Dataset Information

Semiconductor Electronic Label-Free Assay for Predictive Toxicology.

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.

SUBMITTER: Mao Y

PROVIDER: S-EPMC4846994 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Semiconductor Electronic Label-Free Assay for Predictive Toxicology.

Mao Yufei Y Shin Kyeong-Sik KS Wang Xiang X Ji Zhaoxia Z Meng Huan H Chui Chi On CO

Scientific reports 20160427

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 holi ...[more]

PMID: 27117746

Similar Datasets

Project description:OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

Dataset Information

Semiconductor Electronic Label-Free Assay for Predictive Toxicology.

Publications

Semiconductor Electronic Label-Free Assay for Predictive Toxicology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets