler ultrasound

Bioprocess optimization towards higher productivity and better quality control relies on real-time process monitoring tools to measure process and culture parameters. Cell concentration and viability are among the most important parameters to be monitored during bioreactor operations that are typically determined using optical methods on an extracted sample. In this paper, we have developed an online non-invasive sensor to measure cell concentration and viability based on Doppler ultrasound. An ultrasound transducer is mounted outside the bioreactor vessel and emits a high frequency tone burst (15 MHz) through the vessel wall. Acoustic backscatter from cells in the bioreactor depends on cell concentration and viability. The backscattered signal is collected through the same transducer and analyzed using multivariate data analysis (MVDA) to characterize and predict the cell culture properties. We have developed accurate MVDA models to predict the Chinese hamster ovary (CHO) cell concentration in a broad range from 0.1 × 10⁶ cells/mL to 100 × 10⁶ cells/mL, and cell viability from 3% to 99%. The non-invasive monitoring is ideal for single use bioreactor and the in-situ measurements removes the burden for offline sampling and dilution steps. This method can be similarly applied to other suspension cell culture modalities.

Artificial Intelligent Label-Free SERS Profiling of Serum Exosomes for Breast Cancer Diagnosis and Postoperative Assessment

Breast cancer subtypes have important implications of treatment responses and clinical outcomes. Exosomes have been considered as promising biomarkers for liquid biopsies, but the utility of exosomes for accurate diagnosis of distinct breast cancer subtypes is a grand challenge due to the difficulty in uncovering the subtle compositional difference in complex clinical settings. Herein, we report an artificial intelligent surface-enhanced Raman spectroscopy (SERS) strategy for label-free spectroscopic analysis of serum exosomes, allowing for accurate diagnosis of breast cancer and assessment of surgical outcomes. Our deep learning algorithm trained with SERS spectra of cancer cell-derived exosomes is demonstrated with a 100% prediction accuracy for human patients with different breast cancer subtypes who do not undergo surgery using SERS spectra of serum exosomes. Furthermore, when combined with similarity analysis by principal component analysis, our approach is able to evaluate the surgical outcomes of breast cancer of distinct molecular subtypes.

A nanoarchitecture of a gold cluster conjugated gold nanorod hybrid system and its application in fluorescence imaging and plasmonic photothermal therapy

Engineering different nanomaterials into a single functional material can impart unique properties of the parental nanoparticles, especially in the field of bio imaging and therapy. Gold nanomaterials having different sizes, shapes and dimensionalities exhibit exceptional properties apart from their non-toxicity and hence are strong candidates in the biomedical field. Designing a hybrid nanomaterial of two gold nanostructures retaining the individual properties of the parental nanomaterials is challenging. Here, we demonstrate the synthesis of a hybrid nanomaterial (GQC@GNR), comprising an extremely small gold nanocluster and a representative of the asymmetric gold nanostructure, i.e., a gold nanorod, both having their own different exclusive optical properties like tuneable emission and NIR absorption characteristics, respectively. The hybrid system is designed to retain its emission and absorption in the NIR region to use it as an agent for simultaneous imaging and therapy. The formation of GQC@GNR and its architectonics heavily depend on the synthesis route and the parameters adopted which in turn have a direct influence on its properties. The architecture and its connection to the optical properties are explained using UV-Vis absorption and photoluminescence spectroscopy, zeta potential, transmission electron microscopy, etc. DFT-based computational modelling supports architectonics as explained by the experimental findings. The formation of the gold-gold hybrid system witnessed interesting science with a strong indication that materials with desired properties can be designed by appropriately modulating the architectonics of hybrid formation. Finally, folate conjugated GQC@GNR demonstrated its efficacy for targeted imaging and photothermal therapy in HeLa cells and tumor-bearing animal models. The detailed therapeutic efficacy of GQC@GNR is also explained based on Raman spectroscopy.

