Joakim Lundeberg

The paper uses spatial transcriptomics to analyze immune infiltration in pediatric ependymoma, revealing compartmentalized immune niches and heterogeneous T-cell therapy opportunities.

The paper uses in utero barcoding and single-cell transcriptomics to map clonal relationships in mouse mesoderm-derived tissues, revealing lineage relationships in liver and heart mesenchyme.

SIMPlex generates matched spatial and single-nucleus transcriptomic profiles from the same tissue section to resolve cell states with spatial signatures in mouse and human cancer tissues.

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.

The paper uses multimodal single-cell profiling to show how puberty and chemotherapy affect pediatric ovarian tissue, identifying vulnerabilities and a potential biomarker.

The paper identifies rare gain-of-function regulatory mutations as significant contributors to the missing heritability of bicuspid aortic valve.

This study uses single-cell RNA-sequencing and spatial transcriptomics to analyze epithelial and fibroblast states in hormone therapy-naive prostate cancer, identifying novel phenotypes and molecular perturbations.

SuperFocus integrates histopathology with single-cell spatial multi-omics data from spot-based measurements, improving accuracy by 28-73% over existing methods.

The paper introduces MAGPIE, a framework for integrating spatial transcriptomics, metabolomics, and tissue morphology data, demonstrating its application in studying drug-induced pulmonary fibrosis.

This study shows that disrupting microglial TGF-β signaling causes region-specific demyelination in the spinal cord, with severity varying by age and sex.

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.

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

This study maps the spatial organization of the developing human lung using high-parametric protein imaging, revealing cell type composition and temporal changes across five developmental timepoints.

This study maps spatial transcriptomes of human and mouse pulmonary fibrosis to identify fibrotic niches and differences in alveolar regeneration.

AAnet identifies archetypal cell states in single-cell data to reveal tumor heterogeneity and spatial organization.

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

The paper identifies heritable transcriptional diversity within cell types by studying clonal human lymphocytes and mouse brain cells, showing clone-specific chromatin accessibility leads to distinct gene expression.

This study uses spatial transcriptomics and snRNA-seq to analyze host-pathogen interactions in P. berghei-infected mouse liver tissues, revealing changes in lipid metabolism and inflammatory responses.

This study introduces a method to generate high-quality single-nucleus transcriptomic data from FFPE samples using recent single-cell genomic technology.

This study creates a comprehensive cell atlas of the first-trimester human brain using single-cell RNA sequencing and spatial transcriptomics, identifying over 600 distinct cell states and their spatial organization.

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 SiT, a method to characterize spatial isoform variation and sequence heterogeneity in tissue sections, revealing regional isoform switching and RNA editing in mouse brain.

The paper uses spatial transcriptomic data to show that bulk genomic signature scoring in prostate cancer biopsies is confounded by tumour heterogeneity, leading to inaccurate risk assessments.

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 creates a comprehensive topographic atlas of early human lung development, identifying cell states, trajectories, and interactions to understand cell heterogeneity origins.

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.