Biology subjects

Schlagheck, C.

Publications and source records attributed to Schlagheck, C..

3 recordsLinked to original sources

bioRxiv2025

Deep learning predicts tissue outcomes in retinal organoids

Retinal organoids have become important models for studying development and disease, yet stochastic heterogeneity in the formation of cell types, tissues, and phenotypes remains a major challenge. This limits our ability to precisely experimentally address the early developmental trajectories towards these outcomes. Here, we utilize deep learning to predict the differentiation path and resulting tissues in retinal organoids well before they become visually discernible. Our approach effectively bypasses the challenge of organoid-related heterogeneity in tissue formation. For this, we acquired a high-resolution time-lapse imaging dataset comprising about 1,000 organoids and over 100,000 images enabling precise temporal tracking of organoid development. By combining expert annotations with advanced image analysis of organoid morphology, we characterized the heterogeneity of the retinal pigmented epithelium (RPE) and lens tissues, as well as global organoid morphologies over time. Using this training set, our deep learning approach accurately predicts the emergence and size of RPE and lens tissue formation on an organoid-by-organoid basis at early developmental stages, refining our understanding of when early lineage decisions are made. This approach advances knowledge of tissue and phenotype decision-making in organoid development and can inform the design of similar predictive platforms for other organoid systems, paving the way for more standardized and reproducible organoid research. Finally, it provides a direct focus on early developmental time points for in-depth molecular analyses, alleviated from confounding effects of heterogeneity.

developmental biology↗

bioRxiv2023

2-photon laser printing to mechanically stimulate multicellular systems in 3D

Most biological activities take place in 3D environments, where cells communicate with each other in various directions and are located in a defined, often microstructured, space. To investigate the effect of defined cyclic mechanical forces on a multicellular system, we develop a sub-millimeter sized stretching device for mechanical stimulation of a structurally restricted, soft multicellular microenvironment. For the stretching device, a multimaterial 3D microstructure made of PDMS and gelatine-based hydrogel is printed via 2-photon polymerization (2PP) method. The printed structures are first characterized microscopically and mechanically to study the effect of different printing parameters. With 2PP, organotypic cell cultures are then directly printed into the hydrogel structures to achieve true 3D cell culture systems. These are mechanically stimulated with a cantilever by indenting the stretching device at a defined point. As a most important result, the cells in the 3D organotypic cell culture change morphology and actin orientation when exposed to cyclic mechanical stretch, even within short timescales of just 30 minutes. As a proof of concept, we encapsulated a Medaka retinal organoid in the same structure to demonstrate that even preformed organoids can be stimulated by our method. The results demonstrate the power of 2PP to manufacturing multifunctional soft devices for mechanically controlling multicellular systems at micrometer resolution and thus mimicking mechanical stress situations, as they occur in vivo.

biophysics↗

bioRxiv2023

Visualisation of gene expression within the context of tissues: an X-ray computed tomography-based multimodal approach

The development of an organism is orchestrated by the spatial and temporal expression of genes. Accurate visualisation of gene expression patterns in the context of the surrounding tissues offers a glimpse into the mechanisms that drive morphogenesis. We developed correlative light-sheet fluorescence microscopy and X-ray computed tomography approach to map gene expression patterns to the whole organisms 3D anatomy at cellular resolution. We show that this multimodal approach is applicable to gene expression visualised by protein-specific antibodies and fluorescence RNA in situ hybridisation, offering a detailed understanding of individual phenotypic variations in model organisms. Furthermore, the approach provides a unique possibility to identify tissues together with their 3D cellular and molecular composition in anatomically less-defined in vitro models, such as organoids. We anticipate that the visual and quantitative insights into the 3D distribution of gene expression within tissue architecture, by the multimodal approach developed here, will be equally valuable for reference atlases of model organisms development, as well as for comprehensive screens and morphogenesis studies of in vitro models.

developmental biology↗