Label-Free Plasmon-Enhanced Spectroscopic HER2 Detection for Dynamic Therapeutic Surveillance of Breast Cancer

The expression of human epidermal growth factor receptor-2 (HER2) has important implications for pathogenesis, progression, and therapeutic efficacy of breast cancer. The detection of its variation during the treatment is crucial for therapeutic decision-making but remains a grand challenge, especially at the cellular level. Here, we develop a machine learning-driven surface-enhanced Raman spectroscopy (SERS)-integrated strategy for label-free detection of cellular HER2. Specifically, our method allows the extraction of cell-rich spectral signatures utilized for identification and classification of cancer cells with distinct HER2 expression with a high accuracy of 99.6%. By combining label-free SERS detection and machine learning-driven chemometric analysis, we are able to perform longitudinal monitoring of therapeutic efficacy at the cellular level during the treatment of HER2+ breast cancer, which aids in the subsequent decision-making and management. This work provides a promising technique capable of performing dynamic label-free spectroscopic detection for therapeutic surveillance of diseases.

Discriminating cell line specific features of antibiotic-resistant strains of Escherichia coli from Raman spectra via machine learning analysis

While Raman spectroscopy can provide label-free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain-specific (and function-specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model.

Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging

This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information-nucleic acids, proteins, and lipids-from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.

Raman spectroscopic characterization of selenium N-heterocyclic carbene compounds

In this study, Raman spectroscopy is employed to analyze and characterize two salts (N-heterocyclic carbene) and their respective selenium N-heterocyclic carbene compounds. The features observed as differences among Raman spectral data of two different N-heterocyclic carbene salts are called Salt-I and Salt-II and their respective Se compounds, called Compound-I & Compound-II, are used to confirm the formation covalent bond between Se atom carbon atom of carbene. Enhancement in peak intensities and shifting of peak positions is directly related with compound formation. Raman spectral data provide a detail information about bond formation, chemical and structural differences between salts and compounds. The observed Raman spectral features of both salts and compounds are in consistent with computationally calculated Raman spectral features. Raman spectral features of each salt and its respective compound was further analyzed with principal component analysis, which was found helpful for differentiating each salt from its respective compound.

The role of pro- and antiangiogenic factors in angiogenesis process by Raman spectroscopy

Raman spectroscopy and Raman imaging are powerful techniques to monitor biochemical composition around blood vessel. The aim of this study was to understand the role of pro- and antiangiogenic factors in angiogenesis process. Raman imaging and Raman single spectrum measurements allow the diagnosis of cancer biochemical changes in blood vessel based on several biomarkers simultaneously. We have demonstrated that Raman imaging combined with statistical methods are useful to monitoring pro- and antiangiogenic factors responsible for angiogenesis process. In this work Raman markers of proangiogenic and antiangiogenic factors were identified based on their vibrational signatures. Obtained results can help understand how growing tumor create its vascular system.

Raman spectroscopy-a tool for rapid differentiation among microbes causing urinary tract infections

Urinary tract infections belong to the most common infections in the world. Besides community-acquired infections, nosocomial infections pose a high risk especially for patients having indwelling catheters, undergoing urological surgeries or staying at hospital for prolonged time. They can be often complicated by antimicrobial resistance and/or biofilm formation. Therefore, a rapid diagnostic tool enabling timely identification of a causative agent and its susceptibility to antimicrobials is a need. Raman spectroscopy appears to be a suitable method that allows rapid differentiation among microbes and provides a space for further analyses, such as determination of capability of biofilm formation or antimicrobial susceptibility/resistance in tested strains. Our work here presents a possibility to differ among most common microbes causing urinary tract infections (belonging to 20 species). We tested 254 strains directly from colonies grown on Mueller-Hinton agar plates. The results show that it is possible to distinguish among the tested species using Raman spectroscopy, which proves its great potential for future use in clinical diagnostics. Moreover, we present here a pilot study of a real-time analysis and identification (in less than 10 min) of single microbial cells directly in urine employing optical tweezers combined with Raman spectroscopy.

Krüppel-like factor 6 (KLF6) requires its amino terminal domain to promote villous trophoblast cell fusion

INTRODUCTION

Villous cytotrophoblast (vCTB) cells fuse to generate and maintain the syncytiotrophoblast layer required for placental development and function. Krüppel-like factor 6 (KLF6) is a ubiquitous transcription factor with an N-terminal acidic transactivation domain and a C-terminal zinc finger DNA-binding domain. KLF6 is highly expressed in placenta, and it is required for proper placental development. We have demonstrated that KLF6 is necessary for cell fusion in human primary vCTBs, and in the BeWo cell line.

