Optimizing the Conditions and Use of Synthetic Matrix for Three-Dimensional In Vitro Retinal Differentiation from Mouse Pluripotent Cells

Cell replacement with stem cell-derived retinal ganglion cells from different protocols

Glaucoma, characterized by a degenerative loss of retinal ganglion cells, is the second leading cause of blindness worldwide. There is currently no cure for vision loss in glaucoma because retinal ganglion cells do not regenerate and are not replaced after injury. Human stem cell-derived retinal ganglion cell transplant is a potential therapeutic strategy for retinal ganglion cell degenerative diseases. In this review, we first discuss a 2D protocol for retinal ganglion cell differentiation from human stem cell culture, including a rapid protocol that can generate retinal ganglion cells in less than two weeks and focus on their transplantation outcomes. Next, we discuss using 3D retinal organoids for retinal ganglion cell transplantation, comparing cell suspensions and clusters. This review provides insight into current knowledge on human stem cell-derived retinal ganglion cell differentiation and transplantation, with an impact on the field of regenerative medicine and especially retinal ganglion cell degenerative diseases such as glaucoma and other optic neuropathies.

Controlling donor and newborn neuron migration and maturation in the eye through microenvironment engineering

Ongoing cell therapy trials have demonstrated the need for precision control of donor cell behavior within the recipient tissue. We present a methodology to guide stem cell-derived and endogenously regenerated neurons by engineering the microenvironment. Being an "approachable part of the brain," the eye provides a unique opportunity to study neuron fate and function within the central nervous system. Here, we focused on retinal ganglion cells (RGCs)-the neurons in the retina are irreversibly lost in glaucoma and other optic neuropathies but can potentially be replaced through transplantation or reprogramming. One of the significant barriers to successful RGC integration into the existing mature retinal circuitry is cell migration toward their natural position in the retina. Our in silico analysis of the single-cell transcriptome of the developing human retina identified six receptor-ligand candidates, which were tested in functional in vitro assays for their ability to guide human stem cell-derived RGCs. We used our lead molecule, SDF1, to engineer an artificial gradient in the retina, which led to a 2.7-fold increase in donor RGC migration into the ganglion cell layer (GCL) and a 3.3-fold increase in the displacement of newborn RGCs out of the inner nuclear layer. Only donor RGCs that migrated into the GCL were found to express mature RGC markers, indicating the importance of proper structure integration. Together, these results describe an "in silico-in vitro-in vivo" framework for identifying, selecting, and applying soluble ligands to control donor cell function after transplantation.

Bioengineering Approaches for the Advanced Organoid Research

Recent advances in 3D cell culture technology have enabled scientists to generate stem cell derived organoids that recapitulate the structural and functional characteristics of native organs. Current organoid technologies have been striding toward identifying the essential factors for controlling the processes involved in organoid development, including physical cues and biochemical signaling. There is a growing demand for engineering dynamic niches characterized by conditions that resemble in vivo organogenesis to generate reproducible and reliable organoids for various applications. Innovative biomaterial-based and advanced engineering-based approaches have been incorporated into conventional organoid culture methods to facilitate the development of organoid research. The recent advances in organoid engineering, including extracellular matrices and genetic modulation, are comprehensively summarized to pinpoint the parameters critical for organ-specific patterning. Moreover, perspective trends in developing tunable organoids in response to exogenous and endogenous cues are discussed for next-generation developmental studies, disease modeling, and therapeutics.

Retinal Organoids: Cultivation, Differentiation, and Transplantation

Retinal organoids (ROs), which are derived from stem cells, can automatically form three-dimensional laminar structures that include all cell types and the ultrastructure of the retina. Therefore, they are highly similar to the retinal structure in the human body. The development of organoids has been a great technological breakthrough in the fields of transplantation therapy and disease modeling. However, the translation of RO applications into medical practice still has various deficiencies at the current stage, including the long culture process, insufficient yield, and great heterogeneity among ROs produced under different conditions. Nevertheless, many technological breakthroughs have been made in transplanting ROs for treatment of diseases such as retinal degeneration. This review discusses recent advances in the development of ROs, improvements of the culture protocol, and the latest developments in RO replacement therapy techniques.

