Raymond E.A. Sanchez

This paper introduces a standards framework for integrating single-cell and spatial omics data into brain cell atlases, enabling interoperable and reusable knowledge-ready resources.

The paper creates a cross-species consensus atlas of primate basal ganglia cell types using multiomic data from human, macaque, and marmoset, identifying 61 conserved cell types and a novel hybrid medium spiny neuron type.

The paper presents an enhancer AAV toolbox for targeting and perturbing striatal cell types and circuits in mice, with validated specificity and cross-species applicability.

The paper introduces MerQuaCo, an open-source tool to automatically detect and quantify imperfections in spatial transcriptomics data, improving quality assessment.

This paper creates a high-resolution transcriptomic and spatial cell type atlas of the whole adult mouse brain, revealing spatial organization and transcription factor codes for cell types.

The paper introduces a data-driven method to extend cell ontologies for multi-modal single-cell omics data, focusing on the Primary Motor Cortex in humans, mice, and marmosets.

The paper provides a guide to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem, its tools, and resources for analyzing brain cell types across species.

SIESTA automates sleep staging in rodents using machine learning, achieving high F1 scores for wakefulness, REM, and NREM sleep stages.