MATERIALS AND METHODS

Full length KLF6 or a mutant lacking its N-terminal domain were expressed in BeWo cells or in primary vCTB cells isolated from human term placentas. Cell fusion, gene and protein expression, and cell proliferation were analyzed. Moreover, Raman spectroscopy and atomic force microscopy (AFM) were used to identify biochemical, topography, and elasticity cellular modifications.

RESULTS

The increase in KLF6, but not the expression of its deleted mutant, is sufficient to trigger cell fusion and to raise the expression of β-hCG, syncytin-1, the chaperone protein 78 regulated by glucose (GRP78), the ATP Binding Cassette Subfamily G Member 2 (ABCG2), and Galectin-1 (Gal-1), all molecules involved in vCTB differentiation. Raman and AFM analysis revealed that KLF6 reduces NADH level and increases cell Young's modulus. KLF6-induced differentiation correlates with p21 upregulation and decreased cell proliferation. Remarkable, p21 silencing reduces cell fusion triggered by KLF6 and the KLF6 mutant impairs syncytialization and decreases syncytin-1 and β-hCG expression.

DISCUSSION

KLF6 induces syncytialization through a mechanism that involves its regulatory transcriptional domain in a p21-dependent manner.

Single cell Raman spectroscopy to identify different stages of proliferating human hepatocytes for cell therapy

Background

Cell therapy provides hope for treatment of advanced liver failure. Proliferating human hepatocytes (ProliHHs) were derived from primary human hepatocytes (PHH) and as potential alternative for cell therapy in liver diseases. Due to the continuous decline of mature hepatic genes and increase of progenitor like genes during ProliHHs expanding, it is challenge to monitor the critical changes of the whole process. Raman microspectroscopy is a noninvasive, label free analytical technique with high sensitivity capacity. In this study, we evaluated the potential and feasibility to identify ProliHHs from PHH with Raman spectroscopy.

Methods

Raman spectra were collected at least 600 single spectrum for PHH and ProliHHs at different stages (Passage 1 to Passage 4). Linear discriminant analysis and a two-layer machine learning model were used to analyze the Raman spectroscopy data. Significant differences in Raman bands were validated by the associated conventional kits.

Results

Linear discriminant analysis successfully classified ProliHHs at different stages and PHH. A two-layer machine learning model was established and the overall accuracy was at 84.6%. Significant differences in Raman bands have been found within different ProliHHs cell groups, especially changes at 1003 cm⁻¹, 1206 cm⁻¹ and 1440 cm⁻¹. These changes were linked with reactive oxygen species, hydroxyproline and triglyceride levels in ProliHHs, and the hypothesis were consistent with the corresponding assay results.

Conclusions

In brief, Raman spectroscopy was successfully employed to identify different stages of ProliHHs during dedifferentiation process. The approach can simultaneously trace multiple changes of cellular components from somatic cells to progenitor cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-021-02619-9.

Time-gated Raman spectroscopy and proteomics analyses of hypoxic and normoxic renal carcinoma extracellular vesicles

Extracellular vesicles (EVs) represent a diverse group of small membrane-encapsulated particles involved in cell-cell communication, but the technologies to characterize EVs are still limited. Hypoxia is a typical condition in solid tumors, and cancer-derived EVs support tumor growth and invasion of tissues by tumor cells. We found that exposure of renal adenocarcinoma cells to hypoxia induced EV secretion and led to notable changes in the EV protein cargo in comparison to normoxia. Proteomics analysis showed overrepresentation of proteins involved in adhesion, such as integrins, in hypoxic EV samples. We further assessed the efficacy of time-gated Raman spectroscopy (TG-RS) and surface-enhanced time-gated Raman spectroscopy (TG-SERS) to characterize EVs. While the conventional continuous wave excitation Raman spectroscopy did not provide a notable signal, prominent signals were obtained with the TG-RS that were further enhanced in the TG-SERS. The Raman signal showed characteristic changes in the amide regions due to alteration in the chemical bonds of the EV proteins. The results illustrate that the TG-RS and the TG-SERS are promising label free technologies to study cellular impact of external stimuli, such as oxygen deficiency, on EV production, as well as differences arising from distinct EV purification protocols.

