Gaining Biological Insights through Supervised Data Visualization.

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE's prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

In-situ characterization of porcine fibroblasts in response to silver ions by Raman spectroscopy and liquid scanning transmission electron microscopy.

Since silver ion is known for its antimicrobial function, most of the research has focused mainly on toxicity effects rather than the role of silver ion in general biology and the behind mechanism of actions of silver ion in mammalian cells. Moreover, a conventional in vitro approach to estimate the effects of silver ion on cells does not provide information about the biochemical changes and might accompany artifacts due to invasive and destructive sample preparation processes. In the present study, in-situ real time approaches were applied to evaluate the impact of silver ion (0.57, 1.34, 1.96, 2.33 mg/L) on fibroblast cells. Raman spectroscopy analysis showed that Raman peak intensities of proteins and nucleic acids significantly increased in the cells after exposure to silver ion for 21 h, especially at relatively higher levels 1.34, 1.96, and 2.33 mg/L. Raman peak at 1585 cm-1 and liquid scanning transmission electron microscopy energy-dispersive x-ray spectroscopy (STEM-EDS) analysis revealed the fate of silver ion that was taken up by the cell and reduced into metallic silver accumulating in the cell as silver nanoparticles. These results suggest cells were undergoing different activities such as enhanced metabolic activities rather than cell apoptosis or cell death. Additionally, Raman spectroscopy predicted the level of silver ion exposed to the cell at 2.11 ± 0.38 and 1.73 ± 0.26 mg/L by the PLS prediction model, compared with the results measured by inductively coupled plasma mass spectrometry (ICP-MS), 2.14 ± 0.07 and 1.87 ± 0.07 mg/L respectively, suggesting Raman spectroscopy can provide a new and fast approach to determine and measure the concentration of silver ion or probably other tested molecules treated to the cell for the future research.

Bioanalytical approaches for the detection, characterization, and risk assessment of micro/nanoplastics in agriculture and food systems.

This review discusses the most recent literature (mostly since 2019) on the presence and impact of microplastics (MPs, particle size of 1 µm to 5 mm) and nanoplastics (NPs, particle size of 1 to 1000 nm) throughout the agricultural and food supply chain, focusing on the methods and technologies for the detection and characterization of these materials at key entry points. Methods for the detection of M/NPs include electron and atomic force microscopy, vibrational spectroscopy (FTIR and Raman), hyperspectral (bright field and dark field) and fluorescence imaging, and pyrolysis-gas chromatography coupled to mass spectrometry. Microfluidic biosensors and risk assessment assays of MP/NP for in vitro, in vivo, and in silico models have also been used. Advantages and limitations of each method or approach in specific application scenarios are discussed to highlight the scientific and technological obstacles to be overcome in future research. Although progress in recent years has increased our understanding of the mechanisms and the extent to which MP/NP affects health and the environment, many challenges remain largely due to the lack of standardized and reliable detection and characterization methods. Most of the methods available today are low-throughput, which limits their practical application to food and agricultural samples. Development of rapid and high-throughput field-deployable methods for onsite screening of MP/NPs is therefore a high priority. Based on the current literature, we conclude that detecting the presence and understanding the impact of MP/NP throughout the agricultural and food supply chain require the development of novel deployable analytical methods and sensors, the combination of high-precision lab analysis with rapid onsite screening, and a data hub(s) that hosts and curates data for future analysis.

Granulocyte colony-stimulating factor promotes an aggressive phenotype of colon and breast cancer cells with biochemical changes investigated by single-cell Raman microspectroscopy and machine learning analysis.

