Mast cells (MCs) undergo degranulation activated by various secretagogues and rapidly secrete pre-formed mediators include in secretory granules, involving in numerous progresses of immune response, hypersensitivity and carcinogenesis. Therefore, it is essential for MCs degranulation detection in vivo and in real-time, particularly the measurement of MC degranulation at single cell level. At the aim of single cell degranulation detection, we here developed a secretion-sensitizing FRET probe, designated tryptase-sensitizing probe (Tryprobe). After treated with C48/80 or trypsin, activated MCs will degranulate to release inflammatory mediators. The release of tryptase in the process of degranulation will destroy the non-fluorescent FRET system of Tryprobe, and then the fluorescence production was detected by fluorescence spectrometer or CCD imaging equipment. Curcumin–pretreated P815 cells sensitized with C48/80, the fluorescence intensity of FRET system significantly declined than the drugs stimulation groups. From single cell scale, the cell morphology is nearly unchanged when cells under the rest state without drug activation. However, after drugs stimulation, the cell morphology is becoming distorting at various degrees, some cells morphology even break in intense degranulation. The morphology change of single mast cell can be observed distinctly in bright field and green fluorescent channel. To real-time observe single mast cell degranulation, we also established Intensity-Scatter correlation for the reconstruction of degranulation to build an evaluation standard for single cell degranulation using Tryprobe detection method. Accordingly, we anticipate this Intensity-Scatter correlation method using Tryprobe system as a template to be applied to other secretions detection of single cell in the cell and molecular biological fields.
In recent years, cancer has become a common health problem faced by all mankind. Liver cancer and prostate cancer are the most common malignant tumors in the world. Early diagnosis is of great significance for the effective treatment of cancer patients and prolonging their life. Therefore, there is an urgent need to develop a powerful method to detect multiple types of cancer. In this work, we developed a surface enhanced Raman spectroscopy combined with principal component analysis (PCA) and linear discriminant analysis (LDA) multivariate statistical method to detect and screen patients with prostate cancer and liver cancer. In order to further verify the validity of PCA-LDA, the receiver operating characteristic(ROC)curve is used to evaluate the effectiveness of the algorithm. The comparison of the average spectra showed that there was a significant difference between the serum samples of patients with liver cancer and patients with prostate cancer. This may be related to the changes of molecular structure and composition of human serum caused by diseases. PCA-LDA algorithm was used to classify SERS in serum of patients with liver cancer and serum of patients with prostate cancer. The sensitivity and specificity were 100% and 90%, respectively. the receiver operating characteristic(ROC)curve shows that the area under the curve is 1. The results show that the combination of SERS and PCA-LDA algorithm has high accuracy in the discrimination and classification of liver cancer and prostate cancer, and the detection is fast and sensitive, which is a potential detection and screening method.
Mast cells (MCs) degranulation have an extremely momentous role in the progresses of immunoreaction, anaphylaxis as well as the variation of the tumor microenvironment (TME). The emergence of the substances due to MCs degranulation will arouse multiple changes of optical characteristics, such as energy transfer, fluorescence and spectra, etc. In this study, we implement the simultaneous spectral unmixing of excitation and emission by adjusting the cube filters and optical path to solely trigger the donor excitation and obtain the acceptor fluorescence emission. In addition, we add another channel to collect the real-time spectra with a portable and mobile spectroscopy equipment. Here, we developed graphene oxide (GO) and reduced GO (rGO)-based fluorescence resonance energy transfer (FRET) biosensors for MCs degranulation to verify the performance of the dual-channel system on an Inverted Fluorescence Microscope. MCs undergo degranulation can rapidly release tryptase, one proteases of the highest concentration in cytozoic pre-formed mediator. The acceptor fluorescence emission and spectra are detected simultaneously in real-time by tryptase-sensitized FRET biosensor on the dual-channel system. Moreover, the dual-channel can be switched by rapid adjusting optical channel during excitation at any moment. Results showed that the MCs degranulation could be monitoring in real-time on the dual-channel optical system. In particular, the minimal changes of the initial degranulation also could be measured with high response rate. Consequently, this dual-channel system may serve as a potential tool for the investigation of protein-protein interaction, single molecule dynamics and the working mechanism of membrane proteins using FRET-related techniques.
