An Open-source Facs Automation System for High-throughput Cell Biology
Diane M. Wiener, Emily Huynh, Ilakkiyan Jeyakumar, Sophie Bax, Samia Sama, Joana P. Cabrera, Verina Todorova, Madhuri Vangipuram, Shivanshi Vaid, Fumitaka Otsuka, Yoshitsugu Sakai, Manuel D. Leonetti, Rafael Gómez-Sjöberg
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.
Introduction
Rapid advances in CRISPR/Cas and other genome editing technologies are transforming our ability to engineer the properties of cells. The ability to deploy these technologies at scale offers unprecedented opportunities for both research and clinical applications. For example, the large-scale generation of fluorescently-labeled cell libraries is enabling the mapping of sub-cellular localization and molecular interactions of all individual proteins encoded in the human genome [1, 2]. For clinical applications, high-throughput cell engineering enables the identification of efficient design rules for immune cell therapies [3] and the systematic optimization of therapeutic delivery vectors [4, 5]. To capitalize on these opportunities, new methods are needed to automate and accelerate the different steps required to manufacture and handle large numbers of engineered cells.
Materials and methods
We include as supplementary information a fully-detailed build guide, design files and schematics, and bill of materials such that the automation system may be replicated. The total cost of the system is ~$15,000, where the majority is the price of the robotic arm ($10,500) and machining ($3,500). The Standard Operating Procedure (SOP), software screen recording, and video of operation are included in the supplement as well. In addition, a brief summary of the design and methods is below.
Results
Case study design and workflow description
Here, we describe the development of a FACS automation platform by focusing on a specific application as a case study. OpenCell is a large-scale effort to map the localization and interactions of proteins within the human cell [1]. To this end, we use CRISPR editing to endogenously tag protein-coding genes of interest with a mNeonGreen fluorescent reporter (S1 Fig). Specifically, each engineered cell population contains a single gene tagged with the fluorescent reporter. This CRISPR engineering workflow creates an arrayed library of engineered populations expressing individual fluorescently-tagged genes, all using the same parental HEK293T cell line (S1 Fig). For each gene target, FACS is used to isolate successfully edited cells from a heterogeneous cell pool (Fig 1A–1C). This workflow encompasses the key steps common to all FACS experiments: (1) a cell suspension is loaded into the instrument (Fig 1A); (2) the fluorescence distribution of single cells is profiled from a representative fraction of that sample (Fig 1B); (3) this profile is analyzed to define a fluorescent sub-population of interest ("gating", in our case the top 1% brightest cells, Fig 1C, left); (4) this sub-population is isolated by cell sorting into a new vessel for subsequent cell culture (Fig 1C, right).
Discussion
Laboratory automation is critical to increasing the throughput, accuracy, and efficiency of experiments [17–19, 21]. Automation standardizes procedures and minimizes human error and variation between operators. Automation also reduces the hands-on time required from researchers, allowing them to focus on data analysis and interpretation. Another advantage is that the skill level required to operate instrumentation is reduced because complex decisions (for example, how to define sorting gates) can be made algorithmically. Overall, this results in increased robustness, reproducibility and data generation throughput, while also reducing the cost and time required.
Acknowledgments
We thank Marco Brun for preliminary discussions and connection to Sony Biotechnology; Rinku Jana and Don Hesler at Sony Biotechnology for championing our collaboration; and Sandy Schmid and Greg Courville for feedback on the manuscript. Figure illustrations were created with BioRender.com.
Citation: Wiener DM, Huynh E, Jeyakumar I, Bax S, Sama S, Cabrera JP, et al. (2024) An open-source FACS automation system for high-throughput cell biology. PLoS ONE 19(3): e0299402. https://doi.org/10.1371/journal.pone.0299402
Editor: Ning Cai, Beijing University of Posts and Telecommunications, CHINA
Received: April 11, 2023; Accepted: February 8, 2024; Published: March 21, 2024
Copyright: © 2024 Wiener et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the results presented in the study are available within the manuscript and its Supporting Information files, and from: https://github.com/czbiohub/2023-facs-automation-pub https://cad.onshape.com/documents/c1a3ab256e8df7a71b82db8c/w/ab65c37920d47d4a2484a277/e/421207c6e308bccf3dd4e5b1?configuration=default&renderMode=0&uiState=640b930bf24bcf207edf60a6 https://go.sonybiotechnology.com/gate-vertex.html.
Funding: The authors received no specific funding for this work.
Competing interests: The SH800S cell sorter that is part of the automation system is a commercial product sold by Sony Corporation. Fumitaka Otsuka and Yoshitsugu Sakai are employees of Sony Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299402#abstract0
