Åsa Segerstolpe

This paper identifies asymmetric-drive rectification as a mechanism converting stress into structural memory, enabling functional specialization in cells and adaptive computational systems.

SlideCNA detects spatial copy number alterations from Slide-seq data to identify sub-clones in metastatic breast cancer.

The paper uses spatial transcriptomics to map cell types and their spatial organization in healthy and fibrotic human liver, identifying changes in hepatocyte populations during fibrosis.

The paper uses single-nuclei RNA-seq and spatial transcriptomics to analyze gene expression changes in the right atrium of patients with ischemic heart disease, revealing inflammatory microvascular dysfunction and altered tissue composition.

This paper creates a spatial cell atlas of neuroblastoma, revealing tumor heterogeneity through multi-omics data and identifying spatial compartmentalization and signaling patterns.

SCHAF generates spatially resolved single-cell transcriptomes from H&E histology images using vision transformers and adversarial learning, showing good agreement with ground-truth data.

SM-Omics is a platform that combines spatial transcriptomics and proteomics to analyze tissue samples at scale.

The paper introduces Tangram, a method that aligns single-nucleus RNA-seq data with spatial data to create an anatomically integrated spatial map of the mouse brain.

This study creates a single-cell and spatial atlas of autopsy tissues from COVID-19 patients, revealing altered cellular responses and potential therapeutic targets.