Biology subjects

Hollt, T.

Publications and source records attributed to Hollt, T..

10 recordsLinked to original sources

bioRxiv2025

A cross-species spatial transcriptomic atlas of the human and non-human primate basal ganglia

The basal ganglia are interconnected subcortical nuclei with complex topographical organization that orchestrate goal-directed behaviors and are implicated in neurodegenerative movement disorders. We generated a cellular-resolution, spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, sampling over one million cells in each species. By integrating spatial data with a cross-species, consensus snRNA-seq cell type taxonomy, this atlas reveals conserved principles of molecular organization within and across structures. The cellular architecture is complex but highly stereotyped, with gene expression gradients superimposed onto discrete compartments. Extensive spatial sampling illuminates 3D gradients of molecular organization in the striatum and reveals cell type-specific core and shell compartments in the primate internal globus pallidus, which is conserved with mouse. This unified, cross-species spatial transcriptomic atlas will be a foundational resource for characterizing the molecular and functional organization of the basal ganglia and their roles in health and disease.

neuroscience↗

bioRxiv2025

Spatial patterning of transcriptional and regulatory programs in the primate subcortex

Mammalian brain cell identity is shaped by intrinsic factors and external context. We present a spatially resolved transcriptomic and gene regulatory atlas of cell types across all subcortical regions in a primate, the common marmoset. Dense sampling and cross-species integration revealed spatially precise neuronal assemblies, including in complex midbrain and diencephalic structures. Chromatin accessibility and transcriptional identity are spatially tuned within and across subcortical structures; spatial gradients within hippocampal subfields are orchestrated by graded transcription factors acting through graded enhancers. The primate-expanded thalamic GABAergic population shares transcriptional and regulatory syntax with conserved midbrain populations, reflecting an evolutionary adaptation compared with rodents. Similar regional expression across cell types can arise by distinct regulatory architectures, as for telencephalic astrocytes and neurons. Conversely, distant cell types can share regulatory programs despite divergent identities: striatal GABAergic medium spiny neurons and telencephalic glutamatergic neurons share a postsynaptic regulatory program despite divergent lineage, region, and neurotransmitter identity.

neuroscience↗

bioRxiv2025

Tissue-embedded CD4⁺ plasticity structures mucosal immunity in IBD-related inflammation

CD4 T helper (Th) cell responses to commensal microbiota are linked to Inflammatory Bowel Disease (IBD), yet how Th programs coexist and evolve in human tissues remains poorly defined. Here, we profiled CD4 memory T cells in intestinal biopsies using immunological and histological approaches to map their phenotypes, functional states, and spatial relationships across disease states. A marked expansion of CD4 T cells concomitant with a ROR{gamma}t Th population with elevated T-bet expression was linked to progression of inflammation. Moreover, Foxp3 cells co-expressing ROR{gamma}t emerged within the inflamed niche, indicating regulatory-Th17 plasticity. Trajectory visualization revealed a potential branched differentiation path towards either regulatory or tissue-resident Th17-like fates, with both termini expressing activation and proliferation markers. Correlation network analysis connected pro-inflammatory CD4 states to T-betGranzyme-B CD8 subsets, indicative of crosstalk between helper and cytotoxic lineages. Histology corroborated this organization, showing frequent interactions between CD4 and CD8 cells in the lamina propria and epithelial border. In functional assays, TCR stimulation during active disease revealed broad suppression of CD4 pro-inflammatory cytokines concomitant with expansion of Foxp3 cells. Conversely, HLA-DR+CD38+ memory subset retained multifunctionality, producing elevated levels of pro-inflammatory cytokines. Together, these results provide insight into a dynamic, tissue-embedded CD4 landscape in IBD. HighlightsO_LIIBD inflammation shapes CD4+ T-cell plasticity and tissue-residency programming. C_LIO_LICorrelated CD4+ and cytotoxic CD8+ T cells co-localize in inflamed mucosa. C_LIO_LIMucosal CD4 memory T cells show hypo-responsiveness in active IBD. C_LIO_LIHLA-DR+CD38+ memory CD4 T cells amplify inflammation via cytokine output. C_LI

immunology↗

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↗

bioRxiv2022

Comparative transcriptomics reveals human-specific cortical features

Humans have unique cognitive abilities among primates, including language, but their molecular, cellular, and circuit substrates are poorly understood. We used comparative single nucleus transcriptomics in adult humans, chimpanzees, gorillas, rhesus macaques, and common marmosets from the middle temporal gyrus (MTG) to understand human-specific features of cellular and molecular organization. Human, chimpanzee, and gorilla MTG showed highly similar cell type composition and laminar organization, and a large shift in proportions of deep layer intratelencephalic-projecting neurons compared to macaque and marmoset. Species differences in gene expression generally mirrored evolutionary distance and were seen in all cell types, although chimpanzees were more similar to gorillas than humans, consistent with faster divergence along the human lineage. Microglia, astrocytes, and oligodendrocytes showed accelerated gene expression changes compared to neurons or oligodendrocyte precursor cells, indicating either relaxed evolutionary constraints or positive selection in these cell types. Only a few hundred genes showed human-specific patterning in all or specific cell types, and were significantly enriched near human accelerated regions (HARs) and conserved deletions (hCONDELS) and in cell adhesion and intercellular signaling pathways. These results suggest that relatively few cellular and molecular changes uniquely define adult human cortical structure, particularly by affecting circuit connectivity and glial cell function.