Roadmap on Universal Photonic Biosensors for Real-Time Detection of Emerging Pathogens

The COVID-19 pandemic has made it abundantly clear that the state-of-the-art biosensors may not be adequate for providing a tool for rapid mass testing and population screening in response to newly emerging pathogens. The main limitations of the conventional techniques are their dependency on virus-specific receptors and reagents that need to be custom-developed for each recently-emerged pathogen, the time required for this development as well as for sample preparation and detection, the need for biological amplification, which can increase false positive outcomes, and the cost and size of the necessary equipment. Thus, new platform technologies that can be readily modified as soon as new pathogens are detected, sequenced, and characterized are needed to enable rapid deployment and mass distribution of biosensors. This need can be addressed by the development of adaptive, multiplexed, and affordable sensing technologies that can avoid the conventional biological amplification step, make use of the optical and/or electrical signal amplification, and shorten both the preliminary development and the point-of-care testing time frames. We provide a comparative review of the existing and emergent photonic biosensing techniques by matching them to the above criteria and capabilities of preventing the spread of the next global pandemic.

Raman spectroscopy assisted biochemical evaluation of L929 fibroblast cells on differentially crosslinked gelatin hydrogels

Biochemical evaluation of cell-matrix interaction using conventional labelling techniques often possesses limitations due to dye entrapment. In contrast, Raman spectroscopy guided approach offers label-free determination of cell-matrix biochemistry. Herein, gelatin (Gel) matrices modified with 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide/ N-Hydroxysuccinimide (EDC/NHS) and glutaraldehyde (GTA) was used as standards for comparative evaluation. Raman spectroscopy was deployed as a label-free approach to investigate interaction of cells with Gel hydrogels. Raman-based approach assisted in evaluation of cell-matrix interactions by identifying key biomolecular signatures retrospecting the fact that L929 fibroblast cells portrayed excellent growth and proliferation kinetics in crosslinked Gel as compared to its bare counterpart. EDC crosslinked hydrogels exhibited superior cell proliferation than its GTA counterparts. Cell proliferation on differentially crosslinked gel was also confirmed using standard MTT Assay and Rhodamine-DAPI staining thus corroborating the fact that Raman spectroscopy can be deployed as a superior label-free alternative towards real-time determination of cell proliferation and growth.

Exploring subcellular responses of prostate cancer cells to clinical doses of X-rays by Raman microspectroscopy

Modern techniques of radiotherapy such as fractioned radiotherapy require applications of low doses of ionizing radiation (up to 10 Gy) for effective patient treatment. It is, therefore, crucial to understand the response mechanisms in cancer cells irradiated with low (clinical) doses. The cell's response to irradiation depends on a dose and post-irradiation time. Both factors should be considered when studying the influence of ionizing radiation on cancer cells. Thus, in the present study, PC-3 prostate cancer cells were irradiated with clinical doses of X-rays to determine dose- and time-dependent response to the irradiation. Raman spectroscopy and biological methods (MTT and comet assays) were applied for the analysis of biochemical changes in the cells induced by low doses of X-ray irradiation at 0 h and 24 h post-irradiation timepoints. Due to a limited view of the biochemical changes at the subcellular level given by single spectrum Raman measurements, Raman mapping of the whole cell area was performed. The results were compared with those obtained for cell irradiation with high doses. The analysis was based on the Partial Least Squares Regression (PLSR) method for the cytoplasmic and nuclear regions separately. Additionally, for the first time, irradiation classification was performed to confirm Raman spectroscopy as a powerful tool for studies on cancer cells treated with clinical doses of ionizing radiation.

Paper-based electrochemical sensor for on-site detection of the sulphur mustard

Herein, we report a novel paper-based electrochemical sensor for on-site detection of sulphur mustards. This sensor was conceived combining office paper-based electrochemical sensor with choline oxidase enzyme to deliver a sustainable sensing tool. The mustard agent detection relies on the evaluation of inhibition degree of choline oxidase, which is reversibly inhibited by sulphur mustards, by measuring the enzymatic by-product H₂O₂ in chronoamperometric mode. A nanocomposite constituted of Prussian Blue nanoparticles and Carbon Black was used as working electrode modifier to improve the electroanalytical performances. This bioassay was successfully applied for the measurement of a sulphur mustard, Yprite, obtaining a detection limit in the millimolar range (LOD = 0.9 mM). The developed sensor, combined with a portable and easy-to-use instrumentation, can be applied for a fast and cost-effective detection of sulphur mustards.

