Human iPSC Modeling Reveals Mutation-Specific Responses to Gene Therapy in a Genotypically Diverse Dominant Maculopathy.

Dominantly inherited disorders are not typically considered to be therapeutic candidates for gene augmentation. Here, we utilized induced pluripotent stem cell-derived retinal pigment epithelium (iPSC-RPE) to test the potential of gene augmentation to treat Best disease, a dominant macular dystrophy caused by over 200 missense mutations in BEST1. Gene augmentation in iPSC-RPE fully restored BEST1 calcium-activated chloride channel activity and improved rhodopsin degradation in an iPSC-RPE model of recessive bestrophinopathy as well as in two models of dominant Best disease caused by different mutations in regions encoding ion-binding domains. A third dominant Best disease iPSC-RPE model did not respond to gene augmentation, but showed normalization of BEST1 channel activity following CRISPR-Cas9 editing of the mutant allele. We then subjected all three dominant Best disease iPSC-RPE models to gene editing, which produced premature stop codons specifically within the mutant BEST1 alleles. Single-cell profiling demonstrated no adverse perturbation of retinal pigment epithelium (RPE) transcriptional programs in any model, although off-target analysis detected a silent genomic alteration in one model. These results suggest that gene augmentation is a viable first-line approach for some individuals with dominant Best disease and that non-responders are candidates for alternate approaches such as gene editing. However, testing gene editing strategies for on-target efficiency and off-target events using personalized iPSC-RPE model systems is warranted. In summary, personalized iPSC-RPE models can be used to select among a growing list of gene therapy options to maximize safety and efficacy while minimizing time and cost. Similar scenarios likely exist for other genotypically diverse channelopathies, expanding the therapeutic landscape for affected individuals.

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.