Granulocyte colony-stimulating factor (G-CSF) is produced at high levels in several cancers and is directly linked with metastasis in gastrointestinal (GI) cancers. In order to further understand the alteration of molecular compositions and biochemical features triggered by G-CSF treatment at molecular and cell levels, we sought to investigate the long term treatment of G-CSF on colon and breast cancer cells measured by label-free, non-invasive single-cell Raman microspectroscopy. Raman spectrum captures the molecule-specific spectral signatures ("fingerprints") of different biomolecules presented on cells. In this work, mouse breast cancer line 4T1 and mouse colon cancer line CT26 were treated with G-CSF for 7 weeks and subsequently analyzed by machine learning based Raman spectroscopy and gene/cytokine expression. The principal component analysis (PCA) identified the Raman bands that most significantly changed between the control and G-CSF treated cells. Notably, here we proposed the concept of aggressiveness score, which can be derived from the posterior probability of linear discriminant analysis (LDA), for quantitative spectral analysis of tumorigenic cells. The aggressiveness score was effectively applied to analyze and differentiate the overall cell biochemical changes of G-CSF-treated two model cancer cells. All these tumorigenic progressions suggested by Raman analysis were confirmed by pro-tumorigenic cytokine and gene analysis. A high correlation between gene expression data and Raman spectra highlights that the machine learning based non-invasive Raman spectroscopy offers emerging and powerful tools to better understand the regulation mechanism of cytokines in the tumor microenvironment that could lead to the discovery of new targets for cancer therapy.

Tracking the Differentiation Status of Human Neural Stem Cells through Label-Free Raman Spectroscopy and Machine Learning-Based Analysis.

The ability to noninvasively monitor stem cells' differentiation is important to stem cell studies. Raman spectroscopy is a non-harmful imaging approach that acquires the cellular biochemical signatures. Herein, we report the first use of label-free Raman spectroscopy to characterize the gradual change during the differentiation process of live human neural stem cells (NSCs) in the in vitro cultures. Raman spectra of 600-1800 cm-1 were measured with human NSC cultures from the undifferentiated stage (NSC-predominant) to the highly differentiated one (neuron-predominant) and subsequently analyzed using various mathematical methods. Hierarchical cluster analysis distinguished two cell types (NSCs and neurons) through the spectra. The subsequently derived differentiation rate matched that measured by immunocytochemistry. The key spectral biomarkers were identified by time-dependent trend analysis and principal component analysis. Furthermore, through machine learning-based analysis, a set of eight spectral data points were found to be highly accurate in classifying cell types and predicting the differentiation rate. The predictive accuracy was the highest using the artificial neural network (ANN) and slightly lowered using the logistic regression model and linear discriminant analysis. In conclusion, label-free Raman spectroscopy with the aid of machine learning analysis can provide the noninvasive classification of cell types at the single-cell level and thus accurately track the human NSC differentiation. A set of eight spectral data points combined with the ANN method were found to be the most efficient and accurate. Establishing this non-harmful and efficient strategy will shed light on the in vivo and clinical studies of NSCs.

FEAST of biosensors: Food, environmental and agricultural sensing technologies (FEAST) in North America.

We review the challenges and opportunities for biosensor research in North America aimed to accelerate translational research. We call for platform approaches based on: i) tools that can support interoperability between food, environment and agriculture, ii) open-source tools for analytics, iii) algorithms used for data and information arbitrage, and iv) use-inspired sensor design. We summarize select mobile devices and phone-based biosensors that couple analytical systems with biosensors for improving decision support. Over 100 biosensors developed by labs in North America were analyzed, including lab-based and portable devices. The results of this literature review show that nearly one quarter of the manuscripts focused on fundamental platform development or material characterization. Among the biosensors analyzed for food (post-harvest) or environmental applications, most devices were based on optical transduction (whether a lab assay or portable device). Most biosensors for agricultural applications were based on electrochemical transduction and few utilized a mobile platform. Presently, the FEAST of biosensors has produced a wealth of opportunity but faces a famine of actionable information without a platform for analytics.

Imaging of PD-L1 in single cancer cells by SERS-based hyperspectral analysis.

