Sebastian Tiesmeyer

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

This paper introduces a tool to detect overlapping cells and segmentation errors in 3D spatial transcriptomics data.

This study evaluates the Xenium In Situ spatial transcriptomics platform using mouse brain and human breast cancer datasets, comparing it to other technologies and benchmarking analysis tools.

Sainsc provides a segmentation-free tool for analyzing high-resolution spatial transcriptomics data, enabling accurate subcellular cell-type mapping with lower computational demands.

The paper identifies a disrupted trophoblast transition in preeclampsia, linking placental pathology to maternal syndrome through multi-omics profiling and in vitro analyses.

SSAM-lite provides a lightweight, web-based tool for rapid, segmentation-free analysis of spatially resolved transcriptomics data, enabling broad accessibility without requiring computational expertise.

This paper introduces SSAM, a cell segmentation-free method for cell-type identification and tissue domain detection in in situ transcriptomics data, showing it detects known and discovers new cell types in mouse brain tissues.

The paper uses single nucleus and in situ RNA sequencing to create a comprehensive atlas of human pancreas cells, revealing cell topographies and differences between adult and neonatal pancreata.