Ludvig Bergenstråhle

This paper introduces a deep learning method for spatial landmark detection and tissue registration, showing improved accuracy and stability over existing approaches.

This paper introduces a method to combine histology and spatial gene expression data to infer high-resolution transcriptome maps without sequencing or hybridization.

The paper uses spatial transcriptomics to analyze copy number variations in benign and malignant tissues, revealing clonal patterns and suggesting early cancer evolution mechanisms.

Across 1,176,683 airway and lung cells from 22 datasets, ACE2 rises with age, in males and in smokers' multiciliated cells, and is lowest in young children.

The paper presents a probabilistic method integrating spatial transcriptomics and single-cell RNA-sequencing to spatially map cell types across tissues.

This paper introduces HDST, a high-resolution spatial transcriptomics method, and demonstrates its application in mouse brain tissue to resolve spatial expression patterns and cell types.

The paper introduces a probabilistic framework to analyze spatial transcriptomics data, identifying transcriptomic patterns corresponding to cell types and tissue components in mouse brain samples.