Sergio Marco Salas

TERRA is a foundation model for spatial transcriptomics that predicts tissue behavior under perturbation and identifies multi-scale biological insights across tissues.

This paper introduces a tool to detect overlapping cells and segmentation errors in 3D spatial transcriptomics data.

The paper analyzes unassigned RNAs in spatial transcriptomics data, identifies biological relevance, and introduces a tool for their study.

This study identifies tissue architecture and immune niches as key factors influencing ctDNA release in colorectal cancer by integrating spatial transcriptomics and in situ mutation detection.

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.

The paper generates a single-cell and spatial transcriptomic atlas of human fetal meninges and finds that meningiomas likely originate from dura lineage cells.

This paper adapts copy number variation inference to image-based spatial transcriptomics data to map malignant clones and tumor microenvironment at single-cell resolution.

The paper uses snRNA-seq and spatial transcriptomics to study oligodendroglia in Parkinson's disease, identifying disease-specific subclasses and transcriptomic changes linked to myelin integrity and cell communication.

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 compares the development and evolution of sensory circuits in the pallium of birds, lizards, and mammals, revealing divergent developmental trajectories but functional similarity in sensory processing.

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.

The paper uses snRNA-seq to identify eight interneuron classes in the human dorsal striatum, validates markers with in situ hybridization, and compares them to mouse interneurons.

The paper introduces an open-source method for high-throughput in-situ transcriptomics and provides a computational workflow for data analysis.

BirthSeq isolates and analyzes cells based on their birth date across vertebrate tissues, enabling temporal studies of cell fate in development.

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 uses a de novo spatial modeling approach to identify novel cell populations in the developing human heart, revealing previously unreported molecular diversity.

This paper introduces a direct RNA detection method called HybRISS, which improves transcript detection efficiency compared to cDNA-based in situ sequencing and enables multiplexed spatial transcriptomic analysis of tissues.

This paper introduces HybISS, a spatial transcriptomic method that improves detection flexibility, multiplexing, and signal-to-noise while maintaining throughput efficiency in human and mouse brain tissue.