Label-free microfluidic enrichment of photoreceptor cells.


Journal

Experimental eye research
ISSN: 1096-0007
Titre abrégé: Exp Eye Res
Pays: England
ID NLM: 0370707

Informations de publication

Date de publication:
10 2020
Historique:
received: 23 12 2019
revised: 20 05 2020
accepted: 21 07 2020
pubmed: 11 8 2020
medline: 29 1 2021
entrez: 11 8 2020
Statut: ppublish

Résumé

Inherited retinal degenerative disorders such as retinitis pigmentosa and Usher syndrome are characterized by progressive death of photoreceptor cells. To restore vision to patients blinded by these diseases, a stem cell-based photoreceptor cell replacement strategy will likely be required. Although retinal stem cell differentiation protocols suitable for generating photoreceptor cells exist, they often yield a rather heterogenous mixture of cell types. To enrich the donor cell population for one or a few cell types, scientists have traditionally relied upon the use of antibody-based selection approaches. However, these strategies are quite labor intensive and require animal derived reagents and equipment that are not well suited to current good manufacturing practices (cGMP). The purpose of this study was to develop and evaluate a microfluidic cell sorting device capable of exploiting the physical and mechanical differences between retinal cell types to enrich specific donor cell populations such as Retinal Pigment Epithelial (RPE) cells and photoreceptor cells. Using this device, we were able to separate a mixture of RPE and iPSC-derived photoreceptor precursor cell lines into two substantially enriched fractions. The enrichment factor of the RPE fraction was 2 and that of the photoreceptor precursor cell fraction was 2.7. Similarly, when human retina, obtained from 3 independent donors, was dissociated and passed through the sorting device, the heterogeneous mixture could be reliably sorted into RPE and photoreceptor cell rich fractions. In summary, microfluidic cell sorting is a promising approach for antibody free enrichment of retinal cell populations.

Identifiants

pubmed: 32771499
pii: S0014-4835(20)30424-3
doi: 10.1016/j.exer.2020.108166
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

108166

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Auteurs

Nicholas E Stone (NE)

The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Andrew P Voigt (AP)

Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA.

Jessica A Cooke (JA)

Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA.

Joseph C Giacalone (JC)

Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA.

Srinivas Hanasoge (S)

The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.

Robert F Mullins (RF)

Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA.

Budd A Tucker (BA)

Institute for Vision Research, Department of Ophthalmology and Visual Science, Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA.

Todd Sulchek (T)

The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. Electronic address: todd.sulchek@me.gatech.edu.

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Classifications MeSH