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Enzyme-Linked Immunosorbent Assay01:33

Enzyme-Linked Immunosorbent Assay

In 1971, Peter Perlman and Eva Engvall developed an Enzyme-linked immunosorbent assay (ELISA or EIA). ELISA differs from western blot in that the assays are conducted in microtiter plates or in vivo rather than on an absorbent membrane.
There are many different types of ELISAs, but they all involve an antibody molecule whose constant region binds an enzyme, leaving the variable region free to bind its specific antigen.  Enzyme-substrate reaction allows the antigen to be visualized or quantified.

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Application of MassSQUIRM for Quantitative Measurements of Lysine Demethylase Activity
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An apta-aggregation based machine learning assay for rapid quantification of lysozyme through texture parameters.

Manoharan Sanjay1, Kumar Gaurav2, Maria Jesus Gonzalez-Pabon1

  • 1Biosensors and Bioanalysis Laboratory (LABB), Department of Biological Chemistry and IQUIBICEN-CONICET, Exact and Natural Sciences Faculty (FCEN), University of Buenos Aires (UBA), Buenos Aires, Argentina.

Plos One
|March 8, 2021
PubMed
Summary
This summary is machine-generated.

A new assay uses machine learning to quantify lysozyme (Lys) by analyzing aptamer aggregates. This method offers rapid detection with a low limit of detection, enabling efficient lysozyme measurement.

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Area of Science:

  • Biotechnology
  • Machine Learning Applications
  • Biosensing

Background:

  • Lysozyme (Lys) is a crucial enzyme in biological systems.
  • Accurate quantification of lysozyme is essential for various diagnostic and research applications.
  • Existing lysozyme detection methods may lack speed, sensitivity, or cost-effectiveness.

Purpose of the Study:

  • To develop a novel, rapid, and sensitive assay for lysozyme quantification.
  • To leverage aptamer aggregation properties and machine learning for biosensing.
  • To explore the potential of apta-aggregation in developing a machine learning-based lysozyme assay.

Main Methods:

  • Utilized an anti-lysozyme aptamer exhibiting aggregation upon lysozyme exposure (apta-aggregation).
  • Developed a machine learning model to analyze texture and area parameters from images of lysozyme-induced aptamer aggregates.
  • Implemented two assay sets with varying lysozyme concentrations (1-20 mM and 25-100 mM) and sample volumes (100 μl and 10 μl, respectively).

Main Results:

  • The assay demonstrated a low experimental limit of detection of 1 mM for lysozyme.
  • Achieved a rapid response time of less than 10 seconds for lysozyme quantification.
  • Machine learning analysis of aggregate morphology provided reliable quantification across different concentration ranges.

Conclusions:

  • The developed apta-aggregation assay coupled with machine learning offers a promising new method for lysozyme quantification.
  • The assay exhibits high sensitivity and speed, suitable for various applications.
  • Bioinformatics studies confirmed the optimal operating mode for the aptamer-based detection mechanism.