Toward Raman Subcellular Imaging of Endothelial Dysfunction

Multiple diseases are at some point associated with altered endothelial function, and endothelial dysfunction (ED) contributes to their pathophysiology. Biochemical changes of the dysfunctional endothelium are linked to various cellular organelles, including the mitochondria, endoplasmic reticulum, and nucleus, so organelle-specific insight is needed for better understanding of endothelial pathobiology. Raman imaging, which combines chemical specificity with microscopic resolution, has proved to be useful in detecting biochemical changes in ED at the cellular level. However, the detection of spectroscopic markers associated with specific cell organelles, while desirable, cannot easily be achieved by Raman imaging without labeling. This critical review summarizes the current advances in Raman-based analysis of ED, with a focus on a new approach involving molecular Raman reporters that could facilitate the study of biochemical changes in cellular organelles. Finally, imaging techniques based on both conventional spontaneous Raman scattering and the emerging technique of stimulated Raman scattering are discussed.

The Use of Raman Spectroscopy to Monitor Metabolic Changes in Stressed Metschnikowia sp. Yeasts

Raman spectroscopy is a universal method designed for the analysis of a wide range of physical, chemical and biological systems or various surfaces. This technique is suitable to monitor various components of cells, tissues or microorganisms. The advantages include very fast non-contact and non-destructive analysis and no or minimal need for sample treatment. The yeasts Metschnikowia can be considered as industrially usable producers of pulcherrimin or single-cell lipids, depending on cultivation conditions and external stress. In the present study, Raman spectroscopy was used as an effective tool to identify both pulcherrimin and lipids in single yeast cells. The analysis of pulcherrimin is very demanding; so far, there is no optimal procedure to analyze or identify this pigment. Based on results, the strong dependence of pulcherrimin production on the ferric ion concentration was found with the highest yield in media containing 0.1 g/L iron. Further, production of lipids in Metschnikowia cells was studied at different temperatures and C:N ratios, using Raman spectroscopy to follow fatty acids composition, under different regimes, by monitoring the iodine number. The results of Raman spectroscopy were comparable with the fatty acid analysis obtained by gas chromatography. This study therefore supported use of Raman spectroscopy for biotechnological applications as a simple tool in the identification and analysis both the pulcherrimin and microbial lipids. This method provides a quick and relatively accurate estimation of targeted metabolites with minimal sample modification and allows to monitor metabolic changes over time of cultivation.

SERS characterization of aggregated and isolated bacteria deposited on silver-based substrates

Surface-enhanced Raman scattering (SERS), based on the enhancement of the Raman signal of molecules positioned within a few nanometres from a structured metal surface, is ideally suited to provide bacterial-specific molecular fingerprints which can be used for analytical purposes. However, for some complex structures such as bacteria, the generation of reproducible SERS spectra is still a challenging task. Among the various factors influencing the SERS variability (such as the nature of SERS-active substrate, Raman parameters and bacterial specificity), we demonstrate in this study that the environment of Gram-positive and Gram-negative bacteria deposited on ultra-thin silver films also impacts the origin of the SERS spectra. In the case of densely packed bacteria, the obtained SERS signatures were either characteristic of the secretion of adenosine triphosphate for Staphylococcus aureus (S. aureus) or the cell wall and the pili/flagella for Escherichia coli (E. coli), allowing for an easy discrimination between the various strains. In the case of isolated bacteria, SERS mapping together with principal component analysis revealed some variabilities of the spectra as a function of the bacteria environment and the bactericidal effect of the silver. However, the variability does not preclude the SERS signatures of various E. coli strains to be discriminated.

In search of the correlation between nanomechanical and biomolecular properties of prostate cancer cells with different metastatic potential