We developed a hyperspectral imaging tool based on surface-enhanced Raman spectroscopy (SERS) probes to determine the expression level and visualize the distribution of PD-L1 in individual cells. Electron-microscopic analysis of PD-L1 antibody - gold nanorod conjugates demonstrated binding the cell surface and internalization into endosomal vesicles. Stimulation of cells with IFN-γ or metformin was used to confirm the ability of SERS probes to report treatment-induced changes. The multivariate curve resolution-alternating least squares (MCR-ALS) analysis of spectra provided a greater signal-noise ratio than single peak mapping. However, single peak mapping allowed a systematic subtraction of background and the removal of non-specific binding and endocytic SERS signals. The mean or maximum peak height in the cell or the mean peak height in the area of specific PD-L1 positive pixels was used to estimate the PD-L1 expression levels in single cells. The PD-L1 levels were significantly up-regulated by IFN-γ and inhibited by metformin in human lung cancer cells from the A549 cell line. In conclusion, the method of analyzing hyperspectral SERS imaging data together with systematic and comprehensive removal of non-specific signals allows SERS imaging to be a quantitative tool in the detection of the cancer biomarker, PD-L1.

Label-free discrimination and quantitative analysis of oxidative stress induced cytotoxicity and potential protection of antioxidants using Raman micro-spectroscopy and machine learning.

Diesel exhaust particles (DEPs) are major constituents of air pollution and associated with numerous oxidative stress-induced human diseases. In vitro toxicity studies are useful for developing a better understanding of species-specific in vivo conditions. Conventional in vitro assessments based on oxidative biomarkers are destructive and inefficient. In this study, Raman spectroscopy, as a non-invasive imaging tool, was used to capture the molecular fingerprints of overall cellular component responses (nucleic acid, lipids, proteins, carbohydrates) to DEP damage and antioxidant protection. We apply a novel data visualization algorithm called PHATE, which preserves both global and local structure, to display the progression of cell damage over DEP exposure time. Meanwhile, a mutual information (MI) estimator was used to identify the most informative Raman peaks associated with cytotoxicity. A health index was defined to quantitatively assess the protective effects of two antioxidants (resveratrol and mesobiliverdin IXα) against DEP induced cytotoxicity. In addition, a number of machine learning classifiers were applied to successfully discriminate different treatment groups with high accuracy. Correlations between Raman spectra and immunomodulatory cytokine and chemokine levels were evaluated. In conclusion, the combination of label-free, non-disruptive Raman micro-spectroscopy and machine learning analysis is demonstrated as a useful tool in quantitative analysis of oxidative stress induced cytotoxicity and for effectively assessing various antioxidant treatments, suggesting that this framework can serve as a high throughput platform for screening various potential antioxidants based on their effectiveness at battling the effects of air pollution on human health.

Raman Spectroscopy characterization extracellular vesicles from bovine placenta and peripheral blood mononuclear cells.

Placenta-derived extracellular vesicles (EVs) are involved in communication between the placenta and maternal immune cells possibly leading to a modulation of maternal T-cell signaling components. The ability to identify EVs in maternal blood may lead to the development of diagnostic and treatment tools for pregnancy complications. The objective of this work was to differentiate EVs from bovine placenta (trophoblast) and peripheral blood mononuclear cells (PBMC) by a label-free, non-invasive Raman spectroscopy technique. Extracellular vesicles were isolated by ultracentrifugation. Dynamic light scattering (DLS) and scanning electron microscopy (SEM) were applied to verify the presence and the size distribution of EVs. Raman peaks at 728 cm-1 (collagen) and 1573 cm-1 (protein) were observed only in PBMC-derived EVs, while the peaks 702 cm-1 (cholesterol) and 1553 cm-1 (amide) appeared only in trophoblast-derived EVs. The discrimination of the Raman spectral fingerprints for both types of EVs from different animals was performed by principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA and LDA results clearly segregated the spectral clusters between the two types of EVs. Moreover, the PBMC-derived EVs from different animals were indistinguishable, while the trophoblast-derived EVs from three placental samples of different gestational ages showed separate clusters. This study reports for the first time the Raman characteristic peaks for identification of PBMC and trophoblast-derived EVs. The development of this method also provides a potential tool for further studies investigating the causes and potential treatments for pregnancy complications.

