Alma Andersson

Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

The paper develops a framework to decode TLS maturation in NSCLC using spatial omics data and deep learning, identifying two distinct spatial ecosystems linked to patient outcomes.

Inferring cellular communication through mapping cells in space using Tangram 2

Tangram2 maps cell-to-cell communication by integrating spatial transcriptomics with single-cell RNA sequencing to identify gene expression changes driven by neighboring cell types.

Spatial landmark detection and tissue registration with deep learning

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

Decoding spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin

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.

Spatial Transcriptomics to define transcriptional patterns of zonation and structural components in the liver

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

Genome-wide Spatial Expression Profiling in FFPE Tissues

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.

Super-resolved spatial transcriptomics by deep data fusion

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

A Landmark-based Common Coordinate Framework for Spatial Transcriptomics Data

The paper introduces a method to align spatial transcriptomics data into a common coordinate framework and demonstrates its application on real and synthetic datasets.

The spatial landscape of clonal somatic mutations in benign and malignant tissue

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

Spatial mapping of cell types by integration of transcriptomics data

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