Christophe Avenel

SFUMATO provides a Bayesian probabilistic framework for spatial transcriptomics that captures uncertainty and enables continuous visualization of transcriptional domains.

The paper presents a protocol combining DNA FISH and multiplexed immunofluorescence to analyze gene and protein expression in large tissue sections at the single-cell level.

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 paper introduces misPLA, a method for spatially mapping protein interactions and activity states in cancer models, revealing shared and disease-specific signaling patterns.

PIP{Sigma}X provides an automated, user-friendly pipeline for analyzing multiplexed tissue images, enabling robust spatial analysis and integration with visualization tools.

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.

Points2Regions clusters spatial transcriptomics data to identify biologically relevant regions across multiple scales efficiently and interactively.

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.

TissUUmaps 3 improves interactive visualization and exploration of large-scale spatial omics data through optimizations and new modules for analysis and quality assessment.

The paper introduces three plugins for visualization and quality control in large-scale multiplex tissue analysis to improve accuracy and user trust in automated approaches.

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