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Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity
Antibody responses serve as the primary protection against SARS-CoV-2 infection through neutralization of viral entry into cells. We have developed a two-dimensional multiplex bead binding assay (2D-MBBA) that quantifies multiple antibody isotypes against multiple antigens from a single measurement....
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351774/ https://www.ncbi.nlm.nih.gov/pubmed/34373852 http://dx.doi.org/10.1101/2021.08.03.454782 |
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author | Koide, Akiko Panchenko, Tatyana Wang, Chan Thannickal, Sara A. Romero, Larizbeth A. Teng, Kai Wen Li, Francesca-Zhoufan Akkappedi, Padma Corrado, Alexis D. Caro, Jessica Diefenbach, Catherine Samanovic, Marie I. Mulligan, Mark J. Hattori, Takamitsu Stapleford, Kenneth A. Li, Huilin Koide, Shohei |
author_facet | Koide, Akiko Panchenko, Tatyana Wang, Chan Thannickal, Sara A. Romero, Larizbeth A. Teng, Kai Wen Li, Francesca-Zhoufan Akkappedi, Padma Corrado, Alexis D. Caro, Jessica Diefenbach, Catherine Samanovic, Marie I. Mulligan, Mark J. Hattori, Takamitsu Stapleford, Kenneth A. Li, Huilin Koide, Shohei |
author_sort | Koide, Akiko |
collection | PubMed |
description | Antibody responses serve as the primary protection against SARS-CoV-2 infection through neutralization of viral entry into cells. We have developed a two-dimensional multiplex bead binding assay (2D-MBBA) that quantifies multiple antibody isotypes against multiple antigens from a single measurement. Here, we applied our assay to profile IgG, IgM and IgA levels against the spike antigen, its receptor-binding domain and natural and designed mutants. Machine learning algorithms trained on the 2D-MBBA data substantially improve the prediction of neutralization capacity against the authentic SARS-CoV-2 virus of serum samples of convalescent patients. The algorithms also helped identify a set of antibody isotype–antigen datasets that contributed to the prediction, which included those targeting regions outside the receptor-binding interface of the spike protein. We applied the assay to profile samples from vaccinated, immune-compromised patients, which revealed differences in the antibody profiles between convalescent and vaccinated samples. Our approach can rapidly provide deep antibody profiles and neutralization prediction from essentially a drop of blood without the need of BSL-3 access and provides insights into the nature of neutralizing antibodies. It may be further developed for evaluating neutralizing capacity for new variants and future pathogens. |
format | Online Article Text |
id | pubmed-8351774 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-83517742021-08-10 Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity Koide, Akiko Panchenko, Tatyana Wang, Chan Thannickal, Sara A. Romero, Larizbeth A. Teng, Kai Wen Li, Francesca-Zhoufan Akkappedi, Padma Corrado, Alexis D. Caro, Jessica Diefenbach, Catherine Samanovic, Marie I. Mulligan, Mark J. Hattori, Takamitsu Stapleford, Kenneth A. Li, Huilin Koide, Shohei bioRxiv Article Antibody responses serve as the primary protection against SARS-CoV-2 infection through neutralization of viral entry into cells. We have developed a two-dimensional multiplex bead binding assay (2D-MBBA) that quantifies multiple antibody isotypes against multiple antigens from a single measurement. Here, we applied our assay to profile IgG, IgM and IgA levels against the spike antigen, its receptor-binding domain and natural and designed mutants. Machine learning algorithms trained on the 2D-MBBA data substantially improve the prediction of neutralization capacity against the authentic SARS-CoV-2 virus of serum samples of convalescent patients. The algorithms also helped identify a set of antibody isotype–antigen datasets that contributed to the prediction, which included those targeting regions outside the receptor-binding interface of the spike protein. We applied the assay to profile samples from vaccinated, immune-compromised patients, which revealed differences in the antibody profiles between convalescent and vaccinated samples. Our approach can rapidly provide deep antibody profiles and neutralization prediction from essentially a drop of blood without the need of BSL-3 access and provides insights into the nature of neutralizing antibodies. It may be further developed for evaluating neutralizing capacity for new variants and future pathogens. Cold Spring Harbor Laboratory 2021-08-04 /pmc/articles/PMC8351774/ /pubmed/34373852 http://dx.doi.org/10.1101/2021.08.03.454782 Text en https://creativecommons.org/licenses/by-nd/4.0/This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Koide, Akiko Panchenko, Tatyana Wang, Chan Thannickal, Sara A. Romero, Larizbeth A. Teng, Kai Wen Li, Francesca-Zhoufan Akkappedi, Padma Corrado, Alexis D. Caro, Jessica Diefenbach, Catherine Samanovic, Marie I. Mulligan, Mark J. Hattori, Takamitsu Stapleford, Kenneth A. Li, Huilin Koide, Shohei Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity |
title | Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity |
title_full | Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity |
title_fullStr | Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity |
title_full_unstemmed | Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity |
title_short | Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity |
title_sort | two-dimensional multiplexed assay for rapid and deep sars-cov-2 serology profiling and for machine learning prediction of neutralization capacity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351774/ https://www.ncbi.nlm.nih.gov/pubmed/34373852 http://dx.doi.org/10.1101/2021.08.03.454782 |
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