Superresolution microscopy localizes endogenous Dvl2 to Wnt signaling-responsive biomolecular condensates.

During organismal development, homeostasis, and disease, Dishevelled (Dvl) proteins act as key signaling factors in beta-catenin-dependent and beta-catenin-independent Wnt pathways. While their importance for signal transmission has been genetically demonstrated in many organisms, our mechanistic understanding is still limited. Previous studies using overexpressed proteins showed Dvl localization to large, punctate-like cytoplasmic structures that are dependent on its DIX domain. To study Dvl's role in Wnt signaling, we genome engineered an endogenously expressed Dvl2 protein tagged with an mEos3.2 fluorescent protein for superresolution imaging. First, we demonstrate the functionality and specificity of the fusion protein in beta-catenin-dependent and beta-catenin-independent signaling using multiple independent assays. We performed live-cell imaging of Dvl2 to analyze the dynamic formation of the supramolecular cytoplasmic Dvl2_mEos3.2 condensates. While overexpression of Dvl2_mEos3.2 mimics the previously reported formation of abundant large "puncta," supramolecular condensate formation at physiological protein levels is only observed in a subset of cells with approximately one per cell. We show that, in these condensates, Dvl2 colocalizes with Wnt pathway components at gamma-tubulin and CEP164-positive centrosomal structures and that the localization of Dvl2 to these condensates is Wnt dependent. Single-molecule localization microscopy using photoactivated localization microscopy (PALM) of mEos3.2 in combination with DNA-PAINT demonstrates the organization and repetitive patterns of these condensates in a cell cycle-dependent manner. Our results indicate that the localization of Dvl2 in supramolecular condensates is coordinated dynamically and dependent on cell state and Wnt signaling levels. Our study highlights the formation of endogenous and physiologically regulated biomolecular condensates in the Wnt pathways at single-molecule resolution.

HTSvis: a web app for exploratory data analysis and visualization of arrayed high-throughput screens.

SUMMARY: Arrayed high-throughput screens (HTS) cover a broad range of applications using RNAi or small molecules as perturbations and specialized software packages for statistical analysis have become available. However, exploratory data analysis and integration of screening results has remained challenging due to the size of the data sets and the lack of user-friendly tools for interpretation and visualization of screening results. Here we present HTSvis, a web application to interactively visualize raw data, perform quality control and assess screening results from single to multi-channel measurements such as image-based screens. Per well aggregated raw and analyzed data of various assay types and scales can be loaded in a generic tabular format. AVAILABILITY AND IMPLEMENTATION: HTSvis is distributed as an open-source R package, downloadable from https://github.com/boutroslab/HTSvis and can also be accessed at http://htsvis.dkfz.de . CONTACT: m.boutros@dkfz.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online .

A global genetic interaction network by single-cell imaging and machine learning.

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

Machine learning and image-based profiling in drug discovery.

The increase in imaging throughput, new analytical frameworks and high-performance computational resources open new avenues for data-rich phenotypic profiling of small molecules in drug discovery. Image-based profiling assays assessing single-cell phenotypes have been used to explore mechanisms of action, target efficacy and toxicity of small molecules. Technological advances to generate large data sets together with new machine learning approaches for the analysis of high-dimensional profiling data create opportunities to improve many steps in drug discovery. In this review, we will discuss how recent studies applied machine learning approaches in functional profiling workflows with a focus on chemical genetics. While their utility in image-based screening and profiling is predictably evident, examples of novel insights beyond the status quo based on the applications of machine learning approaches are just beginning to emerge. To enable discoveries, future studies also need to develop methodologies that lower the entry barriers to high-throughput profiling experiments by streamlining image-based profiling assays and providing applications for advanced learning technologies such as easy to deploy deep neural networks.

Time-resolved mapping of genetic interactions to model rewiring of signaling pathways.

