Large-scale CRISPR screen reveals context-specific genetic regulation of retinal ganglion cell regeneration.
Kevin EmmerichJohn HageterThanh HoangPin LyuAbigail V SharrockAnneliese CeiselJames ThiererZeeshaan ChunawalaSaumya NimmagaddaIsabella PalazzoFrazer MatthewsLiyun ZhangDavid T WhiteCatalina RodriguezGianna GrazianoPatrick MarcosAdam MayTim MulliganBarak ReibmanMeera T SaxenaDavid F AckerleyJiang QianSeth BlackshawEric HorstickJeff S MummPublished in: Development (Cambridge, England) (2024)
Many genes are known to regulate retinal regeneration following widespread tissue damage. Conversely, genes controlling regeneration following limited cell loss, per degenerative diseases, are undefined. As stem/progenitor cell responses scale to injury levels, understanding how the extent and specificity of cell loss impact regenerative processes is important. Here, transgenic zebrafish enabling selective retinal ganglion cell (RGC) ablation were used to identify genes that regulate RGC regeneration. A single cell multiomics-informed screen of 101 genes identified seven knockouts that inhibited and eleven that promoted RGC regeneration. Surprisingly, 35 of 36 genes known/implicated as being required for regeneration following widespread retinal damage were not required for RGC regeneration, and seven even enhanced regeneration kinetics, including proneural factors neurog1, olig2, and ascl1a. Mechanistic analyses revealed ascl1a disruption increased the propensity of progenitor cells to produce RGCs; i.e., increased "fate bias". These data demonstrate plasticity in how Müller glia can convert to a stem-like state and context-specificity in how genes function during regeneration. Increased understanding of how the regeneration of disease-relevant cell types is specifically controlled will support the development of disease-tailored regenerative therapeutics.
Keyphrases
- stem cells
- single cell
- cell therapy
- genome wide
- rna seq
- wound healing
- high throughput
- optical coherence tomography
- dna methylation
- genome wide identification
- mesenchymal stem cells
- crispr cas
- gene expression
- machine learning
- artificial intelligence
- genome wide analysis
- copy number
- atrial fibrillation
- transcription factor