Paulo Czarnewski

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.

This study evaluates the Xenium In Situ spatial transcriptomics platform using mouse brain and human breast cancer datasets, comparing it to other technologies and benchmarking analysis tools.

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

This study shows that Hippo-deficient cardiac fibroblasts differentiate into osteochondroprogenitors and interact with macrophages, promoting fibrosis through CSF1 signaling.

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.

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.

This study uses single-cell transcriptomics and spatial analysis to map early mouse skin development, revealing new cell types and interactions.

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

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