Spatiotemporal dynamic monitoring of fatty acid-receptor interaction on single living cells by multiplexed Raman imaging.

Numerous fatty acid receptors have proven to play critical roles in normal physiology. Interactions among these receptor types and their subsequent membrane trafficking has not been fully elucidated, due in part to the lack of efficient tools to track these cellular events. In this study, we fabricated the surface-enhanced Raman scattering (SERS)-based molecular sensors for detection of two putative fatty acid receptors, G protein-coupled receptor 120 (GPR120) and cluster of differentiation 36 (CD36), in a spatiotemporal manner in single cells. These SERS probes allowed multiplex detection of GPR120 and CD36, as well as a peak that represented the cell. This multiplexed sensing system enabled the real-time monitoring of fatty acid-induced receptor activation and dynamic distributions on the cell surface, as well as tracking of the receptors' internalization processes on the addition of fatty acid. Increased SERS signals were seen in engineered HEK293 cells with higher fatty acid concentrations, while decreased responses were found in cell line TBDc1, suggesting that the endocytic process requires innate cellular components. SERS mapping results confirm that GPR120 is the primary receptor and may work synergistically with CD36 in sensing polyunsaturated fatty acids and promoting Ca2+ mobilization, further activating the process of fatty acid uptake. The ability to detect receptors' locations and monitor fatty acid-induced receptor redistribution demonstrates the specificity and potential of our multiplexed SERS imaging platform in the study of fatty acid-receptor interactions and might provide functional information for better understanding their roles in fat intake and development of fat-induced obesity.

A multi-scale approach to study biochemical and biophysical aspects of resveratrol on diesel exhaust particle-human primary lung cell interaction.

Diesel exhaust particles (DEPs) are major air pollutants that lead to numerous human disorders, especially pulmonary diseases, partly through the induction of oxidative stress. Resveratrol is a polyphenol that ameliorates the production of reactive oxygen species (ROS) and delays aging-related processes. Herein we studied the cytoprotective effect of resveratrol on DEP-exposed human lung cells in a factorial experimental design. This work investigates biophysical features including cellular compositions and biomechanical properties, which were measured at the single-cell level using confocal Raman microspectroscopy (RM) and atomic force microscopy (AFM), respectively. Principal component analysis (PCA), hierarchical cluster analysis (HCA) and partial least square regression (PLS) analysis were applied to analyze Raman spectra with and without resveratrol protection. The health status of individual cells could be effectively predicted using an index derived from characteristic Raman spectral peak (e.g., 1006 cm-1) based on PLS model. AFM measurements indicated that cellular adhesion force was greatly reduced, while Young's modulus was highly elevated in resveratrol treated DEP-exposed cells. Anti-oxidant resveratrol reduced DEP-induced ROS production and suppressed releases of several cytokines and chemokines. These findings suggest resveratrol may enhance resistance of human lung cells (e.g., SAEC) to air pollutants (e.g. DEPs).

Inkjet printed microfluidic paper-based analytical device (µPAD) for glucose colorimetric detection in artificial urine.

This article introduces a novel inkjet printing method for the fabrication of a microfluidic paper-based analytical device (µPADs) with improved analytical performance for colorimetric measurements. Firstly, a hydrophobic boundary was created by wax printing on chromatography paper. Then, chitosan (CHI), 3,3',5,5'-Tetramethylbenzidine (TMB) and enzymatic mixture solvent (glucose oxidase (GOx) and horseradish peroxidase (HRP)) were sequentially printed in the sensing zone. Polyethylene glycol (PEG6000) was mixed with the bienzymatic solution to act as an enzyme stabilizer, forming the printable ink. The resulting µPADs exhibited a linear relationship between color intensity and glucose concentration from 0.0 25 mg/ml to 0 .5mg/ml. The detectable glucose concentration was in a clinically relevant range from 0.01 mg/ml to 4 mg/ml. The limit of detection (LOD) was achieved at 0.01 mg/ml. After 60-day storage under 4 °C, the color intensity at the testing zone retained over 80% of the original intensity. In addition, a smartphone application was developed for in situ colorimetric image processing, and the colorimetric analysis results were compared with those from the use of a scanner followed by processing using ImageJ. Furthermore, the development of this ink printing method also provides a point of care (POC) platform for other substances detection purposes.

