Ruben Dries

dbverse enables efficient analysis of large spatial omics datasets by using embedded databases, outperforming traditional methods in runtime.

This paper creates a high-resolution spatial transcriptomic map of the murine lung's hematopoietic landscape, focusing on megakaryocytes and their interactions with vascular and stromal environments.

This study integrates single-cell and spatial transcriptomic data across species to identify conserved and distinct tumor ecosystems in osteosarcoma, revealing canine models as more faithful and highlighting ECM signaling in metastatic lesions.

This study identifies distinct malignant cell states and myeloid glutamate signaling in aggressive pancreatic neuroendocrine tumors, linking them to poor clinical outcomes and therapeutic vulnerabilities.

This study demonstrates that spatial transcriptomics can be performed on discarded breast biopsy tissue to generate high-quality data for tumor microenvironment analysis.

This study identifies metastasis-initiating osteosarcoma subpopulations that interact with lung and tumor cells to form a metastatic niche, highlighting tumor-stromal interactions as key drivers of metastasis.

This study investigates the therapeutic potential of targeting eIF4A1 to inhibit MYCN expression in neuroblastoma by exploiting its role in translation of polypurine-rich 5' UTRs.

Giotto Suite provides a modular, open-source framework for analyzing spatial multi-omics data across scales and technologies.

The paper introduces MAMS, a metadata standard for single-cell data matrices and analysis workflows, to improve reproducibility and interoperability across datasets.

Giotto is a toolbox for analyzing and visualizing spatial transcriptomic and proteomic data, offering integration with single-cell RNA-seq and supporting diverse datasets.

The paper integrates single-cell transcriptomic and spatial data to analyze cell-type and spatial domain contributions to heterogeneity in the mouse visual cortex.