Automating iPSC generation to enable autologous photoreceptor cell replacement therapy

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Abstract

Background

Inherited retinal degeneration is a leading cause of incurable vision loss in the developed world. While autologous iPSC mediated photoreceptor cell replacement is theoretically possible, the lack of commercially available technologies designed to enable high throughput parallel production of patient specific therapeutics has hindered clinical translation.

Methods

In this study, we describe the use of the Cell X precision robotic cell culture platform to enable parallel production of clinical grade patient specific iPSCs. The Cell X is housed within an ISO Class 5 cGMP compliant closed aseptic isolator (Biospherix XVivo X2), where all procedures from fibroblast culture to iPSC generation, clonal expansion and retinal differentiation were performed.

Results

Patient iPSCs generated using the Cell X platform were determined to be pluripotent via score card analysis and genetically stable via karyotyping. As determined via immunostaining and confocal microscopy, iPSCs generated using the Cell X platform gave rise to retinal organoids that were indistinguishable from organoids derived from manually generated iPSCs. In addition, at 120 days post-differentiation, single-cell RNA sequencing analysis revealed that cells generated using the Cell X platform were comparable to those generated under manual conditions in a separate laboratory.

Conclusion

We have successfully developed a robotic iPSC generation platform and standard operating procedures for production of high-quality photoreceptor precursor cells that are compatible with current good manufacturing practices. This system will enable clinical grade production of iPSCs for autologous retinal cell replacement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-023-03966-2.

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Most cited references 66

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Integrated analysis of multimodal single-cell data

Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen … (2021)

Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

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Induction of pluripotent stem cells from adult human fibroblasts by defined factors.

Kazutoshi Takahashi, Koji TANABE, Mari Ohnuki … (2007)

Successful reprogramming of differentiated human somatic cells into a pluripotent state would allow creation of patient- and disease-specific stem cells. We previously reported generation of induced pluripotent stem (iPS) cells, capable of germline transmission, from mouse somatic cells by transduction of four defined transcription factors. Here, we demonstrate the generation of iPS cells from adult human dermal fibroblasts with the same four factors: Oct3/4, Sox2, Klf4, and c-Myc. Human iPS cells were similar to human embryonic stem (ES) cells in morphology, proliferation, surface antigens, gene expression, epigenetic status of pluripotent cell-specific genes, and telomerase activity. Furthermore, these cells could differentiate into cell types of the three germ layers in vitro and in teratomas. These findings demonstrate that iPS cells can be generated from adult human fibroblasts.

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Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

Kelly Street, Davide Risso, Russell B Fletcher … (2018)

Background Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Results We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. Conclusions Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression. Electronic supplementary material The online version of this article (10.1186/s12864-018-4772-0) contains supplementary material, which is available to authorized users.

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Author and article information

Contributors

Budd A. Tucker:

ORCID: http://orcid.org/0000-0003-2178-1742

budd-tucker@uiowa.edu

Journal

Journal ID (nlm-ta): J Transl Med

Journal ID (iso-abbrev): J Transl Med

Title: Journal of Translational Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1479-5876

Publication date (Electronic): 28 February 2023

Publication date PMC-release: 28 February 2023

Publication date Collection: 2023

Volume: 21

Electronic Location Identifier: 161

Affiliations

[1 ]GRID grid.214572.7, ISNI 0000 0004 1936 8294, Institute for Vision Research, Carver College of Medicine, , University of Iowa, ; 375 Newton Road, Iowa City, IA 52242 USA

[2 ]GRID grid.214572.7, ISNI 0000 0004 1936 8294, Department of Ophthalmology and Visual Sciences, Carver College of Medicine, , University of Iowa, ; Iowa City, IA USA

[3 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Department of Biomedical Informatics, , The Ohio State University, ; Columbus, OH USA

[4 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Biomedical Engineering, Lerner Research Institute, , Cleveland Clinic, ; Cleveland, OH USA

[5 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Orthopaedic Surgery, , Cleveland Clinic, ; Cleveland, OH USA

[6 ]Cell X Technologies Inc, Cleveland, OH USA

Author information

Budd A. Tucker http://orcid.org/0000-0003-2178-1742

Article

Publisher ID: 3966

DOI: 10.1186/s12967-023-03966-2

PMC ID: 9976478

PubMed ID: 36855199

SO-VID: b8891fbd-5eab-43df-a29e-929e8eec626c

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 9 December 2022

Date accepted : 3 February 2023

Funding

Funded by: National Institutes of Health

Award ID: RO1 EY033331

Award Recipient : Budd A. Tucker

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: induced pluripotent stem cells,rna sequencing,retinal differentiation,robotic cell culture,automation,cell therapy,cell manufacturing

Data availability:

ScienceOpen disciplines: Medicine

Keywords: induced pluripotent stem cells, rna sequencing, retinal differentiation, robotic cell culture, automation, cell therapy, cell manufacturing

Automating iPSC generation to enable autologous photoreceptor cell replacement therapy

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Abstract

Background

Methods

Results

Conclusion

Supplementary Information

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Regulatory T cell (Treg) therapy

Most cited references 66

Integrated analysis of multimodal single-cell data

Induction of pluripotent stem cells from adult human fibroblasts by defined factors.

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

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Article

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