Use of Surface-Enhanced Raman Scattering (SERS) Probes to Detect Fatty Acid Receptor Activity in a Microfluidic Device.

In this study, 4-mercaptobenzoic acid (MBA)-Au nanorods conjugated with a GPR120 antibody were developed as a highly sensitive surface-enhanced Raman spectroscopy (SERS) probe, and were applied to detect the interaction of fatty acids (FA) and their cognate receptor, GPR120, on the surface of human embryonic kidney cells (HEK293-GPRR120) cultured in a polydimethylsiloxane (PDMS) microfluidic device. Importantly, the two dominant characteristic SERS peaks of the Raman reporter molecule MBA, 1078 cm-1 and 1581 cm-1, do not overlap with the main Raman peaks from the PDMS substrate when the appropriate spectral scanning range is selected, which effectively avoided the interference from the PDMS background signals. The proposed microfluidic device consisted of two parts, that is, the concentration gradient generator (CGG) and the cell culture well array. The CGG part was fabricated to deliver five concentrations of FA simultaneously. A high aspect ratio well structure was designed to address the problem of HEK cells vulnerable to shear flow. The results showed a positive correlation between the SERS peak intensity and the FA concentrations. This work, for the first time, achieved the simultaneous monitoring of the Raman spectra of cells and the responses of the receptor in the cells upon the addition of fatty acid. The development of this method also provides a platform for the monitoring of cell membrane receptors on single-cell analysis using SERS in a PDMS-based microfluidic device.

Microfluidic chip for non-invasive analysis of tumor cells interaction with anti-cancer drug doxorubicin by AFM and Raman spectroscopy.

Raman spectroscopy has been playing an increasingly significant role for cell classification. Here, we introduce a novel microfluidic chip for non-invasive Raman cell natural fingerprint collection. Traditional Raman spectroscopy measurement of the cells grown in a Polydimethylsiloxane (PDMS) based microfluidic device suffers from the background noise from the substrate materials of PDMS when intended to apply as an in vitro cell assay. To overcome this disadvantage, the current device is designed with a middle layer of PDMS layer sandwiched by two MgF2 slides which minimize the PDMS background signal in Raman measurement. Three cancer cell lines, including a human lung cancer cell A549, and human breast cancer cell lines MDA-MB-231 and MDA-MB-231/BRMS1, were cultured in this microdevice separately for a period of three days to evaluate the biocompatibility of the microfluidic system. In addition, atomic force microscopy (AFM) was used to measure the Young's modulus and adhesion force of cancer cells at single cell level. The AFM results indicated that our microchannel environment did not seem to alter the cell biomechanical properties. The biochemical responses of cancer cells exposed to anti-cancer drug doxorubicin (DOX) up to 24 h were assessed by Raman spectroscopy. Principal component analysis over the Raman spectra indicated that cancer cells untreated and treated with DOX can be distinguished. This PDMS microfluidic device offers a non-invasive and reusable tool for in vitro Raman measurement of living cells, and can be potentially applied for anti-cancer drug screening.

SERS-fluorescence bimodal nanoprobes for in vitro imaging of fatty acid responsive receptor GPR120.

G-protein-coupled receptor 120 (GPR120), as a member of the rhodopsin family of G-protein-coupled receptors, has been shown to function as a sensor for dietary fat in the gustatory and digestive systems. Its specific role in the chemoreception of fatty acids, which is thought to be crucial in understanding the mechanism surrounding the control of fat intake and, accordingly, in the treatment of obesity, remains unclear. Here we report a novel surface-enhanced Raman spectroscopy (SERS)-fluorescence bimodal microscopic technique for detection and imaging of GPR120 in single living cells. CaMoO4:Eu3+@AuNR hybrid nanoparticles are synthesized and characterized as imaging probes. Biocompatibility and imaging capability of the probes are investigated using a model HEK293 cell line with an inducible GPR120 gene transfection. Cellular distribution of GPR120 is visualized by single-cell SERS and fluorescence imaging. A dose-dependent GPR120 response to linoleic acid treatment is revealed by SERS.