Nanomechanical properties of living cells, as measured with atomic force microscopy (AFM), are increasingly recognized as criteria that differentiate normal and pathologically altered cells. Locally measured cell elastic properties, described by the parameter known as Young's modulus, are currently proposed as a new diagnostic parameter that can be used at the early stage of cancer detection. In this study, local mechanical properties of normal human prostate (RWPE-1) cells and a range of malignant (22Rv1) and metastatic prostate cells (LNCaP, Du145 and PC3) were investigated. It was found that non-malignant prostate cells are stiffer than cancer cells while the metastatic cells are much softer than malignant cells from the primary tumor site. Next, the biochemical properties of the cells were measured using confocal Raman (RS) and Fourier-transform infrared (FT-IR) spectroscopies to reveal these cells' biochemical composition as malignant transformation proceeds. Nanomechanical and biochemical profiles of five different prostate cell lines were subsequently analyzed using partial least squares regression (PLSR) in order to identify which spectral features of the RS and FT-IR spectra correlate with the cell's elastic properties. The PLSR-based model could predict Young's modulus values based on both RS and FT-IR spectral information. These outcomes show not only that AFM, RS and FT-IR techniques can be used for discrimination between normal and cancer cells, but also that a linear correlation between mechanical response and biomolecular composition of the cells that undergo malignant transformation can be found. This knowledge broadens our understanding of how prostate cancer cells evolve thorough the multistep process of tumor pathogenesis.

Physicochemical damage and early-stage biological response to X-ray radiation studied in prostate cancer cells by Raman spectroscopy

Exposure to ionizing radiation significantly affects biochemistry of cancer cells. The effect of irradiation can be divided into two stages, that is, the physicochemical stage and the biological response. Both effects induce different biochemical changes in the cells and should be analyzed as two separate phenomena. Thus, in the current study, Raman spectroscopy of prostate cancer cells fixed before (the physicochemical damage model) and just after (the biological response model) irradiation was undertaken to compare biochemical composition of irradiated cancer cells at both stages. Spectroscopic analysis of the cells was performed separately for cytoplasmic and nuclear regions. Biochemical changes of irradiated cells were analyzed using partial least squares regression (PLSR) method on the basis of the collected Raman spectra. Regression coefficients were therefore used to describe differences and similarities between biochemical composition of cancer cells undergoing the physicochemical stage and biological response. Additionally, PLSR models of both phenomena were compared for linear dose-dependence and a cross prediction.

Vibrational Spectroscopy for In Vitro Monitoring Stem Cell Differentiation

Stem cell technology has attracted considerable attention over recent decades due to its enormous potential in regenerative medicine and disease therapeutics. Studying the underlying mechanisms of stem cell differentiation and tissue generation is critical, and robust methodologies and different technologies are required. Towards establishing improved understanding and optimised triggering and control of differentiation processes, analytical techniques such as flow cytometry, immunohistochemistry, reverse transcription polymerase chain reaction, RNA in situ hybridisation analysis, and fluorescence-activated cell sorting have contributed much. However, progress in the field remains limited because such techniques provide only limited information, as they are only able to address specific, selected aspects of the process, and/or cannot visualise the process at the subcellular level. Additionally, many current analytical techniques involve the disruption of the investigation process (tissue sectioning, immunostaining) and cannot monitor the cellular differentiation process in situ, in real-time. Vibrational spectroscopy, as a label-free, non-invasive and non-destructive analytical technique, appears to be a promising candidate to potentially overcome many of these limitations as it can provide detailed biochemical fingerprint information for analysis of cells, tissues, and body fluids. The technique has been widely used in disease diagnosis and increasingly in stem cell technology. In this work, the efforts regarding the use of vibrational spectroscopy to identify mechanisms of stem cell differentiation at a single cell and tissue level are summarised. Both infrared absorption and Raman spectroscopic investigations are explored, and the relative merits, and future perspectives of the techniques are discussed.

Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection

In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA-LDA and PCA-QDA models. The PCA-LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA-LDA and PCA-QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.

Surface-Enhanced Raman Spectroscopy of Organic Molecules and Living Cells with Gold-Plated Black Silicon

Black silicon (bSi) refers to an etched silicon surface comprising arrays of microcones that effectively suppress reflection from UV to near-infrared (NIR) while simultaneously enhancing the scattering and absorption of light. This makes bSi covered with a nm-thin layer of plasmonic metal, i.e., gold, an attractive substrate material for sensing of bio-macromolecules and living cells using surface-enhanced Raman spectroscopy (SERS). The performed Raman measurements accompanied with finite element numerical simulation and density functional theory analysis revealed that at the 785 nm excitation wavelength, the SERS enhancement factor of the bSi/Au substrate is as high as 10⁸ due to a combination of electromagnetic and chemical mechanisms. This finding makes the SERS-active bSi/Au substrate suitable for detecting trace amounts of organic molecules. We demonstrate the outstanding performance of this substrate by highly sensitive and specific detection of a small organic molecule of 4-mercaptobenzoic acid and living C6 rat glioma cell nucleic acids/proteins/lipids. Specifically, the bSi/Au SERS-active substrate offers a unique opportunity to investigate the living cells' malignant transformation using characteristic protein disulfide Raman bands as a marker. Our findings evidence that bSi/Au provides a pathway to the highly sensitive and selective, scalable, and low-cost substrate for lab-on-a-chip SERS biosensors that can be integrated into silicon-based photonics devices.

