SiMCA Data
Abstract/Contents
- Abstract
- Enzyme-linked immunosorbent assays (ELISAs) are a cornerstone of modern molecular detection, but the technique still suffers some notable challenges. One of the biggest problems is discriminating true signal generated by target molecules versus non-specific background arising from the interaction of detection antibodies with the assay substrate or interferents in the sample matrix. Our Single-Molecule Colocalization Assay (SiMCA) overcomes this problem by employing total internal reflection fluorescence (TIRF) microscopy to quantify target proteins based on the colocalization of fluorescent signal from orthogonally labeled capture and detection antibodies. By specifically counting colocalized fluorescent signals, we can essentially eliminate the confounding effects of background produced by non-specific binding of detection antibodies. We further employed a normalization strategy to account for the heterogeneous distribution of the capture antibodies, greatly improving the reproducibility of our measurements. In a series of experiments with TNF-α, we show that SiMCA can achieve a three-fold lower limit of detection compared to conventional single-color assays using the same antibodies and exhibits consistent performance for assays performed in complex specimens such as chicken serum and human blood. Our results help define the pernicious effects of non-specific background in immunoassays and demonstrate the diagnostic gains that can be achieved by eliminating those effects.
Description
| Type of resource | still image, Dataset |
|---|---|
| Date created | [ca. June 2019 - 2021] |
| Date modified | July 9, 2022; December 5, 2022 |
| Publication date | September 28, 2022 |
Creators/Contributors
| Author | Hariri, Amani |
|---|---|
| Author | Newman, Sharon |
| Author | Tan, Steven |
| Research team head | Dunn, Alex |
| Research team head | Soh, H. Tom |
Subjects
| Subject | single molecule |
|---|---|
| Subject | diagnostics |
| Subject | ELISA |
| Subject | Blood |
| Subject | tirf |
| Genre | Image |
| Genre | Data |
| Genre | Image |
| Genre | Data sets |
| Genre | Dataset |
Bibliographic information
| Location | |
|---|---|
| Related item |
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| DOI | https://doi.org/10.25740/bc494tq1762 |
| Location | https://purl.stanford.edu/bc494tq1762 |
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
- Hariri, A., Newman, S., Tan, S., Dunn, A., and Soh, H. (2022). SiMCA Data. Stanford Digital Repository. Available at https://purl.stanford.edu/bc494tq1762
Collection
Stanford Research Data
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