CR_quant
Abstract/Contents
- Abstract
Gold standard immunoassays depend on specific affinity reagents for accurate molecular quantification. Any cross-reactivity of affinity reagents, wherein the reagent non-specifically binds to unintended molecules, can create false positive binding signals and result in inaccurate quantification of analytes. Mitigating cross-reactivity represents one of the greatest challenges in molecular diagnostics, and remains an unsolved problem.
To instead overcome the effects of cross-reactivity, we present a mathematical framework that uses generalized binding equations and noise estimation to enable the use of multiple cross-reactive reagents for multiplexed molecular quantification. As a proof-of-concept, we experimentally demonstrate accurate quantification of a small molecule for which no specific affinity reagents are available, even at high concentrations of a cross-reactive molecule. Furthermore, this robust schema yields well-defined bounds of quantification that make it easier to assess the quality of assay results and predicts under which conditions assay performance is likely to break down. This work turns cross-reactive affinity reagents, which were previously a liability, into an asset for achieving accurate quantification of analytes.
Description
Type of resource | Dataset, text |
---|---|
Date created | [ca. September 2023] |
Publication date | November 21, 2023; November 20, 2023 |
Creators/Contributors
Author | Newman, Sharon Shin |
---|---|
Author | Hein, Linus |
Compiler | adams, Alexandra |
Advisor | Soh, H. Tom |
Subjects
Subject | affinity reagent |
---|---|
Subject | cross-reactivity |
Subject | non-specific binding |
Subject | immunoassays |
Subject | quantification |
Genre | Data |
Genre | Tabular data |
Genre | Data sets |
Genre | Dataset |
Genre | Tables (data) |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
Preferred citation
- Preferred citation
- Newman, S., Hein, L., adams, A., and Soh, H. (2023). CR_quant. Stanford Digital Repository. Available at https://purl.stanford.edu/vc403cg8437. https://doi.org/10.25740/vc403cg8437.
Collection
Stanford Research Data
View other items in this collection in SearchWorksContact information
- Contact
- newmans@stanford.edu
Also listed in
Loading usage metrics...