Partially Differentiated Neuroretinal Cells Promote Maturation of the Retinal Pigment Epithelium

A treatment within sight: challenges in the development of stem cell-derived photoreceptor therapies for retinal degenerative diseases

Stem cell therapies can potentially treat various retinal degenerative diseases, including age-related macular degeneration (AMD) and inherited retinal diseases like retinitis pigmentosa. For these diseases, transplanted cells may include stem cell-derived retinal pigmented epithelial (RPE) cells, photoreceptors, or a combination of both. Although stem cell-derived RPE cells have progressed to human clinical trials, therapies using photoreceptors and other retinal cell types are lagging. In this review, we discuss the potential use of human pluripotent stem cell (hPSC)-derived photoreceptors for the treatment of retinal degeneration and highlight the progress and challenges for their efficient production and clinical application in regenerative medicine.

Retinal Cell Transplantation, Biomaterials, and In Vitro Models for Developing Next-generation Therapies of Age-related Macular Degeneration

Retinal pigment epithelium (RPE) cells grown on a scaffold, an RPE patch, have potential to ameliorate visual impairment in a limited number of retinal degenerative conditions. This tissue-replacement therapy is suited for age-related macular degeneration (AMD), and related diseases. RPE cells must be transplanted before the disease reaches a point of no return, represented by the loss of photoreceptors. Photoreceptors are specialized, terminally differentiated neurosensory cells that must interact with RPE's apical processes to be functional. Human photoreceptors are not known to regenerate. On the RPE's basal side, the RPE transplant must induce the reformation of the choriocapillaris, thereby re-establishing the outer blood-retinal barrier. Because the scaffold is positioned between the RPE and choriocapillaris, it should ideally degrade and be replaced by the natural extracellular matrix that separates these tissues. Besides biodegradable, the scaffolds need to be nontoxic, thin enough to not affect the focal length of the eye, strong enough to survive the transplant procedure, yet flexible enough to conform to the curvature of the retina. The challenge is patients with progressing AMD treasure their remaining vision and fear that a risky surgical procedure will further degrade their vision. Accordingly, clinical trials only treat eyes with severe impairment that have few photoreceptors to interact with the transplanted patch. Although safety has been demonstrated, the cell-replacement mechanism and efficacy remain difficult to validate. This review covers the structure of the retina, the pathology of AMD, the limitations of cell therapy approaches, and the recent progress in developing retinal therapies using biomaterials.

A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p = 6.15 × 10⁻⁷⁶, r = 0.24; cohen’s D = 2.6). Building on this, we developed an augmented annotation algorithm (single cell Annotation via Literature Encoding, or scALE) that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 133 clusters from nine datasets of human breast, colon, heart, joint, ovary, prostate, skin, and small intestine tissues. With the optimized settings, the true cellular identity matched the top prediction in 59% of tested clusters and was present among the top five predictions for 91% of clusters. scALE slightly outperformed an existing method for reference data driven automated cluster annotation, and we demonstrate that integration of scALE can meaningfully improve the annotations derived from such methods. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of well-studied cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature-derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.