neuroscience↗

bioRxiv2021

Co-expression patterns of microglia markers Iba1, TMEM119 and P2RY12 in Alzheimer's disease

Microglia have been identified as key players in Alzheimers disease pathogenesis, and other neurodegenerative diseases. Iba1, and more specifically TMEM119 and P2RY12 are gaining ground as presumedly more specific microglia markers, but comprehensive characterization of the expression of these three markers individually as well as combined is currently missing. Here we used a multispectral immunofluorescence dataset, in which over seventy thousand microglia from both aged controls and Alzheimer patients have been analysed for expression of Iba1, TMEM119 and P2RY12 on a single-cell level. For all markers, we studied the overlap and differences in expression patterns and the effect of proximity to {beta}-amyloid plaques. We found no difference in absolute microglia numbers between control and Alzheimer subjects, but the prevalence of specific combinations of markers (phenotypes) differed greatly. In controls, the majority of microglia expressed all three markers. In Alzheimer patients, a significant loss of TMEM119+-phenotypes was observed, independent of the presence of {beta}-amyloid plaques in its proximity. Contrary, phenotypes showing loss of P2RY12, but consistent Iba1 expression were increasingly prevalent around {beta}-amyloid plaques. No morphological features were conclusively associated with loss or gain of any of the markers or any of the identified phenotypes. All in all, none of the three markers were expressed by all microglia, nor can be wholly regarded as a pan- or homeostatic marker, and preferential phenotypes were observed depending on the surrounding pathological or homeostatic environment. This work could help select and interpret microglia markers in previous and future studies.

neuroscience↗

bioRxiv2021

Iron-loading is a prominent feature of activated microglia in Alzheimer's disease patients

Brain iron accumulation has been found to accelerate disease progression in Amyloid {beta}-positive Alzheimer patients, though the mechanism is still unknown. Microglia have been identified as key-players in the disease pathogenesis, and are highly reactive cells responding to aberrations such as increased iron levels. Therefore, using histological methods, multispectral immunofluorescence and an automated in-house developed microglia segmentation and analysis pipeline, we studied the occurrence of iron-accumulating microglia and the effect on its activation state in human Alzheimer brains. We identified a subset of microglia with increased expression of the iron storage protein ferritin light chain (FTL), together with increased Iba1 expression, decreased TMEM119 and P2RY12 expression. This activated microglia subset represented iron-accumulating microglia and appeared morphologically dystrophic. Multispectral immunofluorescence allowed for spatial analysis of FTL+Iba1+-microglia, which were found to be the predominant A{beta}-plaque infiltrating microglia. Finally, an increase of FTL+Iba1+-microglia was seen in patients with high Amyloid-{beta} load and Tau load. These findings suggest iron to be taken up by microglia and to influence the functional phenotype of these cells, especially in conjunction with A{beta}.

neuroscience↗

bioRxiv2020

Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequenc-ing data analysis

The ever-increasing number of analyzed cells in Single-cell RNA sequencing (scRNA-seq) experiments imposes several challenges on the data analysis. Current analysis methods lack scalability to large datasets hampering interactive visual exploration of the data. We present Cytosplore-Transcriptomics, a framework to analyze scRNA-seq data, including data preprocessing, visualization and downstream analysis. At its core, it uses a hierarchical, manifold preserving representation of the data that allows the inspection and annotation of scRNA-seq data at different levels of detail. Consequently, Cytosplore-Transcriptomics provides interactive analysis of the data using low-dimensional visualizations that scales to millions of cells. AvailabilityCytosplore-Transcriptomics can be freely downloaded from transcriptomics.cytosplore.org Contactb.p.f.lelieveldt@lumc.nl

bioinformatics↗

bioRxiv2018

Conserved cell types with divergent features between human and mouse cortex

Elucidating the cellular architecture of the human neocortex is central to understanding our cognitive abilities and susceptibility to disease. Here we applied single nucleus RNA-sequencing to perform a comprehensive analysis of cell types in the middle temporal gyrus of human cerebral cortex. We identify a highly diverse set of excitatory and inhibitory neuronal types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to a similar mouse cortex single cell RNA-sequencing dataset revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of human cell type properties. Despite this general conservation, we also find extensive differences between homologous human and mouse cell types, including dramatic alterations in proportions, laminar distributions, gene expression, and morphology. These species-specific features emphasize the importance of directly studying human brain.

neuroscience↗

bioRxiv2017

Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analysed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry datasets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We applied HSNE to a study on gastrointestinal disorders and three other available mass cytometry datasets. We found that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional datasets.

bioinformatics↗