Ludvig Larsson

The paper creates a spatial transcriptomic atlas of pediatric brain tumors, identifying spatial archetypes and relapse-associated clones across multiple diagnoses.

This study characterizes gene expression in human growth plates, identifying novel markers and sub-populations of resting zone chondrocytes with features of quiescent stem cells.

This study maps spatial dynamics in early human heart development using single-cell and spatial transcriptomics, identifying cell states and novel features like cardiac fibroblasts and chromaffin cells.

The paper introduces PNA, a DNA-based method to map protein organization and interactions in single cells, revealing abundance, clustering, and colocalization relationships.

MEA-seqX integrates electrophysiological and transcriptional data to reveal coordinated spatiotemporal dynamics in hippocampal circuits and identify cell types based on bimodal profiles.

This paper introduces a spatial multimodal analysis method that integrates histology, mass spectrometry imaging, and spatial transcriptomics to study mRNA and metabolite distributions in brain tissues.

This paper introduces Expansion Spatial Transcriptomics to improve spatial resolution and capture efficiency in spatial transcriptomics by expanding tissue before transcriptome capture, demonstrated on mouse brain samples.

The paper creates a developmental cell atlas of the human spinal cord using single-cell and spatial transcriptomics, revealing spatiotemporal gene regulation and novel insights into ependymoma cancer stem cells.

The paper introduces RRST, a spatial transcriptomics workflow that improves mRNA recovery from low-quality fresh frozen samples, demonstrating its effectiveness across multiple tissue types.

The paper characterizes human prenatal cardiomyocytes using single-cell RNA sequencing and spatial transcriptomics, identifying eight distinct types with location-dependent properties and ligand-receptor interactions.

This study uses spatial transcriptomics to analyze cell type dynamics in rheumatoid arthritis synovium, revealing chemokine-driven interactions in inflamed tissue.

This study uses spatial transcriptomics to analyze liver tissue, revealing zonation patterns and previously uncharacterized structures in the spatial transcriptome.

This paper presents a method for genome-wide spatial mRNA analysis in FFPE tissues, demonstrating its ability to delineate anatomical regions and analyze transcriptomic signatures in ovarian carcinosarcoma.

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

This study uses single-cell and spatial transcriptomics to reveal clonal relationships and cell types in the mouse brain, showing fate-restricted progenitor cells and microglia origins.

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

This study uses spatial transcriptomics to map the transcriptomic landscape of healing murine colon, identifying spatially organized transcriptional programs and their association with human diseases.

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.

This study uses multidimensional transcriptomics to analyze immune cell distribution and identity in HER2+ breast tumors, revealing spatial heterogeneity and interpatient differences in immune infiltration.