Biology subjects

Müller-Bötticher, N.

Publications and source records attributed to Müller-Bötticher, N..

5 recordsLinked to original sources

bioRxiv2026

Atlas-scale spatially aware clustering with support for 3D and multimodal data using SpatialLeiden

Here we extend SpatialLeiden, our spatial clustering algorithm, to enable generalised atlas-scale multi-sample, 3D serial-section, and multimodal spatial omics via flexible neighbour-graph multiplexing on batch-corrected latent spaces. It delivers coherent domains aligning with brain atlases across >100 samples, stable 3D reconstruction of cancer tissue structures, and integrated multimodal features, outperforming specialized tools in modularity and scalability on standard hardware. SpatialLeiden is compatible with scverse for broad and intuitive adoption.

bioinformatics↗

bioRxiv2025

2D, or not 2D? Investigating Vertical Signal Integrity of Tissue Slices

Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in 3D. Cell segmentation assigns transcripts to cells and precedes annotation of cell function. However, cell segmentation is usually performed in 2D, thus unable to deal with spatial doublets arising from overlapping cells, resulting in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds, and inaccurate cell-segmentation by analyzing transcript localization in 3D.

bioinformatics↗

bioRxiv2024

SpatialLeiden - Spatially-aware Leiden clustering

Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is the algorithm of choice in the single-cell community. However, in the field of spatial omics, Leiden has been considered a non-spatial clustering method. Here, we show that by integrating spatial embeddings Leiden clustering is rendered into a computationally highly performant, spatially aware clustering method that compares well with state-of-the art clustering methods.

bioinformatics↗

bioRxiv2024

Sainsc: a computational tool for segmentation-free analysis of in-situ capture

Spatially resolved transcriptomics has become the method of choice to characterise the complexity of biomedical tissue samples. Until recently, scientists have been restricted to profiling methods with high spatial resolution but for a limited set of genes or methods that can profile transcriptome-wide but at low spatial resolution. Through recent developments, there are now methods which offer subcellular spatial resolution and full transcriptome coverage. However, utilizing the high spatial and gene resolution of these new methods remains elusive due to several factors including low detection efficiency, high computational cost and difficulties in delineating cell borders. Here we present Sainsc (Segmentation-free analysis of in-situ capture data), which combines a cell-segmentation free approach with efficient data processing of transcriptome-wide nanometer resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at a subcellular level, together with corresponding maps of the assignment scores that facilitate the interpretation in the local confidence of cell-type assignment. We demonstrate its utility and accuracy across different tissues and profiling methods. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

bioinformatics↗

bioRxiv2021

SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

1The combination of a cells transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data. Availability and ImplementationSSAM-lite is an open-source browser-based web application with source code freely available on Github via https://github.com/HiDiHlabs/ssam-lite. Stable releases can be accessed via https://ssam-lite.bihealth.org and https://ssam-lite.netlify.app, and developmental releases can be accessed via https://dev--ssam-lite.netlify.app. The source code for a locally deployable server version, SSAM-lite-server, is available on GitHub via https://github.com/HiDiHlabs/ssam-lite-server. Both versions require a modern browser with JavaScript and WebGL support. Detailed user guides and documentation can be found at https://ssam-lite.readthedocs.io.

bioinformatics↗