Fang Wang

Injury induces retinal Müller glia of certain cold-blooded vertebrates, but not those of mammals, to regenerate neurons. To identify gene regulatory networks that reprogram Müller glia into progenitor cells, we profiled changes in gene expression and chromatin accessibility in Müller glia from zebrafish, chick, and mice in response to different stimuli. We identified evolutionarily conserved and species-specific gene networks controlling glial quiescence, reactivity, and neurogenesis. In zebrafish and chick, the transition from quiescence to reactivity is essential for retinal regeneration, whereas in mice, a dedicated network suppresses neurogenic competence and restores quiescence. Disruption of nuclear factor I transcription factors, which maintain and restore quiescence, induces Müller glia to proliferate and generate neurons in adult mice after injury. These findings may aid in designing therapies to restore retinal neurons lost to degenerative diseases.

Abstract Annotating cell types based on the single-cell RNA-seq data is a prerequisite for researches on disease progress and tumor microenvironment. Here we show existing annotation methods typically suffer from lack of curated marker gene lists, improper handling of batch effect, and difficulty in leveraging the latent gene-gene interaction information, impairing their generalization and robustness. We developed a pre-trained deep neural network-based model scBERT (single-cell Bidirectional Encoder Representations from Transformers) to overcome the challenges. Following BERT’s approach of pre-train and fine-tune, scBERT obtains a general understanding of gene-gene interaction by being pre-trained on huge amounts of unlabeled scRNA-seq data and is transferred to the cell type annotation task of unseen and user-specific scRNA-seq data for supervised fine-tuning. Extensive and rigorous benchmark studies validated the superior performance of scBERT on cell type annotation, novel cell type discovery, robustness to batch effect, and model interpretability.