Periodic Reporting for period 3 - DECODE (Decoding Context-Dependent Genetic Networks in vivo)
Reporting period: 2022-07-01 to 2023-12-31
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. 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.
So far, the DECODE team developed and applied 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 animal and plant kingdoms. We have generated and improved transgenic in vivo CRISPR-Cas libraries for Drosophila, which are being used to specifically knockout genes either in the whole fly or in selected tissues using CRISPR-Cas9 gene editing. A first subset screen focusing on components of signaling pathways specifically in intestinal stem cells revealed cross-talks between signaling pathways and identified an interesting candidate to follow up. Further studies highlighted the importance of coupling scRNASeq with CRISPR perturbations in order to investigate cellular mechanisms involved in stem cell homeostasis. In parallel we developed and established an inducible CRISPR-Cas mediated gene knockout system for Arabidopsis and generated a medium sized library of knock-out mutant transgenic lines. We optimized perturbation and single-cell sequencing strategies, which was more difficult than foreseen originally. Importantly, we were able to generate a first set of experimental data focusing on perturbation of specific signaling pathways. To ensure that the data being generated can be exploited, we developed a scalable and robust data management infrastructure includes workflows for data processing and downstream biological analyses. We have further implemented an automated pipeline to visualize preliminary analyses by anyone in the team. The data management strategy of the DECODE project has already demonstrated its utility in our daily work, facilitating data sharing and fostering collaborative work across the groups. In parallel to data management solutions, we have developed, benchmarked and improved foundational single cell data analysis tools. As core objective of the project is to quantify phenotypic changes across molecular layers and 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. An important step for the analysis of the perturbation data is the identification and characterization of the effect of perturbation on cell states and expression changes of individual genes in specific cell populations that are affected. To address this, we have developed a series of computational methods to comprehensively address this analysis question. Overall, the project started well, however the build-up period to establish resources and technologies has faced significant delays due to the COVID-19 pandemic, in particular. through laboratory closures, supply shortages and the reduced collaboration and interaction between the partner laboratories.
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 the following 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) opened up new possibilities for systematic screening approaches and were made available through the Vienna Drosophila Resource Center (VDRC). We have further optimized methods to perform mutagenesis with spatial and temporal control in Drosophila. To this end we have developed a new system based on the induction of premature stop codons with cytidine base editors (Doll et al, Science Advances 2023). Furthermore, we are focusing on increasing our perturbation throughput by combining methods. We developed and applied a novel single-cell methodology to generate one of the first single-cell RNAseq maps (Liu et al., Mol Plant 2021) as well as single cell chromatin accessibility map in a crop. 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 novel computational strategies for assaying genetic effects at the single-cell level (Cuomo et al., Mol Sysbiol, 2022), as well advancing the computational foundations for integrating high-dimensional spatio-temporal data from perturbation screens (Velten et al, Nature Methods, 2022). With these achievements, along with the first large experimental data-sets recorded, 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. However, the COVID-19 pandemic as well as unforeseen challenges in method development have caused significant delays in the project plan, so that the project will need more time than previously estimated to fully reach all aims.