Home India Multidimensional Fluorescence Sensor Array for the Detection of Sialic Acids and Other...

Multidimensional Fluorescence Sensor Array for the Detection of Sialic Acids and Other Sugars

Multidimensional Fluorescence Sensor Array for the Detection of Sialic Acids and Other Sugars

🔍 Researchers have developed a fluorescence-surface-enhanced Raman scattering (SERS) dual probe for the detection of sialic acid (SA) and imaging of cell-surface glycan. The probe exhibits high sensitivity and selectivity towards SA, a key biomarker associated with various diseases.

Facts

  • 🔬 The dual probe combines fluorescence and SERS techniques to enable sensitive and specific detection of SA.
  • 🎯 The probe demonstrates a significant fluorescence response upon interaction with SA, allowing for its detection.
  • 💡 The probe’s sensitivity enables the detection of SA at low concentrations, with a wide linear range.
  • 🌟 Multivariate analysis techniques, such as linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA), were employed for data processing and analysis.
  • 📊 LDA analysis successfully discriminated between SA and other sugars based on their spectral fingerprints.
  • 🔢 HCA analysis provided a dendrogram to visualize the clustering patterns of different analytes.
  • 🔬 NMR (nuclear magnetic resonance) experiments confirmed the interaction between the dual probe and SA.

The researchers developed a dual probe that combines fluorescence and surface-enhanced Raman scattering (SERS) techniques for the detection of sialic acid (SA), a crucial biomarker associated with various diseases. The probe exhibited a strong fluorescence response upon interaction with SA, enabling its detection and imaging on cell surfaces. The sensitivity of the probe allowed for the detection of SA at low concentrations, with a wide linear range. The researchers employed multivariate analysis techniques, such as linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA), to process and analyze the collected data. LDA analysis successfully differentiated SA from other sugars based on their distinct spectral fingerprints. HCA analysis provided a dendrogram that visualized the clustering patterns of different analytes. Additionally, NMR experiments confirmed the interaction between the dual probe and SA, supporting the reliability of the detection method.

Q & A related to this research

Q: What is Sialic acid?

A: Sialic acid is a type of carbohydrate molecule that is commonly found on the surface of cells. It plays a significant role in cell recognition and signaling processes.

Q: What is a fluorescence-SERS dual probe?

A: A fluorescence-SERS dual probe is a combination of two detection techniques used in analytical chemistry. Fluorescence refers to the emission of light by a substance when it absorbs light of a specific wavelength. SERS (Surface-enhanced Raman scattering) is a spectroscopic technique that amplifies the Raman signals of molecules adsorbed on metallic surfaces. By combining fluorescence and SERS, researchers can enhance the sensitivity and selectivity of their detection methods.

Q: What is biomarker detection?

A: Biomarker detection involves identifying and measuring specific molecules or indicators in biological samples that can indicate the presence or progression of a disease or condition. These biomarkers can include proteins, nucleic acids, or other molecules that are associated with certain diseases or physiological states.

Q: What is multivariate analysis?

A: Multivariate analysis is a statistical technique used to analyze data sets with multiple variables. It examines the relationships between multiple variables simultaneously to gain insights into patterns, trends, and associations within the data.

Q: What is linear discriminant analysis?

A: Linear discriminant analysis (LDA) is a statistical technique used in pattern recognition and machine learning. It aims to find a linear combination of features that can best separate or discriminate between different classes or groups in a data set. LDA is often used in classification problems to determine which group or category a new observation belongs to based on its features.

Q: What is hierarchical clustering analysis?

A: Hierarchical clustering analysis is a method used to group similar objects or data points into clusters based on their similarities or dissimilarities. It creates a hierarchy of clusters, with smaller, more similar clusters nested within larger, less similar clusters. This analysis helps to reveal the structure and relationships within a data set and can be useful in various fields such as biology, social sciences, and data mining.

Research Paper-

Detection of Sialic Acid and Imaging of Cell-Surface Glycan Using a Fluorescence–SERS Dual Probe

  • Palash Jana
  • Sudeep Koppayithodi
  • Madhukrishnan Murali
  • Monochura Saha
  • Kaustabh Kumar Maiti*
  • Subhajit Bandyopadhyay*
Previous articleIIT Mandi researchers develop niobium pentoxide catalyst for high carbon biofuel synthesis from biomass

LEAVE A REPLY

Please enter your comment!
Please enter your name here