Periodic Reporting for period 2 - DECODE (Decoding Context-Dependent Genetic Networks in vivo)
Période du rapport: 2021-01-01 au 2022-06-30
The evolutionary success of multicellular organisms is based on the division of labor between cells. While some of the molecular determinants for cell fate specification have been identified, a fundamental understanding of which genetic activities are required in each cell of a developing tissue is still outstanding. The DECODE project will develop and apply leading-edge system genetics methods to Arabidopsis and Drosophila, two major model systems from the plant and animal kingdoms to decode context-dependent genetic networks in vivo. To achieve this, DECODE brings together experimental and theoretical groups with complementary expertise in model organism genetics and cellular phenotyping, single-cell genomics, statistics and computational biology. Building on our combined expertise, we will create functional genetic maps using conditional CRISPR/Cas9-based single- and higher order knockout perturbations in vivo combined with single-cell expression profiling and imaging. Coupled with powerful computational analysis, this project will not only define, predict and rigorously test the unique genetic repertoire of each cell, but also unravel how genetic networks adapt their topology and function across cell types and external stimuli. With more than thousand conditional knockouts, characterized by several million single-cell transcriptome profiles and high-resolution imaging this project will create the largest single-cell perturbation map in any model organism and will provide fundamental insights into the genetic architecture of complex tissues. Analyzing two tissues with divergent organization and regulatory repertoire will enable us to uncover general principles in the genetic circuits controlling context-dependent cell behavior. Consequently, we expect that the DECODE project in model organisms will lay the conceptual and methodological foundation for perturbation-based functional atlases in other tissues or species.
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.
To reach the project goals, the DECODE team had to develop resources, methodological procedures and novel strategies for experimental as well as computational approaches. Here we would like to particularly highlight four achievements that were published in internationally recognized journals and made publicly available. The Drosophila in vivo CRISPR-Cas libraries (Port et al, PNAS 2020; Port et al, eLife 2020) open up new possibilities for systematic screening approaches and are made available through the Vienna Drosophila Resource Center (VDRC). We developed and applied a novel single-cell methodology to generate one of the first single-cell RNAseq maps in a crop (Liu et al., Mol Plant 2021). For first-stage data analysis of single cell RNAseq data, we developed a new method that scales to the size of realistic datasets (Ahlmann-Eltze and Huber, Bioinformatics, 2020) and made it available as an R/Bioconductor package (glmGamPoi). We have further developed the novel computational method MEFISTO, providing a principle approach for integrating high-dimensional spatio-temporal data from perturbation screens (Velten et al, Nature Methods, 2021). With these achievements we are in a good position to successfully pursue the DECODE project aim to lay the conceptual and methodological foundation for perturbation-based functional atlases, to create the largest single-cell perturbation maps of the model organisms used and to provide fundamental insights into the genetic architecture of complex tissues.