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Semiconductor Electronic Label-Free Assay for Predictive Toxicology
While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they also have spawned many logistical concerns in providing toxicity screening for copious new materials. Their prioritization is premised on performing cellular-level screening in vitro. Among the screening assay...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4846994/ https://www.ncbi.nlm.nih.gov/pubmed/27117746 http://dx.doi.org/10.1038/srep24982 |
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author | Mao, Yufei Shin, Kyeong-Sik Wang, Xiang Ji, Zhaoxia Meng, Huan Chui, Chi On |
author_facet | Mao, Yufei Shin, Kyeong-Sik Wang, Xiang Ji, Zhaoxia Meng, Huan Chui, Chi On |
author_sort | Mao, Yufei |
collection | PubMed |
description | While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they also have spawned many logistical concerns in providing toxicity screening for copious new materials. Their prioritization is premised on performing cellular-level screening in vitro. Among the screening assays, secretomic assay with high sensitivity, analytical throughput, and simplicity is of prime importance. Here, we build on the over 3-decade-long progress on transistor biosensing and develop the holistic assay platform and procedure called semiconductor electronic label-free assay (SELFA). We demonstrate that SELFA, which incorporates an amplifying nanowire field-effect transistor biosensor, is able to offer superior sensitivity, similar selectivity, and shorter turnaround time compared to standard enzyme-linked immunosorbent assay (ELISA). We deploy SELFA secretomics to predict the inflammatory potential of eleven engineered nanomaterials in vitro, and validate the results with confocal microscopy in vitro and confirmatory animal experiment in vivo. This work provides a foundation for high-sensitivity label-free assay utility in predictive toxicology. |
format | Online Article Text |
id | pubmed-4846994 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-48469942016-05-04 Semiconductor Electronic Label-Free Assay for Predictive Toxicology Mao, Yufei Shin, Kyeong-Sik Wang, Xiang Ji, Zhaoxia Meng, Huan Chui, Chi On Sci Rep Article While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they also have spawned many logistical concerns in providing toxicity screening for copious new materials. Their prioritization is premised on performing cellular-level screening in vitro. Among the screening assays, secretomic assay with high sensitivity, analytical throughput, and simplicity is of prime importance. Here, we build on the over 3-decade-long progress on transistor biosensing and develop the holistic assay platform and procedure called semiconductor electronic label-free assay (SELFA). We demonstrate that SELFA, which incorporates an amplifying nanowire field-effect transistor biosensor, is able to offer superior sensitivity, similar selectivity, and shorter turnaround time compared to standard enzyme-linked immunosorbent assay (ELISA). We deploy SELFA secretomics to predict the inflammatory potential of eleven engineered nanomaterials in vitro, and validate the results with confocal microscopy in vitro and confirmatory animal experiment in vivo. This work provides a foundation for high-sensitivity label-free assay utility in predictive toxicology. Nature Publishing Group 2016-04-27 /pmc/articles/PMC4846994/ /pubmed/27117746 http://dx.doi.org/10.1038/srep24982 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Mao, Yufei Shin, Kyeong-Sik Wang, Xiang Ji, Zhaoxia Meng, Huan Chui, Chi On Semiconductor Electronic Label-Free Assay for Predictive Toxicology |
title | Semiconductor Electronic Label-Free Assay for Predictive Toxicology |
title_full | Semiconductor Electronic Label-Free Assay for Predictive Toxicology |
title_fullStr | Semiconductor Electronic Label-Free Assay for Predictive Toxicology |
title_full_unstemmed | Semiconductor Electronic Label-Free Assay for Predictive Toxicology |
title_short | Semiconductor Electronic Label-Free Assay for Predictive Toxicology |
title_sort | semiconductor electronic label-free assay for predictive toxicology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4846994/ https://www.ncbi.nlm.nih.gov/pubmed/27117746 http://dx.doi.org/10.1038/srep24982 |
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