Gd2O3-doped silica @ Au nanoparticles for in vitro imaging cancer biomarkers using surface-enhanced Raman scattering.

There has been an interest in developing multimodal approaches to combine the advantages of individual imaging modalities, as well as to compensate for respective weaknesses. We previously reported a composite nano-system composed of gadolinium-doped mesoporous silica nanoparticle and gold nanoparticle (Gd-Au NPs) as an efficient MRI contrast agent for in vivo cancer imaging. However, MRI lacks sensitivity and is unsuitable for in vitro cancer detection. Thus, here we performed a study to use the Gd-Au NPs for detection and imaging of a widely recognized human cancer biomarker, epidermal growth factor receptor (EGFR), in individual human cancer cells with surface-enhanced Raman scattering (SERS). The Gd-Au NPs were sequentially conjugated with a monoclonal antibody recognizing EGFR and a Raman reporter molecule, 4-meraptobenzoic acid (MBA), to generate a characteristic SERS signal at 1075cm-1. By spatially mapping the SERS intensity at 1075cm-1, cellular distribution of EGFR and its relocalization on the plasma membrane were measured in situ. In addition, the EGFR expression levels in three human cancer cell lines (S18, A431 and A549) were measured using this SERS probe, which were consistent with the comparable measurements using immunoblotting and immunofluorescence. Our SERS results show that functionalized Gd-Au NPs successfully targeted EGFR molecules in three human cancer cell lines and monitored changes in single cell EGFR distribution in situ, demonstrating its potential to study cell activity under physiological conditions. This SERS study, combined with our previous MRI study, suggests the Gd-Au nanocomposite is a promising candidate contrast agent for multimodal cancer imaging.

In vitro detection of diesel exhaust particles induced human lung carcinoma epithelial cells damage and the effect of resveratrol.

People are taking up antioxidants in their daily diet and being exposed to a potential diesel exhaust particles (DEP)-containing environment. Thus it is important to study in vitro cellular responses when cells are exposed to DEP with or without antioxidant treatment. The investigation of DEP and resveratrol (RES) on cellular biophysical and biochemical changes is needed to better understand the mechanisms of DEP and RES in mammalian cells. A combination of two non-invasive techniques (atomic force microscopy, AFM, and Raman spectroscopy, RM) and multimodal tools were applied to evaluate the biophysical, biochemical alterations and cytokine, membrane potential and cell cycle of cells with or without RES pretreatment to different times of DEP exposure. AFM results indicated that RES protected cells from DEP-induced damage to cytoskeleton and cell architectures, and noted that RES treatments also attenuated DEP-induced alterations in cell elasticity and surface adhesion force over DEP incubation time. RM monitored the changes in characteristic Raman peak intensities of DNA and protein over the DEP exposure time for both RES and non-RES treated groups. The cytokine and chemokine changes quantified by Multiplex ELISA revealed that the inflammatory responses were enhanced with the increase in DEP exposure time and that RES enhanced the expression levels of cytokine and chemokine. This work demonstrated that significant biophysical and biochemical changes in cells might be relevant to early pathological changes induced by DEP damage. Copyright © 2016 John Wiley & Sons, Ltd.

In vitro biomechanical properties, fluorescence imaging, surface-enhanced Raman spectroscopy, and photothermal therapy evaluation of luminescent functionalized CaMoO4:Eu@Au hybrid nanorods on human lung adenocarcinoma epithelial cells.

