Thursday, April 3, 2025
4/3/2025
Non-destructive assessment of retinal differentiation capacity and developmental maturity - ABSTRACT The ability to generate patient induced pluripotent stem cells (iPSCs) has revolutionized the field of regenerative medicine. Like many researchers, we foresee a future where autologous stem cell-derived tissues will be used to treat a wide variety of neurodegenerative disorders, including inherited retinal degenerative blindness. One of the greatest hurdles for autologous cell replacement is that validated patient-specific therapeutics are difficult to produce using traditional manufacturing strategies, which are designed for large scale production of a single product to treat a large patient population. Despite this, decades of human transplant experience (spanning many organs and tissue types) have consistently shown that immunologic matching has a significant impact on graft function and longevity. To enable autologous cell production, we recently reported development of a robotic cell culture platform with imaging capabilities, that can automatically identify, pick, weed, and feed patient-derived cells over the life of a manufacturing campaign. While a step in the right direction, several hurdles to efficient production of autologous cell therapeutics remain. First, despite the use of well characterized iPSC generation protocols, significant variability between patient iPSC lines with respect to retinal differentiation capacity exists. For iPSC lines that lack the ability to efficiently differentiate into the target cell type, production costs and time are significantly increased. Second, traditional methods used to evaluate the clinical suitability of a product are inherently destructive and require repeat sampling throughout the manufacturing campaign (e.g., repeat immunohistochemical analysis during differentiation to track development). Furthermore, it is impossible to evaluate the clinical product intended for human use when destructive release tests are utilized (e.g., impossible to perform RNA-seq analysis on the actual graft that will be placed in a human). In this application, we propose two specific aims which are designed to address each of these major manufacturing hurdles. In Aim 1, we use imaging data collected during iPSC generation and retinal derivation to train an AI algorithm to select lines with a propensity for retinal differentiation. This would eliminate the wasted time and resources that are currently associated with using lines that are difficult to differentiate into retinal tissue. In Aim 2, we will correlate developmental transcript expression with unbiased imaging data collected throughout the retinal differentiation process, to identify morphological cues that can be used to train our robotic platform to assess organoid maturity and enable non- destructive clinical release testing. Upon completion of this program, we believe that we will have generated image analysis algorithms that can be used with automated cell culture platforms to enable production of patient iPSC lines with excellent retinal differentiation capacity, permit tracking of photoreceptor cell development, and allow for the non-destructive qualification of the actual product intended for clinical use.
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