A scalable and cGMP-compatible autologous organotypic cell therapy for Dystrophic Epidermolysis Bullosa

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Abstract

We present Dystrophic Epidermolysis Bullosa Cell Therapy (DEBCT), a scalable platform producing autologous organotypic iPS cell-derived induced skin composite (iSC) grafts for definitive treatment. Clinical-grade manufacturing integrates CRISPR-mediated genetic correction with reprogramming into one step, accelerating derivation of COL7A1-edited iPS cells from patients. Differentiation into epidermal, dermal and melanocyte progenitors is followed by CD49f-enrichment, minimizing maturation heterogeneity. Mouse xenografting of iSCs from four patients with different mutations demonstrates disease modifying activity at 1 month. Next-generation sequencing, biodistribution and tumorigenicity assays establish a favorable safety profile at 1-9 months. Single cell transcriptomics reveals that iSCs are composed of the major skin cell lineages and include prominent holoclone stem cell-like signatures of keratinocytes, and the recently described Gibbin-dependent signature of fibroblasts. The latter correlates with enhanced graftability of iSCs. In conclusion, DEBCT overcomes manufacturing and safety roadblocks and establishes a reproducible, safe, and cGMP-compatible therapeutic approach to heal lesions of DEB patients.

Abstract

Dystrophic Epidermolysis Bullosa is an uncurable monogenetic skin disease. Here, Neumayer et al. develop a cGMP-compatible CRISPR- and iPS cell-based approach that produces gene-corrected, autologous skin composite grafts for definitive treatment.

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Most cited references 70

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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Author and article information

Contributors

Marius Wernig:

ORCID: http://orcid.org/0000-0002-5309-515X

wernig@stanford.edu

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 11 July 2024

Publication date PMC-release: 11 July 2024

Publication date Collection: 2024

Volume: 15

Electronic Location Identifier: 5834

Affiliations

[1 ]GRID grid.168010.e, ISNI 0000000419368956, Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, , School of Medicine, ; Stanford, CA USA

[2 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Pathology, Stanford University, , School of Medicine, ; Stanford, CA USA

[3 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Dermatology—Program in Epithelial Biology, Stanford University, , School of Medicine, ; Stanford, CA USA

[4 ]GRID grid.168010.e, ISNI 0000000419368956, Center for Definitive and Curative Medicine, Stanford University, , School of Medicine, ; Stanford, CA USA

[5 ]European Molecular Biology Laboratory, Genome Biology Unit, ( https://ror.org/03mstc592) Heidelberg, Germany

[6 ]GRID grid.419047.f, ISNI 0000 0000 9894 9337, Thermo Fisher Scientific, Life Sciences Solutions Group, Cell Biology, , Research and Development, ; Frederick, MD USA

[7 ]Department of Dermatology, Columbia University, ( https://ror.org/00hj8s172) New York, NY USA

[8 ]St. John’s Institute of Dermatology, King’s College London, ( https://ror.org/0220mzb33) London, UK

[9 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Genetics, Stanford University, , School of Medicine, ; Stanford, CA USA

[10 ]GRID grid.168010.e, ISNI 0000000419368956, Stanford Genome Technology Center, Stanford University, , School of Medicine, ; Stanford, CA USA

[11 ]I Peace Inc., Palo Alto, CA USA

[12 ]GRID grid.430503.1, ISNI 0000 0001 0703 675X, Department of Dermatology, , University of Colorado School of Medicine, Anschutz Medical Campus, ; Aurora, CO USA

[13 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Chemical and Systems Biology, Stanford University, , School of Medicine, ; Stanford, CA USA

Author information

Jessica L. Torkelson http://orcid.org/0000-0002-1569-803X

Shengdi Li http://orcid.org/0000-0001-8993-3525

Madhuri Vangipuram http://orcid.org/0000-0001-7271-2689

Marius M. Mader http://orcid.org/0000-0002-7467-9672

Joanna Jacków-Malinowska http://orcid.org/0000-0002-5744-7896

Avina Rami http://orcid.org/0000-0002-5509-6033

Alberto Pappalardo http://orcid.org/0000-0002-8949-2552

Kevin R. Roy http://orcid.org/0000-0001-8799-6207

Patrick S. McGrath http://orcid.org/0000-0002-1498-371X

Dennis Roop http://orcid.org/0000-0001-5868-5880

Lars M. Steinmetz http://orcid.org/0000-0002-3962-2865

Marius Wernig http://orcid.org/0000-0002-5309-515X

Anthony E. Oro http://orcid.org/0000-0002-6261-138X

Article

Publisher ID: 49400

DOI: 10.1038/s41467-024-49400-z

PMC ID: 11239819

PubMed ID: 38992003

SO-VID: 7cbecfae-56f6-4315-add9-ed8a95ced082

License:

Open Access This 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/.

History

Date received : 28 March 2023

Date accepted : 25 May 2024

Funding

Funded by: FundRef https://doi.org/10.13039/100000900, California Institute for Regenerative Medicine (CIRM);

Award ID: TRAN1-10416

Award ID: DISC2-12590

Award Recipient : Marius Wernig

Funded by: FundRef https://doi.org/10.13039/100000009, Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.);

Award ID: ARO73170

Award Recipient : Marius Wernig

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ScienceOpen disciplines: Uncategorized

Keywords: stem-cell research,reprogramming,regenerative medicine

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ScienceOpen disciplines: Uncategorized

Keywords: stem-cell research, reprogramming, regenerative medicine

A scalable and cGMP-compatible autologous organotypic cell therapy for Dystrophic Epidermolysis Bullosa

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Most cited references 70

Gene Ontology: tool for the unification of biology

Fast and accurate short read alignment with Burrows–Wheeler transform

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

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