Creating an automated high-throughput flow cytometry system

This is a computer aided design (CAD) of the screening system. Credit: Genomics Institute of the Novartis Research Foundation (GNF)

Flow cytometry is a very powerful tool that has been used for decades and allows for multiparametric readouts at the single cell level within heterogeneous cell populations. However, in the context of high-throughput screening, flow cytometry is slow, low-throughput, and is not amenable to automation.

The GNF group has developed a fully automated screening system that solves this problem. The system can achieve a throughput of 50,000 wells per day, resulting in a fully automated platform that enables robust phenotypic drug discovery.

Over the past five years, this screening system has been used for a variety of drug discovery programs, across many disease areas, with many molecules advancing quickly into preclinical development and into the clinic.

Pharmaceutical companies are tasked with finding new drugs that provide real benefit to patients. A common approach is through the use of phenotypic screening. This is dependent upon the ability to establish complex, biologically relevant screening assays and run high-throughput screening campaigns. However, this kind of complex biology requires new methodologies for lead identification and screening systems like the one developed by the GNF team.

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A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery can be accessed for free at http://journals.sagepub.com/doi/full/10.1177/2472555218773086. For more information about SLAS and its journals, visit http://www.slas.org/journals.

A PDF of this article is available to credentialed media outlets upon request. Contact nhallock@slas.org.

About our Society and Journals

SLAS (Society for Laboratory Automation and Screening) is an international community of nearly 20,000 professionals and students dedicated to life sciences discovery and technology. The SLAS mission is to bring together researchers in academia, industry and government to advance life sciences discovery and technology via education, knowledge exchange and global community building.

SLAS DISCOVERY: 2016 Impact Factor 2.444. Editor-in-Chief Robert M. Campbell, Ph.D., Eli Lilly and Company, Indianapolis, IN (USA). SLAS Discovery (Advancing Life Sciences R&D) was previously published (1996-2016) as the Journal of Biomolecular Screening (JBS).

SLAS TECHNOLOGY: 2016 Impact Factor 2.850. Editor-in-Chief Edward Kai-Hua Chow, Ph.D., National University of Singapore (Singapore). SLAS Technology (Translating Life Sciences Innovation) was previously published (1996-2016) as the Journal of Laboratory Automation (JALA).

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Nan Hallock
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