Shell-isolated nanoparticle-enhanced Raman spectroscopy for characterization of living yeast cells

Studying the biochemistry of yeast cells has enabled scientists to understand many essential cellular processes in human cells. Further development of biotechnological and medical progress requires revealing surface chemistry in living cells by using a non-destructive and molecular structure sensitive technique. In this study shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) was applied for probing the molecular structure of Metschnikowia pulcherrima yeast cells. Important function of studied cells is the ability to eliminate iron from growth media by precipitating the insoluble pigment pulcherrimin. Comparative SERS and SHINERS analysis of the yeast cells in combination with bare Au and shell-isolated Au@SiO₂ nanoparticles were performed. It was observed that additional bands, such as adenine ring-related vibrational modes appear due to interaction with bare Au nanoparticles; the registered spectra do not coincide with the spectra where Au@SiO₂ nanoparticles were used. SHINERS spectra of M. pulcherrima were significantly enhanced comparing to the Raman spectra. Based on first-principles calculations and 830-nm excited Raman analysis of pulcherrimin, the SHINERS signatures of iron pigment in yeast cells were revealed. Being protected from direct interaction of metal with adsorbate, Au@SiO₂ nanoparticles yield reproducible and reliable vibrational signatures of yeast cell wall constituents.

Confocal Raman microspectroscopic analysis on the time-dependent impact of DAPT, a γ-secretase inhibitor, to osteosarcoma cells

Confocal Raman microspectroscopy (CRM) analysis provides subcellular compositional and morphology related information. In this study, we used CRM in conjunction with multivariate statistical analysis to elucidate the time-dependent impact of the γ-secretase inhibitor, DAPT (N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester) of osteosarcoma (OS) cells. The interactions of DAPT (20 μM) with a murine OS cell line K7M2 at 24 and 48 h were monitored. The spectral characteristics of drug action were identified to illustrate the cellular compositional alterations, showing that DAPT induced apoptosis by reducing the protein, lipid and nucleic acid content and structural changes. Multivariate algorithms, principal component analysis (PCA) and linear discriminant analysis (LDA) revealed a clear separation among cells in the untreated control (UT), 24H (DAPT-treated for 24 h), and 48H (DAPT-treated for 48 h) groups, achieving sensitivities of 100%, 96%, 100% and specificities of 98%, 100%, 100%, respectively. After point-scanned spectral imaging, K-means clustering analysis (KCA) was further used to visualize sub-cellular morphological changes and the underlying spectral characteristics in a temporal sequence. Compared with the UT group, Raman imaging results exhibited gradually increased nuclear division of OS cells with DAPT treatment duration extension, along with changes in the physiology of other organelles within the cell. By providing a unique perspective for understanding the temporary cellular responses to DAPT at molecular level, the achieved results form the foundation of strategies for the application of CRM and other Raman-based techniques for studying the therapeutic responses of other anticancer agents in cancer model systems.

Current research progress of mammalian cell-based biosensors on the detection of foodborne pathogens and toxins

Foodborne diseases caused by pathogens and toxins are a serious threat to food safety and human health; thus, they are major concern to society. Existing conventional foodborne pathogen or toxin detection methods, including microbiological assay, nucleic acid-based assays, immunological assays, and instrumental analytical method, are time-consuming, labor-intensive and expensive. Because of the fast response and high sensitivity, cell-based biosensors are promising novel tools for food safety risk assessment and monitoring. This review focuses on the properties of mammalian cell-based biosensors and applications in the detection of foodborne pathogens (bacteria and viruses) and toxins (bacterial toxins, mycotoxins and marine toxins). We discuss mammalian cell adhesion and how it is involved in the establishment of 3D cell culture models for mammalian cell-based biosensors, as well as evaluate their limitations for commercialization and further development prospects.

