Biology subjects

Ruitenberg, M. J.

Publications and source records attributed to Ruitenberg, M. J..

3 recordsLinked to original sources

bioRxiv2026

Gp130 Orchestrates a Bidirectional Neuron-Microglia Circuit for neuroprotection

Acquired central nervous system (CNS) injury is one of the most common neurological conditions globally, yet effective treatment options are lacking. As the main tissue-resident macrophages of the CNS, microglia have emerged as key functional regulators of CNS repair. However, means to induce neuroprotective microglia and harness their intrinsic repair capabilities have remained elusive. Here we identify gp130 as a key receptor molecule for facilitating bidirectional microglia-neuron communication that improves outcomes from CNS injury. We show that activation of gp130 in CNS-resident microglia triggers the secretion of leukemia inhibitory factor (LIF), a neurotrophic cytokine. LIF induces neuronal IL-6 secretion that then acts back onto the microglial gp130 receptor, thus creating a neuroprotective loop. We demonstrate the broad therapeutic potential of acute gp130 activation across multiple models, including traumatic brain injury, stroke, and spinal cord injury, and that this pathway can be leveraged therapeutically with designer cytokines.

neuroscience↗

bioRxiv2022

The neuroprotective effects of estrogen and estrogenic compounds in spinal cord injury

Spinal cord injury (SCI) occurs when the spinal cord is damaged from either a traumatic event or disease. SCI is characterised by multiple injury phases that affect the transmission of sensory and motor signals and lead to temporary or long-term functional deficits. There are few treatments for SCI. Estrogens and estrogenic compounds, however, may effectively mitigate the effects of SCI and therefore represent viable treatment options. This review systematically examines the pre-clinical literature on estrogen and estrogenic compound neuroprotection after SCI. Several estrogens were examined by the included studies: estrogen, estradiol benzoate, Premarin, isopsoralen, genistein, and selective estrogen receptor modulators. Across these pharmacotherapies, we find significant evidence that estrogens indeed offer protection against myriad pathophysiological effects of SCI and lead to improvements in functional outcomes, including locomotion. A STRING functional network analysis of proteins modulated by estrogen after SCI demonstrated that estrogen simultaneously upregulates known neuroprotective pathways, such as HIF-1, and downregulates pro-inflammatory pathways, including IL-17. These findings highlight the strong therapeutic potential of estrogen and estrogenic compounds after SCI.

neuroscience↗

bioRxiv2020

stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues.

bioinformatics↗