Dissecting DSB-repair pathways and cell-identity by transcriptomics

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The analysis of the transcriptome enables the mapping of pathway activity and cell type identity across various contexts, throughout the use of diverse tools. Notably, RNA sequencing (RNA-seq) facilitates an unbiased quantification of RNA species abundance. This study focuses on a computational analysis of both publicly available and newly generated RNA-seq datasets. The objectives of this study are to infer double-strand breaks (DSB) repair pathways in the context of therapeutic genome editing and conduct cell type classification of human-induced pluripotent stem-cell(hiPSC)-derived neurons in vitro. Genome editing, a promising emerging research field that focuses on the treatment of monogenic disorders, relies on the activity of highly specific endonucleases and the capacity of the cells to DSBs. To establish a specific micro-homology end joining (MMEJ)-based therapeutic genome editing for X- linked retinitis pigmentosa, DSB repair pathways’ activity must be assessed in post-mitotic retinal neurons, with a focus on photoreceptors. Using single-cell RNA sequencing (scRNA-seq) analysis, I characterized the dynamics of DSB repair pathways in the transition from dividing cells to post-mitotic retinal cells and showed that gene expression can provide useful insights on the pathways and activity of DSBs repair. Homologous directed repair (HDR) pathway-related genes were expressed at low levels in post-mitotic neurons. Non-homologous end joining (NHEJ) genes were predominantly expressed across all retinal cell types, species, and disease states. Nonetheless, determinant MMEJ genes retained expression at post mitotic stages in in vitro 2D and 3D models and in in vivo retinal samples. Moreover, I showed that hiPSC-derived neurons and retinal organoids represent well-suited in vitro model system for developing genomic engineering approaches in photoreceptors. Furthermore, to advance tissue and neuronal cell engineering in vitro, we capitalized on the analysis of cell types using single-cell transcriptomics. I studied the cell type composition of retinal and brain organoids using scRNA-seq and comparing the results to published reference datasets for cell type classification. scRNA-seq analysis showed the presence of unexpected cell phenotypes arising from brain organoids in vitro. Employing various computational methodologies, I conducted a bioinformatic validation of the cell type composition within in vitro brain organoids. Our analysis revealed the presence of optic vesicle-related cells and cells characteristic of the developing cerebral cortex in brain organoids produced using novel experimental protocols. Lastly, we designed a screening platform for new neuronal-programming transcription factor (TF) combinations in vitro, by relying on the cell identity inferred by the transcriptome and on the random transduction of the TFome library (>1000 inducible ORFs) into hiPSCs. We used an adapted scRNA-seq protocol to measure endogenous and exogenous mRNA content. The exogenous mRNA data gave information about the variety of TF in every cell, while the endogenous mRNA data allowed to infer the cell identity. Most of the screened TFs belonged to the well-studied neuronal-related TF bHLH class. ATOH1 and NEUROG1, which are important TFs that determine neuronal fate, were the most abundantly screened, thus indicating an enrichment of pro-neuronal TF in the screening platform. I could focus on TF combination of high-quality induced neurons by scoring neuronal marker genes in every single cell. The distinct developmental trajectories observed within the dataset can be attributed to the presence of ATOH1, NEUROG1, or NEUROD1. While specific neuronal sub-cell types were not directly linked to these trajectories, we were able to identify TF combinations that were more likely to be associated with specific neuronal sub-cell types. Our findings demonstrate that in vitro-generated neurons induced by TF can stem from a diverse array of TF combinations, some of which involve neuronal-related TF which were not previously reported.:Acknowledgments .................................................................................................................................... 5 Summary ................................................................................................................................................... 6 Abbreviations .......................................................................................................................................... 10 Figures index ........................................................................................................................................... 14 Tables index ............................................................................................................................................ 15 Publications arising from this thesis ....................................................................................................... 16 1 Introduction .................................................................................................................................... 18 1.1 The retina................................................................................................................................ 18 1.1.1 Cell type composition ..................................................................................................... 18 1.1.2 Retinitis pigmentosa ....................................................................................................... 19 1.2 Genome Editing ...................................................................................................................... 21 1.2.1 Tools for targeted and programmable genome editing .................................................. 22 1.2.2 Double strand break (DSB) repair pathways ................................................................... 24 1.3 Induced in vitro neuronal cell types ........................................................................................ 26 1.3.1 Neuronal TF programming .............................................................................................. 26 1.3.2 3D neuronal structures in a dish: brain organoids .......................................................... 28 1.4 Transcriptomics for cell type studies ...................................................................................... 29 1.4.1 Bulk and single-cell RNA sequencing............................................................................... 29 1.4.2 Classification of cell types ............................................................................................... 31 2 Aim.................................................................................................................................................. 36 2.1 Assess DSB pathway activity in the human retina to boost genome editing treatment ......... 36 2.2 Screening of TFs combinations for iNs .................................................................................... 36 3 Results ............................................................................................................................................ 37 3.1 DSB-repair pathways activity in photoreceptors .................................................................... 37 3.1.1 HDR-gene expression across cell-cycle stages ................................................................ 37 9 3.1.2 Comparison of mammalian retinal models ..................................................................... 41 3.1.3 Cones and Rods photoreceptors..................................................................................... 43 3.1.4 iNGN as a practical post-mitotic in vitro testbed ............................................................ 47 3.1.5 DSB-repair pathways in retinal degeneration ................................................................. 47 3.2 Neuronal cell type iden

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