Autologous cell replacement: a noninvasive AI approach to clinical release testing
Budd A. Tucker, … , Robert F. Mullins, Edwin M. Stone
Budd A. Tucker, … , Robert F. Mullins, Edwin M. Stone
Published January 21, 2020
Citation Information: J Clin Invest. 2020;130(2):608-611. https://doi.org/10.1172/JCI133821.
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Commentary

Autologous cell replacement: a noninvasive AI approach to clinical release testing

Abstract

The advent of human induced pluripotent stem cells (iPSCs) provided a means for avoiding ethical concerns associated with the use of cells isolated from human embryos. The number of labs now using iPSCs to generate photoreceptor, retinal pigmented epithelial (RPE), and—more recently—choroidal endothelial cells has grown exponentially. However, for autologous cell replacement to be effective, manufacturing strategies will need to change. Many tasks carried out by hand will need simplifying and automating. In this issue of the JCI, Schaub and colleagues combined quantitative bright-field microscopy and artificial intelligence (deep neural networks and traditional machine learning) to noninvasively monitor iPSC-derived graft maturation, predict donor cell identity, and evaluate graft function prior to transplantation. This approach allowed the authors to preemptively identify and remove abnormal grafts. Notably, the method is (a) transferable, (b) cost and time effective, (c) high throughput, and (d) useful for primary product validation.

Authors

Budd A. Tucker, Robert F. Mullins, Edwin M. Stone

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Figure 1

Schematic depiction of a hypothetical clinical manufacturing pipeline for an autologous retinal cell graft .

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Schematic depiction of a hypothetical clinical manufacturing pipeline fo...
(A) Anatomy of normal human retina and retinal anatomy of a patient with end-stage inherited retinal degenerative blindness with loss of the photoreceptor cell layer. (B) Graft implant process. First, dermal fibroblast cell isolation and expansion. Second, iPSC generation and clonal expansion. Third, CRISPR correction of the patient’s disease-causing genetic mutations. Fourth, QC analysis of CRISPR-corrected patient-derived iPSCs, which includes confirmation of pluripotency, retinal differentiation potential, normal karyotype, and genetic correction in the absence of off-target editing. Fifth, initiation of 3D retinal differentiation. Sixth, QC analysis to demonstrate retinal progenitor cell differentiation at day 30. Seventh, retinal organoids are dissociated and isolated retinal progenitor cells are loaded onto retinal cell delivery scaffolds and matured. Eighth, production pipeline of retinal cell grafts harvested at 60, 90, 120, and 150 days after differentiation to confirm normal maturation, sterility, potency, and identity (shown in purple). Production pipeline, using QBAM retinal cell grafts, can be evaluated at the same intervals without the need to harvest (shown in green). Unusable grafts can be discarded. Ninth, subretinal transplantation of one of the remaining retinal cell grafts. By using the QBAM-based live release testing strategy outlined in green, the number of grafts required for each patient is significantly reduced. Likewise, as compared with the traditional “go-no-go” pipeline depicted in purple, by using a QBAM-based approach, unusable grafts can be discarded during maturation, and the best graft available can be selected for transplantation (17). iPSC, induced pluripotent stem cell; QBAM, quantitative brightfield microscopy; QC, quality control.