Transplantation of miPSC/mESC-derived retinal ganglion cells into healthy and glaucomatous retinas

Optic neuropathies, including glaucoma, are a group of neurodegenerative diseases, characterized by the progressive loss of retinal ganglion cells (RGCs), leading to irreversible vision loss. While previous studies demonstrated the potential to replace RGCs with primary neurons from developing mouse retinas, their use is limited clinically. We demonstrate successful transplantation of mouse induced pluripotent stem cell (miPSC)/mouse embryonic stem cell (mESC)-derived RGCs into healthy and glaucomatous mouse retinas, at a success rate exceeding 65% and a donor cell survival window of up to 12 months. Transplanted Thy1-GFP+ RGCs were able to polarize within the host retina and formed axonal processes that followed host axons along the retinal surface and entered the optic nerve head. RNA sequencing of donor RGCs re-isolated from host retinas at 24 h and 1 week post-transplantation showed upregulation of cellular pathways mediating axonal outgrowth, extension, and guidance. Additionally, we provide evidence of subtype-specific diversity within miPSC-derived RGCs prior to transplantation.

Semi-Automated Approach for Retinal Tissue Differentiation

Purpose

Three-dimensional strategy for the differentiation of pluripotent stem cells to the retina has been widely used to study retinal development, although the cell production and drug discovery applications are limited by the throughput. Here we attempted to scale up the protocol using a semiautomated approach.

Methods

For the experiments we used the Rx-GFP mouse embryonic stem cell (mES) reporter cell line, specific for early retinal development and human embryonic stem cell line Brn3b-tdTomato, specific for retinal ganglion cells. To increase the throughput, we implemented automated media exchange using Thermo WellWash Versa with Thermo RapidStack robot. To analyze the rate of retinal differentiation in mouse stem-cell derived organoids we imaged the plates at day 10 of differentiation using Life Technologies EVOS Fl Auto. The automated image analysis of fluorescent images was performed with custom Python OpenCV script.

Results

The implementation of a semiautomated approach significantly reduced the operator time needed: 34 minutes versus two hours for 960 organoids over the course of 25 days without any change in differentiation pattern and quantity of retinal differentiation. Automated image analysis showed that Forskolin treatment starting from day 1 leads to a significant increase in retinal field induction efficiency.

Conclusions

Semiautomated approach can be applied to retinal tissue differentiation to increase the throughput of the protocol. We demonstrated that automated image analysis can be used to evaluate differentiation efficiency, as well as for troubleshooting and to study factors affecting retinal differentiation.

Translational Relevance

Using robotic approach reduces the risk of human error and allows to perform all cycle of cell production in enclosed conditions, which is critical for GMP cell manufacture.

Convolutional Neural Networks Can Predict Retinal Differentiation in Retinal Organoids

We have developed a deep learning-based computer algorithm to recognize and predict retinal differentiation in stem cell-derived organoids based on bright-field imaging. The three-dimensional "organoid" approach for the differentiation of pluripotent stem cells (PSC) into retinal and other neural tissues has become a major in vitro strategy to recapitulate development. We decided to develop a universal, robust, and non-invasive method to assess retinal differentiation that would not require chemical probes or reporter gene expression. We hypothesized that basic-contrast bright-field (BF) images contain sufficient information on tissue specification, and it is possible to extract this data using convolutional neural networks (CNNs). Retina-specific Rx-green fluorescent protein mouse embryonic reporter stem cells have been used for all of the differentiation experiments in this work. The BF images of organoids have been taken on day 5 and fluorescent on day 9. To train the CNN, we utilized a transfer learning approach: ImageNet pre-trained ResNet50v2, VGG19, Xception, and DenseNet121 CNNs had been trained on labeled BF images of the organoids, divided into two categories (retina and non-retina), based on the fluorescent reporter gene expression. The best-performing classifier with ResNet50v2 architecture showed a receiver operating characteristic-area under the curve score of 0.91 on a test dataset. A comparison of the best-performing CNN with the human-based classifier showed that the CNN algorithm performs better than the expert in predicting organoid fate (84% vs. 67 ± 6% of correct predictions, respectively), confirming our original hypothesis. Overall, we have demonstrated that the computer algorithm can successfully recognize and predict retinal differentiation in organoids before the onset of reporter gene expression. This is the first demonstration of CNN's ability to classify stem cell-derived tissue in vitro.