Literature DB >> 32893488

Sequential Protein Capture in Multiplex Single Molecule Arrays: A Strategy for Eliminating Assay Cross-Reactivity.

Tal Gilboa1,2, Adam M Maley1,2, Alana F Ogata1,2, Connie Wu1,2, David R Walt1,2.   

Abstract

Measurements of multiple biomolecules within the same biological sample are important for many clinical applications to enable accurate disease diagnosis or classification. These disease-related biomarkers often exist at very low levels in biological fluids, necessitating ultrasensitive measurement methods. Single-molecule arrays (Simoa), a bead-based digital enzyme-linked immunosorbent assay, is the current state of the art for ultrasensitive protein detection and can detect sub-femtomolar protein concentrations, but its ability to achieve high-order multiplexing without cross-reactivity remains a challenge. Here, a sequential protein capture approach for multiplex Simoa assays is implemented to eliminate cross-reactivity between binding reagents by sequentially capturing each protein analyte and then incubating each capture bead with only its corresponding detection antibody. This strategy not only reduces cross-reactivity to background levels and significantly improves measurement accuracies, but also enables higher-order multiplexing. As a proof of concept, the sequential multiplex Simoa assay is used to measure five different cytokines in plasma samples from Coronavirus Disease 2019 (COVID-19) patients. The ultrasensitive sequential multiplex Simoa assays will enable the simultaneous measurements of multiple low-abundance analytes in a time- and cost-effective manner and will prove especially critical in many cases where sample volumes are limited.
© 2020 Wiley-VCH GmbH.

Entities:  

Keywords:  ELISA; Simoa; cross-reactivity; multiplex assay; ultrasensitive protein detection

Mesh:

Substances:

Year:  2020        PMID: 32893488      PMCID: PMC8238389          DOI: 10.1002/adhm.202001111

Source DB:  PubMed          Journal:  Adv Healthc Mater        ISSN: 2192-2640            Impact factor:   9.933


  30 in total

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