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Related Concept Videos

Enzyme-Linked Immunosorbent Assay01:33

Enzyme-Linked Immunosorbent Assay

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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...
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ELISA-R: an R-based method for robust ELISA data analysis.

Taru S Dutt1,2, John S Spencer1,2, Burton R Karger3

  • 1Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO, United States.

Frontiers in Immunology
|October 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new R-based method for analyzing Enzyme-linked immunosorbent assay (ELISA) data without a standard curve, crucial for complex vaccine research like tuberculosis. The open-access tool simplifies data processing and reporting for antibody detection.

Keywords:
ELISAMycobacterium lepraeMycobacterium tuberculosisantibodiescurve-fittingdata analysisendpoint titer

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Area of Science:

  • Immunology
  • Biotechnology
  • Computational Biology

Background:

  • Enzyme-linked immunosorbent assay (ELISA) is widely used for detecting antigens or antibodies.
  • Traditional ELISA analysis relies on standard curves, which are not feasible for novel antigens, such as those from complex pathogens like *Mycobacterium tuberculosis* (Mtb).
  • Existing methods for non-standard curve ELISA lack robustness, particularly in vaccine research.

Purpose of the Study:

  • To develop and present a straightforward, open-access R-based data analysis method for Enzyme-linked immunosorbent assay (ELISA).
  • To enable robust analysis of ELISA data in situations where standard curves cannot be generated, specifically for complex vaccine research.
  • To provide a tool for end-point titer determination and curve-fitting models applicable to non-standard ELISA datasets.

Main Methods:

  • Developed an R-based pipeline for direct input of ELISA instrument data.
  • Implemented data cleaning, formatting, and automated report generation functionalities.
  • Incorporated end-point titer determination and curve-fitting models for data analysis.

Main Results:

  • Successfully demonstrated a robust method for analyzing ELISA data without a standard curve.
  • The R-based method allows for direct data processing, calculation, and reporting while preserving raw data.
  • Validated the method using published data comparing anti-Mtb antibodies in vaccinated versus unvaccinated mice.

Conclusions:

  • The presented R-based method offers a significant advancement for ELISA data analysis in complex research scenarios, particularly for vaccine development against challenging pathogens.
  • This open-access tool enhances the feasibility and reliability of analyzing ELISA data when standard curves are not applicable.
  • The method facilitates accurate comparison of antibody responses, as exemplified by the anti-Mtb antibody study.