Boudewijn P. F. Lelieveldt

The paper analyzes multi-modal Patch-seq data from macaque striatum to reveal morphoelectric diversity and functional specialization of neuronal cell types, highlighting primate-specific features and differences from rodents.

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.

This paper creates a cross-species spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, revealing conserved molecular organization and cell type-specific compartments.

The paper presents a spatially resolved transcriptomic and gene regulatory atlas of primate subcortical regions, revealing spatially tuned transcriptional and regulatory programs across cell types.

GeneSurfer explores and annotates gene co-expression modules in 3D spatial transcriptomics data, identifying localized transcriptome-wide co-expression patterns.

SpaceWalker explores spatial transcriptomics data gradients to reveal tissue architecture and gene expression patterns through interactive visualization.

This study uses single nucleus transcriptomics to compare human and non-human primate cortical tissues, identifying human-specific gene expression patterns and their potential functional roles.

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.

This study evaluates six algorithms for cell type matching in spatial transcriptomics data and presents consensus strategies for improved analysis.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

NS-Forest identifies minimal marker gene combinations for cell type determination from scRNA-seq data, revealing key roles of signaling and non-coding RNAs in neuronal identity.

SIRV infers spatial RNA velocities at single-cell resolution by integrating spatial transcriptomics with scRNA-seq data, revealing spatial differentiation patterns in developing tissues.

The paper identifies iron-accumulating microglia in Alzheimer's brains, linking iron uptake to microglial activation and disease pathology.

Cytosplore-Transcriptomics provides a scalable interactive framework for analyzing scRNA-seq data, enabling visualization and downstream analysis of large datasets.

SCHNEL is a scalable clustering tool for high-dimensional single-cell data that outperforms existing methods on cytometry datasets and handles large datasets efficiently.

The paper identifies transcriptomic signatures of brain regional vulnerability to Parkinson’s disease by analyzing gene expression patterns across Braak Lewy body stages in human brain datasets.

The paper compares cell types in human and mouse cortex using single nucleus RNA-sequencing, finding conserved architecture with species-specific differences.

The paper develops a structural equation model that integrates genetic variants and brain imaging features using spatial transcriptomics data to study Alzheimer's disease.

The paper introduces HSNE for analyzing mass cytometry data, revealing rare cell types and overcoming t-SNE's scalability limitations.