July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Semi-automated approach for 3D retinal organoids differentiation
Author Affiliations & Notes
  • Evgenii Kegeles
    The Schepens Eye Research Institute of Massachusetts Eye and Ear, an affiliate of Harvard Medical School, Boston, Massachusetts, United States
    School of Biological and Medical Physics, Moscow Institute of Physics and Technology (State University), Russian Federation
  • Petr Y Baranov
    The Schepens Eye Research Institute of Massachusetts Eye and Ear, an affiliate of Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Evgenii Kegeles, None; Petr Baranov, None
  • Footnotes
    Support  The BrightFocus Foundation, The Department of Ophthalmology HMS, The Massachusetts Lions Fund, ARVO AMD Genentech Fellowship (Petr Baranov)
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 3321. doi:
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      Evgenii Kegeles, Petr Y Baranov; Semi-automated approach for 3D retinal organoids differentiation. Invest. Ophthalmol. Vis. Sci. 2019;60(9):3321.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose : Three-dimensional strategy for the differentiation of pluripotent stem cells into retinal tissue became a major strategy for the derivation of retinal neurons. It has been widely used to study development, though the cell transplantation and drug discovery applications are limited by the throughput of the method. Here we attempted to scale-up the retinal differentiation using semi-automated approach.

Methods : Rx-GFP mouse embryonic stem cells (mES) (RIKEN) were used for all of the differentiation experiments. For retinal differentiation mES were seeded in 96 well plates for embryoid body formation (3,000 cells/well), followed by feeding with Optic Vesicle medium (d1, d2 and d5) and then Optic Cup medium (d9 to d25). To increase the throughput, we implemented automated liquid handling for cell seeding and media exchange using Thermo WellWash Versa in couple with Thermo RapidStack robot. The time needed for each of the major 13 steps of differentiation was recorded and compared to manual cells production procedure. To select organoids with higher retinal areas we scanned the plates at day 9 using Life Technologies EVOS Fl Auto. Then automated image analysis using ImageJ script was performed. Retinal development in both automated and manual conditions was assessed by flow cytometry on d9 for early development markers (Rx, Pax6) and on d25 with staining for RGC markers: RBPMS and Math5.

Results : We identified the most time-consuming steps of the retinal differentiation protocol which have the highest potential for automation: seeding, differentiation induction, medium change and automated image analysis for determination of GFP-positive organoids ready for picking. Semi-automated approach significantly reduces the operator time needed for cell differentiation: 45 minutes vs. 3 hours for ten 96-well plates (960 organoids) over the course of 25 days without any change in differentiation pattern.

Conclusions : Semi-automated approach can be applied to retinal tissue differentiation from mES in order to increase the throughput and reproducibility of the protocol. It enables studies that require large number of retinal tissue, such as drug discovery or cell transplantation. Also, since the operator time is minimized it reduces the risk of human error and allows to perform all cycle in enclosed conditions, which is critical for GMP cell manufacture.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.


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