Combining serum albumin via adsorption-exfoliation on hydroxyapatite particles (HAp) with surface-enhanced Raman scattering (SERS), we developed a novel quantitative analysis of albumin method from blood serum for breast cancer screening applications. For adults, the normal range of serum albumin is defined as 3.5-5.0 g/dL, and the levels <3.5 g/dL is called hypoalbuminemia. The quantitatively analysis obtained by our HAp method had a good linear relationship from 1 to 10 g/dL. More importantly, the lower limit of detection was less than the albumin prognostic factor for disease (3.5 g/dL). Serum albumin was adsorbed and exfoliated by HAp from serum samples of breast cancer patients and healthy volunteers, and then mixed with silver colloids to perform SERS spectral analysis. Subtle changes in the SERS spectra of serum proteins indicated that some specific biomolecular contents and albumin secondary structures change with cancer progression. Principal component analysis (PCA), as a spectral dimensionality reduction method, combining with a linear discriminant analysis (LDA) was employed to screen and classify breast cancer. Based on the PCA-LDA algorithm, yielding the diagnostic sensitivity and specificity of breast cancer patients were 95% and 90%, respectively. This exploratory work demonstrated that HAp adsorbed-exfoliated serum proteins combined with SERS spectroscopy has great potential for label-free and non-invasive screening of breast cancer.
Independent emission-spectral unmixing fluorescence resonance energy transfer, Iem-spFRET, is a novel and powerful tool for measuring FRET efficiency in real time. In this paper, we simulate the measurement error of the Iem-spFRET by introducing random noise in sample data, donor fingerprint, and acceptor fingerprint. The random noise intensity is set from 0.0005 to 0.0025, corresponding to 5% - 25% of the maximum donor fingerprint intensity. The simulated results show the effect of random noise on apparent FRET efficiency (EfD) is less than on receptor-to-donor concentration ratio (Rc). Random noise with 10% maximum donor fingerprint intensity only leads to 0.33% variation of when the noise is added to both sample and fingerprints. These results indicate that Iem-spFRET is a robust method and could be applied on cases with weak FRET signal.
Serum proteins contain many biomarkers of diseases, such as cancer. It would be important to purify the serum proteins for disease diagnosis. In this paper, cellulose acetate (CA) membrane was employed to purify serum proteins from human serum while removing other serum constituents and exogenous substances. The purified serum proteins were mixed with silver nanoparticles for SERS measurement. A total of 42 SERS spectra were recorded from purified serum proteins obtained from 20 liver cancer patients and 22 healthy volunteers. Subtle but discernible spectral changes of the two groups could be observed in the SERS spectra. Principal components analysis (PCA) and linear discriminate analysis (LDA) algorithm were introduced to analyze the difference between the two groups. Additionally, this method is nondestructive, fast and easy to operate, which is greatly important for clinical serum sample detection. These results indicated that SERS signal of serum proteins purified with CA membrane has a good prospect in liver cancer screening.
Esophageal carcinoma is a common cancer worldwide with a high mortality. Early diagnosis and treatment is critical to reduce the mortality of esophageal cancer patients. In this work, we developed a novel method for detection of esophageal cancer by Raman spectroscopy measurements of extracellular fluid taken from esophageal tissue. The extracellular fluid samples were prepared by sliding the esophageal tissue over an aluminum plate substrate, and then the Raman spectra of the air-drying extracellular fluid samples from 10 esophageal cancer patients and 10 healthy volunteers were successfully recorded. Difference spectrum analysis combined with the assignment of Raman bands indicated that there were subtle but distinct changes between esophageal cancer and normal tissues, which could be associated with the special changes of nucleic acid, protein, lipid and other biological molecules during the process of canceration. To further investigate the diagnostic ability of extracellular fluid taken from human esophageal tissue, the spectral data was combined with multivariate analysis processes. Principal component analysis (PCA), as a spectral dimensionality reduction approach, and in conjunction with the linear discriminant analysis (LDA) algorithm, was employed to identify the esophageal cancer samples, and the diagnostic sensitivity and specificity of 90% and 80%, respectively, could be achieved for classification between normal and cancer groups. Moreover, receiver operating characteristic (ROC) curves further confirmed the effectiveness of the diagnostic algorithm based on PCA-LDA diagnostic algorithm. The results of this exploratory study demonstrated the great potential of esophageal cancer screening based on the analysis of extracellular fluid of tissue, and provided a rapid and label-free tool for clinical cancer detection.
