Idania Carolina Lubo Julio

UniST reconstructs dense 3D spatial transcriptomics data from sparse serial sections, restoring structural continuity and biologically meaningful expression patterns in multiple tissue contexts.

COSIE is a computational framework that integrates multiple spatial omics modalities to generate high-resolution molecular landscapes, outperforming existing methods across diverse datasets.

This study introduces a deep learning model called spacer to analyze spatially resolved transcriptomics data, revealing novel insights into cellular recruitment mechanisms in tumors and heart tissue during myocarditis.

This study compares imaging-based spatial transcriptomics platforms using FFPE tumor samples, finding differences in performance and highlighting factors affecting data quality.