During the first reporting period, the DECODE team set out to develop experimental and computational tools to systematically analyze genetic network systems in vivo in a large scale never seen before. For the experimental approaches, we are using Drosophila and Arabidopsis as two major model systems from the plant and animal kingdoms. So far, we have generated transgenic in vivo CRISPR-Cas libraries for Drosophila. These libraries can now be used to specifically knockout genes either in the whole fly or in selected tissues using CRISPR-Cas9 gene editing. First baseline experiments were performed to validate and refine the experimental set-up. The effect of the knockdown on single cells was tracked by state-of-the-art fluorescence microscopy and single-cell RNA sequencing technologies. In parallel we developed and established an inducible CRISPR-Cas mediated gene knockout system for Arabidopsis. In order to avoid unwanted background gene editing, we had to introduce two layers of controlling the CRISPR-Cas system in Arabidopsis. We further successfully established and benchmarked single-sell sequencing strategies, which was more difficult than foreseen originally. Underpinning the computational analysis for in vivo single-cell mapping, we set-up a scalable and robust workflow for data processing and we worked on data management strategies for DECODE. Specifically, we designed and implemented a shared, project-wide metadata database and developed its web interface in order to facilitate data sharing, collaborative work and workflow standardization and automation across the project team. In parallel to data management solutions, we have developed and benchmarked foundational single cell data analysis tools. As the project is based on measuring several layers of phenotypic changes in diverse model systems, we further developed computational methods beyond state-of-the-art for the analysis of high-throughput, multidimensional phenotype perturbation data and to integrate multi-omics single-cell profiles, which can also take temporal and spatial dependencies into account. Overall, the project started well, but faced significant delays due to the COVID-19 pandemy, e.g. through laboratory closures and supply shortages.