Browse
Submit Data
Databases
API
Help

Dataset Information

18 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Optimization of Surface-Enhanced Raman Spectroscopy Detection Conditions for Interaction between Gonyautoxin and Its Aptamer.

ABSTRACT: This study aimed to optimize the detection conditions for surface-enhanced Raman spectroscopy (SERS) of single-stranded DNA (ssDNA) in four different buffers and explore the interaction between gonyautoxin (GTX1/4) and its aptamer, GO18. The influence of the silver colloid solution and MgSO₄ concentration (0.01 M) added under four different buffered conditions on DNA SERS detection was studied to determine the optimum detection conditions. We explored the interaction between GTX1/4 and GO18 under the same conditions as those in the systematic evolution of ligands by exponential enrichment technique, using Tris-HCl as the buffer. The characteristic peaks of GO18 and its G-quadruplex were detected in four different buffer solutions. The change in peak intensity at 1656 cm^-1 confirmed that the binding site between GTX1/4 and GO18 was in the G-quadruplex plane. The relative intensity of the peak at 1656 cm^-1 was selected for the GTX1/4-GO18 complex (I₁₆₅₆/I₁₀₉₉) to plot the ratio of GTX1/4 in the Tris-HCl buffer condition (including 30 μL of silver colloid solution and 2 μL of MgSO₄), and a linear relationship was obtained as follows: Y = 0.1867X + 1.2205 (R² = 0.9239). This study provides a basis for subsequent application of SERS in the detection of ssDNA, as well as the binding of small toxins and aptamers.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8779825 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Surface Enhanced Raman Spectroscopy for Single Molecule Protein Detection.

Project description:A two-step process of protein detection at a single molecule level using SERS was developed as a proof-of-concept platform for medical diagnostics. First, a protein molecule was bound to a linker in the bulk solution and then this adduct was chemically reacted with the SERS substrate. Traut's Reagent (TR) was used to thiolate Bovine serum albumin (BSA) in solution followed by chemical cross linking to a gold surface through a sulfhydryl group. A Glycine-TR adduct was used as a control sample to identify the protein contribution to the SER spectra. Gold SERS substrates were manufactured by electrochemical deposition. Solutions at an ultralow concentration were used for attaching the TR adducts to the SERS substrate. Samples showed the typical behavior of a single molecule SERS including spectral fluctuations, blinking and Raman signal being generated from only selected points on the substrate. The fluctuating SER spectra were examined using Principle Component Analysis. This unsupervised statistics allowed for the selecting of spectral contribution from protein moiety indicating that the method was capable of detecting a single protein molecule. Thus we have demonstrated, that the developed two-step methodology has the potential as a new platform for medical diagnostics.

| S-EPMC6710251 | biostudies-literature

Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection.

Project description:Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.

| S-EPMC9600112 | biostudies-literature

Surface-enhanced Raman spectroscopy based quantitative bioassay on aptamer-functionalized nanopillars using large-area Raman mapping.

Project description:Surface-enhanced Raman spectroscopy (SERS) has been used in a variety of biological applications due to its high sensitivity and specificity. Here, we report a SERS-based biosensing approach for quantitative detection of biomolecules. A SERS substrate bearing gold-decorated silicon nanopillars is functionalized with aptamers for sensitive and specific detection of target molecules. In this study, TAMRA-labeled vasopressin molecules in the picomolar regime (1 pM to 1 nM) are specifically captured by aptamers on the nanostructured SERS substrate and monitored by using an automated SERS signal mapping technique. From the experimental results, we show concentration-dependent SERS responses in the picomolar range by integrating SERS signal intensities over a scanning area. It is also noted that our signal mapping approach significantly improves statistical reproducibility and accounts for spot-to-spot variation in conventional SERS quantification. Furthermore, we have developed an analytical model capable of predicting experimental intensity distributions on the substrates for reliable quantification of biomolecules. Lastly, we have calculated the minimum needed area of Raman mapping for efficient and reliable analysis of each measurement. Combining our SERS mapping analysis with an aptamer-functionalized nanopillar substrate is found to be extremely efficient for detection of low-abundance biomolecules.

| S-EPMC3915935 | biostudies-literature

Designing dendronic-Raman markers for sensitive detection using surface-enhanced Raman spectroscopy.

Project description:Surface-Enhanced Raman Spectroscopy (SERS) is well-established as a tool for bio-diagnostics but is often limited by analyte sensitivity and the need for specialized substrates. Signal enhancement can be achieved by attaching multiple Raman markers to a single analyte. Dendronic frameworks with multiple Raman markers attached to the periphery offer an opportunity to examine this idea. In this article, dendrons with thiophenol groups on their periphery were synthesized and tested as a SERS analyte. For this study, simple gold nanoparticles (∼60 nm) were used as a substrate. A 102 fold enhancement in detection was observed upon going from a mono-thiophenol (MT) to a tetra-thiophenol (TT). Dendronic Raman markers increased the probability of SERS occurrence at lower concentrations when compared to a single Raman active molecule. This strategy extends the applicability of SERS, as these analyte molecules can be just mixed or drop-casted on any kind of SERS substrate.

| S-EPMC9071040 | biostudies-literature

Optimization of laser deposited silver nanoparticle substrates for surface-enhanced raman spectroscopy.

