Semiconductor Electronic Label-Free Assay for Predictive Toxicology.

Yufei Mao,Kyeong-Sik Shin,Zhaoxia Ji,Huan Meng,Xiang Wang,Chi On Chui

doi:10.1038/srep24982

Abstract

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.

Highlights

While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they have spawned many logistical concerns in providing toxicity screening for copious new materials
The low output signal level of an field-effect transistor (FET) sensor can be readily boosted in the electrical domain, yet such amplification should occur as close as possible to the sensor in order to minimize the noise induced by parasitic components[19]
We have proposed and demonstrated in this work a label-free, high-sensitivity biomolecular assay platform, semiconductor electronic label-free assay (SELFA), based on our amplifying T-nwFET biosensor

Summary

Introduction

While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they have spawned many logistical concerns in providing toxicity screening for copious new materials. While assessments have been performed in an observational-based descriptive toxicological fashion using animals, same results can, in our opinion, be obtained more effectively in a predictive manner with target-specific and mechanism-based biological observations[1,2,3,4,5,6] This is in agreement with the vision of the National Academy of Sciences (NAS), which advocates a paradigm shift in the approach to toxicological testing in the 21st century, including the development of robust scientific platforms for screening a large number of toxicants at the cellular level[1]. The data of secretomic assays are utilized to rank material toxicity in vitro and predict their toxicity in vivo, offering great value in planning and prioritizing the expensive and time-consuming animal experiments for validation purpose Most biomolecular assays, such as the enzyme-linked immunosorbent assay (ELISA), are labeling-based, have limited analytical throughput and/or sensitivity, involve tedious experimental procedures, and necessitate costly infrastructure, reagents, and trained personnel. Label-free FET immunoassays have not been meaningfully adopted and impacted the biomedical and pharmaceutical industry even after over 3 decades of intense research

Methods

Results

Conclusion