Context-dependent changes in genetic interactions are an important feature of cellular pathways and their varying responses under different environmental conditions. However, methodological frameworks to investigate the plasticity of genetic interaction networks over time or in response to external stresses are largely lacking. To analyze the plasticity of genetic interactions, we performed a combinatorial RNAi screen in Drosophila cells at multiple time points and after pharmacological inhibition of Ras signaling activity. Using an image-based morphology assay to capture a broad range of phenotypes, we assessed the effect of 12768 pairwise RNAi perturbations in six different conditions. We found that genetic interactions form in different trajectories and developed an algorithm, termed MODIFI, to analyze how genetic interactions rewire over time. Using this framework, we identified more statistically significant interactions compared to end-point assays and further observed several examples of context-dependent crosstalk between signaling pathways such as an interaction between Ras and Rel which is dependent on MEK activity. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

High-throughput downstream process development for cell-based products using aqueous two-phase systems (ATPS) - A case study.

The availability of preparative-scale downstream processing strategies for cell-based products presents a critical juncture between fundamental research and clinical development. Aqueous two-phase systems (ATPS) present a gentle, scalable, label-free, and cost-effective method for cell purification, and are thus a promising tool for downstream processing of cell-based therapeutics. Here, the application of a previously developed robotic screening platform that enables high-throughput cell partitioning analysis in ATPS is reported. In the present case study a purification strategy for two model cell lines based on high-throughput screening (HTS)-data and countercurrent distribution (CCD)-modeling, and validated the CCD-model experimentally is designed. The obtained data are shown an excellent congruence between CCD-model and experimental data, indicating that CCD-models in combination with HTS-data are a powerful tool in downstream process development. Finally, the authors are shown that while cell cycle phase significantly influences cell partitioning, cell type specific differences in surface properties are the main driving force in charge-dependent separation of HL-60 and L929 cells. In order to design a highly robust purification process it is, however, advisable to maintain constant growth conditions.

High-throughput cell quantification assays for use in cell purification development - enabling technologies for cell production.

High-throughput screening (HTS) technology is gaining increasing importance in downstream process development of cell-based products. The development of such HTS-technologies, however, is highly dependent on the availability of robust, accurate, and sensitive high-throughput cell quantification methods. In this article, we compare state-of-the-art cell quantification methods with focus on their applicability in HTS-platforms for downstream processing of cell-based products. Sensitivity, dynamic range, and precision were evaluated for four methods that differ in their respective mechanism. In addition, we evaluated the performance of these methods over a range of buffer compositions, medium densities, and viscosities, representing conditions found in many downstream processing methods. We found that CellTiter-Glo™ and flow cytometry are excellent tools for high-throughput cell quantification. Both methods have broad working ranges (3-4 log) and performed well over a wide range of buffer compositions. In comparison, CyQuant® Direct and CellTracker™ had smaller working ranges and were more sensitive to changes in buffer composition. For fast and sensitive quantification of a single cell type, CellTiter-Glo™ performed best, while for more complex cell mixtures flow cytometry is the method of choice. Our analysis will facilitate the selection of the most suitable method for a specific application and provides a benchmark for future HTS development in downstream processing of cell-based products.

High-throughput downstream process development for cell-based products using aqueous two-phase systems.

As the clinical development of cell-based therapeutics has evolved immensely within the past years, downstream processing strategies become more relevant than ever. Aqueous two-phase systems (ATPS) enable the label-free, scalable, and cost-effective separation of cells, making them a promising tool for downstream processing of cell-based therapeutics. Here, we report the development of an automated robotic screening that enables high-throughput cell partitioning analysis in ATPS. We demonstrate that this setup enables fast and systematic investigation of factors influencing cell partitioning. Moreover, we examined and optimized separation conditions for the differentiable promyelocytic cell line HL-60 and used a counter-current distribution-model to investigate optimal separation conditions for a multi-stage purification process. Finally, we show that the separation of CD11b-positive and CD11b-negative HL-60 cells is possible after partial DMSO-mediated differentiation towards the granulocytic lineage. The modeling data indicate that complete peak separation is possible with 30 transfers, and >93% of CD11b-positive HL-60 cells can be recovered with >99% purity. The here described screening platform facilitates faster, cheaper, and more directed downstream process development for cell-based therapeutics and presents a powerful tool for translational research.