Project description:Creating sensitive and reproducible substrates for surface-enhanced Raman spectroscopy (SERS) has been a challenge in recent years. While SERS offers significant benefits over traditional Raman spectroscopy, certain hindrances have limited their commercial use, especially in settings where low limits of detection are necessary. We studied a variety of laser-deposited silver microstructured SERS substrates with different morphology as a means to optimize analyte detection. We found that using a 405 nm laser to deposit lines of silver nanoparticles (AgNPS) from a 2 mM silver nitrate and sodium citrate solution offered not only the best enhancement, but also the most consistent and reproducible substrates. We also found that the probability of deposition by laser was wavelength dependent and that longer wavelengths were less likely to deposit than shorter wavelengths. This work offers a better understanding of the laser deposition process as well as how substrate shape and structure effect SERS signals.

| S-EPMC9794414 | biostudies-literature

Nanofluidic biosensing for beta-amyloid detection using surface enhanced Raman spectroscopy.

Project description:Trace detection of the conformational transition of beta-amyloid peptide (Abeta) from a predominantly alpha-helical structure to beta-sheet could have a large impact in understanding and diagnosing Alzheimer's disease. We demonstrate how a novel nanofluidic biosensor using a controlled, reproducible surface enhanced Raman spectroscopy active site was developed to observe Abeta in different conformational states during the Abeta self-assembly process as well as to distinguish Abeta from confounder proteins commonly found in cerebral spinal fluid.

| S-EPMC2587305 | biostudies-literature

Detection of ethyl carbamate in liquors using surface-enhanced Raman spectroscopy.

Project description:Ethyl carbamate (EC), a potential carcinogen, can be formed during the fermentation and storage of alcoholic beverages. In this work, quantitative detection of EC in alcoholic beverages by using surface-enhanced Raman spectroscopy (SERS) is reported. Flower-shaped silver nanostructure substrates and silver nanocube substrates were prepared and employed as SERS platform. Flower-like silver substrates had better Raman enhancement effect on EC and were selected for further EC detection. In EC SERS spectra based on flower-shaped silver substrates, the strongest and reproducible characteristic band at 857 cm-1 was chosen for establishing a linear regression model in the concentrations ranging from 10-5 to 10-9 M, which effectively extended the application scope of the quantitative model for determination EC. Furthermore, a real alcoholic beverage was tested to verify the feasibility and reliability of the method.

| S-EPMC6304119 | biostudies-literature

Rapid Detection of Bacteria from Blood with Surface-Enhanced Raman Spectroscopy.

Project description:Traditional methods for identifying pathogens in bacteremic patients are slow (24-48+ h). This can lead to physicians making treatment decisions based on an incomplete diagnosis and potentially increasing the patient's mortality risk. To decrease time to diagnosis, we have developed a novel technology that can recover viable bacteria directly from whole blood and identify them in less than 7 h. Our technology combines a sample preparation process with surface-enhanced Raman spectroscopy (SERS). The sample preparation process enriches viable microorganisms from 10 mL of whole blood into a 200 μL aliquot. After a short incubation period, SERS is used to identify the microorganisms. We further demonstrated that SERS can be used as a broad detection method, as it identified a model set of 17 clinical blood culture isolates and microbial reference strains with 100% identification agreement. By applying the integrated technology of sample preparation and SERS to spiked whole blood samples, we were able to correctly identify both Staphylococcus aureus and Escherichia coli 97% of the time with 97% specificity and 88% sensitivity.

| S-EPMC4988670 | biostudies-literature

Digital surface enhanced Raman spectroscopy for quantifiable single molecule detection in flow.

Project description:Surface enhanced Raman scattering (SERS) provides a label free method of analyzing molecules from diverse and complex signals, potentially with single molecule sensitivity. The chemical specificity inherent in the SERS spectrum can identify molecules; however signal variability arising from the diversity of plasmonic environments can limit quantification, particularly at low concentrations. Here we show that digitizing, or counting SERS events, can decrease the limit of detection in flowing solutions enabling quantification of single molecules. By using multivariate curve resolution and establishing a score threshold, each individual spectrum can be classified as containing an event or not. This binary "yes/no" can then be quantified, and a linear region can be established. This method was shown to lower the limit of detection to the lowest physical limit, and lowered the limit of detection by an order of magnitude from the traditional, intensity based LOD calculations.

| S-EPMC11229883 | biostudies-literature

Surface enhanced Raman spectroscopy detection of biomolecules using EBL fabricated nanostructured substrates.

Project description:Fabrication and characterization of conjugate nano-biological systems interfacing metallic nanostructures on solid supports with immobilized biomolecules is reported. The entire sequence of relevant experimental steps is described, involving the fabrication of nanostructured substrates using electron beam lithography, immobilization of biomolecules on the substrates, and their characterization utilizing surface-enhanced Raman spectroscopy (SERS). Three different designs of nano-biological systems are employed, including protein A, glucose binding protein, and a dopamine binding DNA aptamer. In the latter two cases, the binding of respective ligands, D-glucose and dopamine, is also included. The three kinds of biomolecules are immobilized on nanostructured substrates by different methods, and the results of SERS imaging are reported. The capabilities of SERS to detect vibrational modes from surface-immobilized proteins, as well as to capture the protein-ligand and aptamer-ligand binding are demonstrated. The results also illustrate the influence of the surface nanostructure geometry, biomolecules immobilization strategy, Raman activity of the molecules and presence or absence of the ligand binding on the SERS spectra acquired.

| S-EPMC4401373 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data