Abstract
To produce abundant cell culture samples to generate large, standardized image datasets of human induced pluripotent stem (hiPS) cells, we developed an automated workflow on a Hamilton STAR liquid handler system. This was developed specifically for culturing hiPS cell lines expressing fluorescently tagged proteins, which we have used to study the principles by which cells establish and maintain robust dynamic localization of cellular structures. This protocol includes all details for the maintenance, passage and seeding of cells, as well as Matrigel coating of 6-well plastic plates and 96-well optical-grade, glass plates. We also developed an automated image-based hiPS cell colony segmentation and feature extraction pipeline to streamline the process of predicting cell count and selecting wells with consistent morphology for high-resolution three-dimensional (3D) microscopy. The imaging samples produced with this protocol have been used to study the integrated intracellular organization and cell-to-cell variability of hiPS cells to train and develop deep learning-based label-free predictions from transmitted-light microscopy images and to develop deep learning-based generative models of single-cell organization. This protocol requires some experience with robotic equipment. However, we provide details and source code to facilitate implementation by biologists less experienced with robotics. The protocol is completed in less than 10 h with minimal human interaction. Overall, automation of our cell culture procedures increased our imaging samples’ standardization, reproducibility, scalability and consistency. It also reduced the need for stringent culturist training and eliminated culturist-to-culturist variability, both of which were previous pain points of our original manual pipeline workflow.
Key points
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This protocol describes an automated workflow for the high-throughput culture of human induced pluripotent stem cells expressing fluorescently tagged proteins, and their seeding on 96-well optical-grade, glass-bottom plates for high-quality, live-cell three-dimensional microscopy on a large scale.
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This produces large, standardized image datasets that we have used to study integrated intracellular organization and cell-to-cell variability, and to generate deep learning-based models of three-dimensional single-cell organization.
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Data availability
Source data are provided in this paper. Source data are provided with this paper.
Code availability
Workflow scripts are available from GitHub: https://github.com/AllenInstitute/aics-automated-cell-culture-workflow. Calibration, priming and initialization information can be found in the Supplementary Information file. The entire methods is also available as a python package ‘PyHamilton’ (github.com/dgretton/pyhamilton) available at https://github.com/stefangolas/ipsc-robot.
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Acknowledgements
We thank J. Burn for editing the video tutorial, A. R. Horwitz and the Allen Institute for Cell Science Team for helpful discussions. We also thank S. Golas from the Massachusetts Institute of Technology Media Lab for converting the Hamilton methods to pyHamilton code. The WTC line that we used to create our gene-edited cell lines was provided by the Bruce R. Conklin Laboratory at the Gladstone Institute and University of California, San Francisco. We acknowledge the Allen Institute for Cell Science founder, P. G. Allen, for his vision, encouragement and support.
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Contributions
Conceptualization: S.M.R., W.W., R.N.G. and N.G. Methodology: M.E.C., B.W.G., J.A., M.A.F., A.H., M.C.H., I.A.M., A.M.N., D.J.T., C.Y., R.N.G. and N.G. Software: M.J.S.-B., A.N. and B.P.W. Validation: M.E.C., B.W.G., J.A., A.B., M.A.F., A.H., M.C.H., W.L., I.A.M., A.M.N., W.J.T., D.J.T., C.Y. and N.G. Formal analysis: M.E.C., A.B., D.J.T. and C.Y. Investigation: M.E.C., B.W.G., M.C.H., W.L. and W.J.T. Resources: W.W. Data curation: M.E.C., A.B., M.A.F., A.H., M.C.H., W.L., I.A.M., A.M.N., W.J.T. and N.G. Writing — original draft: M.E.C., B.W.G., A.B., D.J.T., C.Y. and N.G. Writing — review and editing: M.E.C., B.W.G., A.B., E.M.A., M.A.F., I.A.M., A.M.N., A.N., S.M.R., E.E.S., D.J.T., C.Y., R.N.G. and N.G. Visualization: M.E.C., B.W.G., A.B., T.P.D., D.J.T., C.Y. and N.G. Supervision: S.M.R., W.W., R.N.G. and N.G. Project administration: I.A.M. and N.G.
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Nature Protocols thanks Virgile Viasnoff and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Key references using this protocol
Viana, M. P. et al. Nature 613, 345–354 (2023): https://doi.org/10.1038/s41586-022-05563-7
Donovan-Maiye, R. M. et al. PLoS Comput. Biol. 18, e1009155 (2022): https://doi.org/10.1371/journal.pcbi.1009155
Supplementary information
Supplementary Information
Supplementary Procedures and Fig. 1
Source data
Source Data Fig. 5, 6, 7 and 8
Statistical data and a link to a Quilt repository for the source image data.
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Gregor, B.W., Coston, M.E., Adams, E.M. et al. Automated human induced pluripotent stem cell culture and sample preparation for 3D live-cell microscopy. Nat Protoc 19, 565–594 (2024). https://doi.org/10.1038/s41596-023-00912-w
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DOI: https://doi.org/10.1038/s41596-023-00912-w
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