Biology subjects

Thijssen, J.

Publications and source records attributed to Thijssen, J..

4 recordsLinked to original sources

bioRxiv2026

Morphoelectric Diversity and Specialization of Neuronal Cell Types in the Primate Striatum

The basal ganglia are evolutionary ancient subcortical nuclei that form interconnected loops with the neocortex and limbic system to regulate movement, learning, habit formation, emotion, and motivation. Their dysfunction contributes to major neurological and psychiatric disorders, yet most cellular-level insights derive from rodent studies, leaving knowledge gaps in humans and translationally relevant primate species. To address this, we generated multi-modal Patch-seq data linking transcriptomic identity with morphological and electrophysiological properties in macaque striatum, the input nucleus of the basal ganglia. We found underappreciated diversity among medium spiny neurons, including non-canonical types, and variation aligned with functional gradients. Interneurons also exhibited spatial variation and even greater morphoelectric diversity, highlighting their functional modularity. Despite broad evolutionary conservation, we identified primate-specific features and key differences from rodent striatal neurons. By integrating molecular classification with cellular properties that shape network function, our findings provide insights into the functional organization of the primate striatum.

neuroscience↗

bioRxiv2026

Human Neocortical Glutamatergic Neurons Revealed Through Multimodal Profiling

The human neocortex underlies higher cognition and is the engine of complex thought. Yet our understanding of its neuronal diversity is limited by sparse access to tissue, inconsistent sampling across studies, and a lack of multiple modality data. Although single-cell transcriptomic taxonomies are an important framework for characterizing cell type diversity, transcriptomic information alone cannot reveal the cellular properties that define neuronal computations. To address this, we performed Patch-seq, a method for collecting Morphology, Electrophysiology, and Transcriptomic data from a single neuron. We focused on glutamatergic, neocortical, excitatory neurons, the principal long-range projecting neurons of the cortex, and systematically integrated their morphoelectric features with transcriptomic identity. In combination with spatial transcriptomic data, we interrogated 39 of 42 transcriptomically-defined neuron types with a layer-centric perspective. Morphoelectric properties, such as cortical depth, apical dendrite structure, and excitability clearly distinguish transcriptomic subclasses and support many finer transcriptomic types. Morphoelectric properties are influenced by spatial location in supragranular layers, while deeper layers exhibit greater heterogeneity. Cross-species comparisons reveal conserved subclass organization but pronounced differences in apical dendrite arborization between mouse and human, and surprising similarities between human and macaque. Together, these datasets provide a unified multimodal reference that advances our understanding of human cortical circuitry and establishes a foundation for experimental and computational studies of human brain function and disease.

neuroscience↗

bioRxiv2024

GeneSurfer Enables Transcriptome-wide Exploration and Functional Annotation of Gene Co-expression Modules in 3D Spatial Transcriptomics Data

Gene co-expression provides crucial insights into biological functions, however, there is a lack of exploratory analysis tools for localized gene co-expression in large-scale datasets. We present GeneSurfer, an interactive interface designed to explore localized transcriptome-wide gene co-expression patterns in the 3D spatial domain. Key features of GeneSurfer include transcriptome-wide gene filtering and gene clustering based on spatial local co-expression within transcriptomically similar cells, multi-slice 3D rendering of average expression of gene clusters, and on-the-fly Gene Ontology term annotation of co-expressed gene sets. Additionally, GeneSurfer offers multiple linked views for investigating individual genes or gene co-expression in the spatial domain at each exploration stage. Demonstrating its utility with both spatial transcriptomics and single-cell RNA sequencing data from the Allen Brain Cell Atlas, GeneSurfer effectively identifies and annotates localized transcriptome-wide co-expression, providing biological insights and facilitating hypothesis generation and validation.

bioinformatics↗

bioRxiv2023

SpaceWalker: Interactive Gradient Exploration for Spatial Transcriptomics Data

Spatial transcriptomics (ST) enables profiling the expression of hundreds of genes in tissue sections, down to the level of single cells in their tissue environment. The gradient structure of ST data is particularly interesting for tissue biology, since spatial gene expression gradients often represent tissue compartment edges, whereas in the single-cell transcriptomic domain, gene expression gradients may represent cell type differences and smooth phenotypic transitions. Various computational approaches have been developed to extract information from either the spatial domain or gene expression domain individually. However, integrative biological interpretation of expression gradients in single cell and ST data spaces remains challenging. Many prior spatial transcriptomics analysis pipelines are script-based, lack interactive exploration facilities, and do not have specific facilities for automatic identification of localized expression gradients. Here, we present SpaceWalker, a visual analytics tool for exploring the local gradient structure of ST data. The user is guided by the local intrinsic dimensionality of the high-dimensional data to define seed locations, from which a flood-fill algorithm approximates k-nearest neighbor subgraph topology on the fly. In several use cases, we demonstrate that the spatial projection of these local subgraphs highlights tissue architectural features, and that interactive retrieval of gene expression gradients in the spatial and transcriptomic domains confirms known biology, and provides additional insights into the tissue architecture. We also show that SpaceWalker generalizes to several different ST protocols, and scales well to large, multi-slice, whole-brain ST data, while maintaining real-time interaction performance.

bioinformatics↗