EVROS Data
Abstract/Contents
- Abstract
- Precision medicine requires highly scalable methods of multiplexed biomarker quantification that can accurately describe patient physiology. Unfortunately, contemporary molecular detection methods are generally limited to a dynamic range of sensitivity spanning just 3–4 orders of magnitude, whereas the actual physiological dynamic range of the human plasma proteome spans more than 10 orders of magnitude. Current methods rely on sample splitting and differential dilution to compensate for this mismatch, but such measures greatly limit the reproducibility and scalability that can be achieved—in particular, the effects of non-linear dilution can greatly confound the analysis of multiplexed assays. We describe here a two-pronged strategy for equalizing the signal generated by each analyte in a multiplexed panel, thereby enabling simultaneous quantification of targets spanning a wide range of concentrations. We apply our ‘EVROS’ strategy to a proximity ligation assay and demonstrate simultaneous quantification of four analytes present at concentrations spanning from low femtomolar to mid-nanomolar levels. In this initial demonstration, we achieve a dynamic range spanning seven orders of magnitude in a single 5 µl sample of undiluted human serum, highlighting the opportunity to achieve sensitive, accurate detection of diverse analytes in a highly multiplexed fashion.
Description
Type of resource | Dataset, text |
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Date modified | May 31, 2023 |
Publication date | May 11, 2023 |
Creators/Contributors
Author | Newman, Sharon | |
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Research team head | Soh, Tom |
Subjects
Subject | Biosensors > Research |
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Subject | Tuning |
Subject | protein |
Genre | Data |
Genre | Database |
Genre | Tabular data |
Genre | Data sets |
Genre | Dataset |
Genre | Databases |
Genre | Tables (data) |
Bibliographic information
Related item |
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DOI | https://doi.org/10.25740/pf688zn8684 |
Location | https://purl.stanford.edu/pf688zn8684 |
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. (2023). EVROS Data. Stanford Digital Repository. Available at https://purl.stanford.edu/pf688zn8684. https://doi.org/10.25740/pf688zn8684.
Collection
Stanford Research Data
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