Annika Vannan

The paper combines two spatial transcriptomic chemistries to improve both sensitivity and coverage in a single tissue section, showing high concordance and enabling more comprehensive analysis.

The paper introduces SpatialRNA, a Python package enabling the application of Graph Neural Networks to spatial transcriptomics data for domain detection.

DenoIST improves spatial transcriptomics data by removing contaminating transcripts, enhancing gene expression specificity and cell type annotation.

This paper creates a spatial transcriptomic atlas of acute neonatal lung injury across development and disease severity using 1.2 million cells from 17 human infant lungs.

Xenium profiling of 343 genes in 1,126,820 nuclei from 19 human lungs maps fibrosis niches and orders alveolar remodeling from alveolar-capillary disruption to SPP1+ macrophage accumulation.