Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.01.13.523766

Micromotion derived fluid shear stress mediates peri-electrode gliosis through mechanosensitive ion channels

Trotier, A. F.·Bagnoli, E.·Walski, T.·Evers, J.·Pugliese, E.·Lowery, M. M.·Kilcoyne, M.·Fitzgerald, U.·Biggs, M.
View original source ↗Export metadata ↓

Abstract

Clinical applications for neural implant technologies are steadily advancing. Yet, despite clinical successes, neuroelectrode-based therapies require invasive neurosurgery and can subject local soft-tissues to micro-motion induced mechanical shear, leading to the development of peri-implant scaring. This reactive glial tissue creates a physical barrier to electrical signal propagation, leading to loss of device function. Although peri-electrode gliosis is a well described contributor to neuroelectrode failure, the mechanistic basis behind the initiation and progression of glial scarring remains poorly understood. Here, we develop an in silico model of electrode-induced shear stress to evaluate the evolution of the peri-electrode fluid-filled void, encompassing a solid and viscoelastic liquid/solid interface. This model was subsequently used to inform an in vitro parallel-plate flow model of micromotion mediated peri-electrode fluid shear stress. Ventral mesencephalic E14 rat embryonic in vitro cultures exposed to physiologically relevant fluid shear exhibited upregulation of gliosis-associated proteins and the overexpression of two mechanosensitive ion channel receptors, PIEZO1 and TRPA1, confirmed in vivo in a neural probe induced rat glial scar model. Finally, it was shown in vitro that chemical inhibition/activation of PIEZO1 could exacerbate or attenuate astrocyte reactivity as induced by fluid shear stress and that this was mitochondrial dependant. Together, our results suggests that mechanosensitive ion channels play a major role in the development of the neuroelectrode micromotion induced glial scar and that the modulation of PIEZO1 and TRPA1 through chemical agonist/antagonist may promote chronic electrode stability in vivo. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/523766v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@962f03org.highwire.dtl.DTLVardef@1e8a6adorg.highwire.dtl.DTLVardef@116cbb6org.highwire.dtl.DTLVardef@19830da_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPeri-electrode void progression is mediated by fluid flow shear stress C_LIO_LIOscillatory fluid flow shear stress replicates neuroelectrode glial scarring in vitro C_LIO_LIAstrocyte PIEZO1 and TRPA1 are upregulated at the peri-electrode region in response to electrode micromotion C_LIO_LIPIEZO1 pharmaceutical activation diminishes shear stress-induced gliosis C_LIO_LIPIEZO1 chemical inhibition exacerbates gliosis and reduces mitochondrial functions C_LI

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Trotier, A. F., Bagnoli, E., Walski, T., Evers, J., Pugliese, E., Lowery, M. M., Kilcoyne, M., Fitzgerald, U., Biggs, M.. 2023-01-13. Micromotion derived fluid shear stress mediates peri-electrode gliosis through mechanosensitive ion channels. https://doi.org/10.1101/2023.01.13.523766

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