Discrimination of Dental Pulp Stem Cell Regenerative Heterogeneity by Single-Cell Raman Spectroscopy

IMPACT STATEMENT

This study is the first to investigate and confirm the effectiveness of single-cell Raman spectroscopy (SCRM), in its ability to discriminate between dental pulp stem cells (DPSCs) with contrasting proliferative and differentiation capabilities. The findings show that SCRM can rapidly and noninvasively distinguish and identify DPSC subpopulations in vitro with superior proliferative and multipotency properties, versus lesser quality DPSCs, thereby overcoming the significant heterogeneity issues surrounding DPSC ex vivo expansion and differentiation capabilities. Such findings support further SCRM assessment for the selective screening/isolation of superior quality DPSCs from whole dental pulp tissues, for more effective in vitro evaluation and therapy development.

Scalable nanolaminated SERS multiwell cell culture assay

This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.

Insights Into the Role of Extracellular DNA and Extracellular Proteins in Biofilm Formation of Vibrio parahaemolyticus

The extracellular polymeric substances (EPS) construct the three-dimensional (3-D) structure of biofilms, but their respective roles are still not clear. Therefore, this study aimed to illuminate the role of key chemical components [extracellular DNA (eDNA), extracellular proteins, and carbohydrates] of EPS in biofilm formation of Vibrio parahaemolyticus. The correlations between each key chemical component and biofilm formation were first determined, showing that the biofilm formation of V. parahaemolyticus was strongly positively correlated with both eDNA and protein content (P < 0.01), but not with carbohydrates. Subsequently, individual DNase I or protease K treatment markedly reduced the initial adhesion and structural stability of the formed biofilms by hydrolyzing the eDNA or extracellular proteins, but did not induce significant dispersion of mature biofilms. However, the combination of DNase I and protease K treatment induced the obvious dispersion of the mature biofilms through the concurrent destruction of eDNA and extracellular proteins. The analysis at a structural level showed that the collapse of biofilms was mainly attributed to the great damage of the loop configuration of eDNA and the secondary structure of proteins caused by the enzyme treatment. Therefore, this study provides a deep understanding of the role of key chemical components of EPS in biofilm development of V. parahaemolyticus, which may give a new strategy to develop environmentally friendly methods to eradicate the biofilms in food industry.

Raman spectral characterization of silver metal-based complexes of different benzimidazolium ligands

In this study, Raman spectroscopy has been employed for the characterization of two structurally different monodentate N-heterocyclic carbene ligands (ligand-1 and ligand-2) and their respective complexes (complex-1 and complex-2). The Raman spectral features are found helpful for the confirmation of formation of complexes. The significant Raman spectral features are identified for benzimidazole ring with higher intensities in carbene complexes having more polarizability as compared to their ligands, providing the evidence for the formation of coordinate covalent bond. The successful complexation is further supported by using multivariate data analysis technique, Principal Component Analysis (PCA), which is found very helpful to highlight the variability of Raman spectral data of both ligands and their respective metal complexes from each other. Moreover, the coordination of carbene with Ag(I) is confirmed from the dominant spectral markers of higher intensities at 359 cm^-1 in complex-1 and 338 cm^-1 in complex-2. The effective and reliable characterization and confirmation of metal complexes indicates the potential of Raman spectroscopy for its use for the characterization of the organometallic complexes and other chemical products.

Raman spectroscopy along with Principal Component Analysis for the confirmation of Silver(I)-N-heterocyclic carbene complex formation

In this study Raman spectroscopy is employed for the characterization of two different ligands called as S1 and S2 and their respective co-ordinate complexes called C1 and C2. Specific Raman spectral signatures are observed for each of these Silver(I)-N-heterocyclic carbene complexes Ag(I)-(NHCs), which can be associated with the imidazolium ring, part of both of the ligands, indicating the formation of new coordinate covalent bond. For the detailed analysis, Raman spectral data of these ligands and complexes is analyzed by multivariate data analysis technique, Principal Component Analysis (PCA) which is found very helpful to differentiate two ligands and complexes from each other. The significant Raman peaks with higher intensities in the complexes as compared to the respective ligands are associated with imidazole ring which can be attributed to the enhanced polarizability of this ring on complex formation. Moreover, the spectral features associated with (AgC) bond are observed with higher intensity at 360 in (C1) and 383 in (C2). This study indicates the potential of Raman spectroscopy for the characterization and confirmation of formation of organometallic complexes and other chemical products.