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Wiener DM, Huynh E, Jeyakumar I, Bax S, Sama S, Cabrera JP, Todorova V, Vangipuram M, Vaid S, Otsuka F, Sakai Y, Leonetti MD, Gómez-Sjöberg R. An open-source FACS automation system for high-throughput cell biology. PLoS One 2024; 19:e0299402. [PMID: 38512845 PMCID: PMC10956866 DOI: 10.1371/journal.pone.0299402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 02/08/2024] [Indexed: 03/23/2024] Open
Abstract
Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions ("gating") to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instrument's software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology.
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Affiliation(s)
- Diane M. Wiener
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Emily Huynh
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Ilakkiyan Jeyakumar
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Sophie Bax
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Samia Sama
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Joana P. Cabrera
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Verina Todorova
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Madhuri Vangipuram
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Shivanshi Vaid
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Fumitaka Otsuka
- Medical Business Group, Sony Corporation, San Jose, California, United States of America
| | - Yoshitsugu Sakai
- Medical Business Group, Sony Corporation, San Jose, California, United States of America
| | - Manuel D. Leonetti
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
| | - Rafael Gómez-Sjöberg
- Chan Zuckerberg Biohub–San Francisco, San Francisco, California, United States of America
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Liu Y, Zhao H, Fu B, Jiang S, Wang J, Wan Y. Mapping Cell Phenomics with Multiparametric Flow Cytometry Assays. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:272-281. [PMID: 36939758 PMCID: PMC9590532 DOI: 10.1007/s43657-021-00031-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 09/28/2021] [Accepted: 10/11/2021] [Indexed: 11/26/2022]
Abstract
Phenomics explores the complex interactions among genes, epigenetics, symbiotic microorganisms, diet, and environmental exposure based on the physical, chemical, and biological characteristics of individuals and groups. Increasingly efficient and comprehensive phenotyping techniques have been integrated into modern phenomics-related research. Multicolor flow cytometry technology provides more measurement parameters than conventional flow cytometry. Based on detailed descriptions of cell phenotypes, rare cell populations and cell subsets can be distinguished, new cell phenotypes can be discovered, and cell apoptosis characteristics can be detected, which will expand the potential of cell phenomics research. Based on the enhancements in multicolor flow cytometry hardware, software, reagents, and method design, the present review summarizes the recent advances and applications of multicolor flow cytometry in cell phenomics, illuminating the potential of applying phenomics in future studies.
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Affiliation(s)
- Yang Liu
- Biomedical Analysis Center, Army Medical University, Chongqing, 400038 China
- Chongqing Key Laboratory of Cytomics, Chongqing, 400038 China
| | - Haichu Zhao
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518055 China
| | - Boqiang Fu
- National Institute of Metrology, Beijing, 100029 China
| | - Shan Jiang
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518055 China
| | - Jing Wang
- National Institute of Metrology, Beijing, 100029 China
| | - Ying Wan
- Biomedical Analysis Center, Army Medical University, Chongqing, 400038 China
- Chongqing Key Laboratory of Cytomics, Chongqing, 400038 China
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3
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Ding M, Edwards BS. High-Throughput Flow Cytometry in Drug Discovery. SLAS DISCOVERY 2018; 23:599-602. [DOI: 10.1177/2472555218778250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Mei Ding
- Discovery Sciences, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden
| | - Bruce S. Edwards
- Department of Pathology, Center for Molecular Discovery, University of New Mexico, Albuquerque, NM, USA
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Joslin J, Gilligan J, Anderson P, Garcia C, Sharif O, Hampton J, Cohen S, King M, Zhou B, Jiang S, Trussell C, Dunn R, Fathman JW, Snead JL, Boitano AE, Nguyen T, Conner M, Cooke M, Harris J, Ainscow E, Zhou Y, Shaw C, Sipes D, Mainquist J, Lesley S. A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery. SLAS DISCOVERY 2018; 23:697-707. [PMID: 29843542 PMCID: PMC6055113 DOI: 10.1177/2472555218773086] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The goal of high-throughput screening is to enable screening of compound libraries in an automated manner to identify quality starting points for optimization. This often involves screening a large diversity of compounds in an assay that preserves a connection to the disease pathology. Phenotypic screening is a powerful tool for drug identification, in that assays can be run without prior understanding of the target and with primary cells that closely mimic the therapeutic setting. Advanced automation and high-content imaging have enabled many complex assays, but these are still relatively slow and low throughput. To address this limitation, we have developed an automated workflow that is dedicated to processing complex phenotypic assays for flow cytometry. 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 5 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. This report will highlight a diversity of approaches that automated flow cytometry has enabled for phenotypic drug discovery.
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Affiliation(s)
- John Joslin
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - James Gilligan
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Paul Anderson
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Catherine Garcia
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Orzala Sharif
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Janice Hampton
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Steven Cohen
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Miranda King
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Bin Zhou
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Shumei Jiang
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | | | - Robert Dunn
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - John W Fathman
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Jennifer L Snead
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Anthony E Boitano
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Tommy Nguyen
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Michael Conner
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Mike Cooke
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Jennifer Harris
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Ed Ainscow
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Yingyao Zhou
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Chris Shaw
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Dan Sipes
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - James Mainquist
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Scott Lesley
- 1 Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
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