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Lüthi, J.

Publications and source records attributed to Lüthi, J..

2 recordsLinked to original sources

Shield-4i: A Whole-mount Multiplexed Imaging Platform for Studying Multiscale Information Flow in 3D Multicellular Systems

Self-organization in multicellular systems emerges from reciprocal interactions across spatiotemporal scales. Understanding how subcellular organization, tissue remodeling and developmental outcome are coordinated, thus requires simultaneous profiling of biological processes spanning orders of magnitudes in space and time. Yet, a unified experimental and computational framework for capturing these multiscale properties across in vivo and stem cell-derived systems has been lacking. Here, we introduce Shield-4i, a high-throughput, versatile, and accessible method for automated in toto iterative immunofluorescence imaging of whole-mount structures at subcellular resolution. Through polyepoxide-mediated inter- and intramolecular crosslinking, Shield-4i preserves sample integrity during repeated SDS-based elution cycles. We benchmark this method in gastrulating zebrafish and post-implantation mouse embryos and demonstrate its applicability to stem cell-derived 3D gastruloids, achieving up to 30-plex measurements of proteins and their post-translational modifications across hundreds of samples. To enable scalable analysis, we developed a dedicated 3D workflow supporting OME-Zarr-based and FAIR-compliant data storage, standardized processing, and multiscale feature extraction. Applying this framework to investigate gastruloid self-organization, we quantify how cellular physicochemical state and signaling properties encode cell position along embryonic axes and connect molecular patterning and fate decisions to morphological symmetry breaking at the multicellular scale. Together, Shield-4i provides a high-content in toto spatial proteomics platform for dissecting multiscale information flow and self-organization in multicellular systems.

systems biology↗

Fractal: Towards FAIR bioimage analysis at scale with OME-Zarr-native workflows

The rapid growth in microscopy data volume, dimensionality, and diversity urgently calls for scalable and reproducible analysis frameworks. While efforts on the open OME-Zarr format have helped standardize the storage of large microscopy datasets, solutions for standardized processing are still lacking. Here, we introduce two complementary contributions to address this gap: 1) the Fractal task specification, defining OME-Zarr processing units that can interoperate across computational environments and workflow engines, and 2) the Fractal platform, using this specification to enable scalable and modular OME-Zarr-native analysis workflows. We demonstrate their use across diverse biological research data, including terabyte-scale multiplexed, volumetric, and time-lapse imaging. In a clinical setting, we show that Fractal workflows achieve near-identical quantification of millions of cells across independent deployments, demonstrating the reproducibility required for translational applications. With its growing community of contributors, the Fractal ecosystem provides a foundation for FAIR microscopy image analysis relying on open file formats.

bioinformatics↗