In this article, we have studied the feasibility of using Raman spectroscopy and multivariate statistical algorithms to distinguish human hepatoma cells from normal human liver cells with the aim to explore a label-free and non-invasive method for detecting and screening hepatoma cells. High-quality Raman spectra were obtained from 50 normal liver cells (Lo2 cell line) and 50 hepatoma cells (HepG2 cell line) in the range of 500-1750 cm-1. There are significant differences in Raman spectra between normal liver cells and hepatoma cells, which indicated special changes in the content of biomolecules including nucleic acids, proteins and lipid in different cell lines. Principal component analysis (PCA) and linear discriminate analysis (LDA) were used to classify the Raman spectra obtained from hepatoma cells and normal liver cells, and the discrimination sensitivity and specificity were 98% and 100%, respectively. In addition, PCA in conjunction with support vector machine (SVM) (with a Gaussian radial basis function) was also employed to classify the same Raman spectra dataset, and the sensitivity and specificity could be improved to 100% and 100%, respectively, indicating that the classification performance of PCA-SVM is superior to that of PCA-LDA. This exploratory study demonstrated that Raman spectroscopy technique combined with multivariate statistical algorithms as a clinical cell-based biosensor has great potential for noninvasive cancer cell detection and screening.
Mast cell (MCs) researches have received worldwide attention and achieved great achievements. Degranulation of MCs is not only related to anaphylaxis, but also plays an important role in the formation and progression of tumor. The existing detection methods could not fully reflect the degree of cell degranulation. In this paper, surface-enhanced Raman scattering (SERS) was used to detect and analyze the degranulation degree of MCs treated with different concentrations of C48/80 (compound 48/80, a mast cell activator). The culture supernatants of cells treated with different concentrations of C48/80 (0 μg/mL, 2 μg/mL and 10 μg/mL) were mixed with Ag colloids and high quality SERS spectra were acquired. The assignment of SERS bands combined with differential spectra analysis indicated that biomolecules associated with cell degranulation in the C48/80 treated groups were changed compared with the control group, including a decrease in the percentage of lipid content and an increase in the relative contents of collegen and phosphatidylserine. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) diagnostic algorithms were employed to analyze and distinguish the SERS spectra of different cell degranulation groups with high sensitivity, specificity and accuracy. The larger value of the integration area under the ROC curve also suggested the greater forecast accuracy. This exploratory work demonstrates that the combination of SERS technology and PCA-LDA algorithm has great potential for developing a label-free, comprehensive and accurate method for detecting cell degranulation.
Degranulation in mast cell is usually characterized by the release of tryptase. We developed a fluorescence resonance energy transfer (FRET) probe based on graphene oxide (GO) to detect tryptase released from mast cells. The GO based FRET probe is composed of GO and a self-assembled complex of tryptase-specific recognition peptide chains labeled with isothiocyanate fluorescein. The fluorescence intensity around the mast cells increased when the mast cells were stimulated with C48/80, a kind of reagent promoting degranulation. The fluorescence distribution is inhomogenous. The fluorescence intensity was dependent on the concentration of C48/80 and the stimulation time. These results demonstrate that GO-base FRET probe could be used to study degranulation in mast cells.
Cancer cells secrete copious amounts of secretory granules, exosomes, proteases. Recently, studies reviewed that mast cells (MCs) play crucial roles in the growth, spread and metastasis of tumor. MCs are one of the earliest cell types to infiltrate developing tumors. MCs undergo degranulation in response to various stimuli and rapidly release diverse bioactive mediators, such as histamine, tryptase, serotonin, tumor necrosis factor α (TNFα), which will tremendously affect the tumor microenvironment (TME). However, the mechanisms between the secretion of MCs degranulation and tumor remain unclear. Therefore, we developed a nanobiosensor based on fluorescence resonance energy transfer (FRET) for the determination of P815 mast cells and HeLa cells by secretagogues. With the pep-FITC as an energy donor and reduced grapheme oxide (rGO) as an energy acceptor, the two parts assemble an efficient FRET biosensor through electrostatic and stacking interaction (- interaction). Sensitized secretory cells can produce tryptase which would hydrolyze the specific cleavage site of the peptide, leading to ruin FRET system and then yield intensive fluorescence (FL) recovery of quenched FITC. Results showed that P815 cells are more sensitive and intense secretory than HeLa cells owing to more amount secretory mediators of P815 can change the microenvironment and further exacerbate the degree of degranulation in return. Our findings suggest that FRET biosensor have the ability to detect the extracellular dynamics of the cancer cells microenvironment. In addition, targeting mast cells may serve as a novel therapeutic scheme for cancer treatment and that inhibiting mast cell function may lead to tumor regression.
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