Highly dispersible Eu3+-doped CaMoO4@Au-nanorod hybrid nanoparticles (HNPs) exhibit optical properties, such as plasmon resonances in the near-infrared region at 790 nm and luminescence at 615 nm, offering multimodal capabilities: fluorescence imaging, surface-enhanced Raman spectroscopy (SERS) detection and photothermal therapy (PTT). HNPs were conjugated with a Raman reporter (4-mercaptobenzoic acid), showing a desired SERS signal (enhancement factor 5.0 × 105). The HNPs have a heat conversion efficiency of 25.6%, and a hyperthermia temperature of 42°C could be achieved by adjusting either concentration of HNPs, or laser power, or irradiation time. HNPs were modified with antibody specific to cancer biomarker epidermal growth factor receptor, then applied to human lung cancer (A549) and mouse hepatocyte cells (AML12), and in vitro PTT effect was studied. In addition, the biomechanical properties of A549 cells were quantified using atomic force microscopy. This study shows the potential applications of these HNPs in fluorescence imaging, SERS detection, and PTT with good photostability and biocompatibility.

Near-infrared photothermal therapy of Prussian-blue-functionalized lanthanide-ion-doped inorganic/plasmonic multifunctional nanostructures for the selective targeting of HER2-expressing breast cancer cells.

We report the synthesis, characterization, and application of Prussian blue (PB) functionalized CaMoO4:Eu@SiO2@Au nanorod hybrid nanoparticles (HNPs), with multimodal capabilities such as fluorescence imaging, surface-enhanced Raman spectroscopy (SERS) detection and photothermal therapy (PTT). The average size of CaMoO4:Eu@SiO2 NPs was 206 nm. The HNPs are highly dispersible in water for several weeks without settling and show a strong absorption in the near-infrared region, overlapping with the PB absorption between 600 nm and 1000 nm and the surface plasmon resonance of Au nanorods around 800 nm. Upon 808 nm laser excitation, HNPs show hyperthermia temperature (∼43 °C). Moreover, PB-functionalized NPs can be used in clinical trials for the treatment of radioactive exposure, and PB acts as a Raman reporter molecule (2152 cm-1 characteristic peak) with good biosafety and stability in the human body. In addition, coating the surface of CaMoO4:Eu NPs with both SiO2 and Au nanorods increases the biocompatibility of the HNPs. Furthermore, the PTT efficiency of human epidermal growth factor receptor 2 (HER2) antibody-conjugated HNPs on MDA-MB-435 cancerous cells was significantly higher than that of hepatocyte cells (noncancerous). This is due to the greater uptake of HNPs on cancerous cells than on noncancerous cells. Together, this study shows the potential applications of these HNPs in fluorescence imaging, SERS detection, and PTT functionalities with good photostability and biocompatibility.

Biochemical, biophysical, and genetic changes of porcine trophoblast-derived stem-like cells during differentiation as evaluated using Raman microspectroscopy, Atomic force microscopy, and quantitative polymerase chain reaction.

Porcine trophoblast-derived stem-like cells grown into serum medium start to differentiate and become senescent within 30 days. However, trophoblast-derived cells, cultured in vitro in a defined and non-serum medium, have the regenerative properties, such as indefinite passage and foreign DNA receptivity, similar to stem cells. To evaluate the biochemical, biophysical, and genetic changes of the terminal differentiation of trophoblast derived cells, Raman microspectroscopy, atomic force microscopy, and qPCR were applied. It was found that Raman spectral intensities of characteristic peaks, cell morphology, and Young's modulus can be used to distinguish differentiated and undifferentiated trophoblast cells. In addition, 17 cytoskeleton and extracellular matrix-related genes were significantly impacted by medium type (non-serum versus serum). Our findings suggest that Raman microspectroscopy and atomic force microscopy-both considered as label-free, non-invasive techniques-can be applied to distinguish differentiated trophoblast cells, and cellular biochemical information and biophysical properties can be indicative of cellular differences during cell differentiation. In addition, most of cytoskeleton-related genes exhibit similar pattern to that of Young's modulus during trophoblast cell differentiation, indicating the potential connection between cytoskeleton-related genes and cellular stiffness.