bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.09.21.508917

Brain mapping and spatial protein profiling reveal functional connectivity deficits and molecular changes following repetitive mild traumatic brain injury in wild-type mice.

Abstract

Repetitive mild traumatic brain injury (rmTBI) is a leading and severe threat to cognition that often goes undiagnosed. A major challenge in developing diagnostics and treatments for the consequences of rmTBI is the fundamental knowledge gaps that explain how rmTBI promotes brain dysfunction. It is both critical and urgent to understand the neuropathological and functional consequences of rmTBI to develop effective therapeutic strategies. In this study, we sought to define the extent of altered brain functional connectivity (FC) and expression of neuropathological markers after rmTBI. We performed two rmTBI (2x 0.6{square}J impacts 24{square}h apart) in male and female C57BL/6J wild-type (WT) (~2.5-3mo) mice using closed head injury model of engineered rotational acceleration (CHIMERA) or sham procedures. At 5-6 days post-injury (dpi), we measured changes in brain volume and FC using T2-weighted images, resting-state functional MRI (rsfMRI), and graph theory analyses. We used diffusion tensor imaging (DTI) to assess microstructural changes in white matter tracts. In addition, at 7dpi, we measured changes in Iba1 and GFAP to determine the extent of gliosis. The expression of disease-associated protein markers in grey and white matter regions were evaluated using the NanoString-GeoMx digital spatial protein profiling (DSP) platform. The rsfMRI data revealed aberrant changes in connectivity such as node clustering coefficient, global and local efficiency, participation coefficient, eigenvector centrality, and betweenness centrality in thalamus and other key brain regions that process visual, auditory, and somatosensory information. In addition, DTI revealed significantly decreased fractional anisotropy (FA) and axial diffusivity in the optic tract. Also, mean, radial, and axial diffusivity (L1) were significantly increased in the hippocampus. DSP revealed that phospho-serine 199 tau (pS199) as well as glial markers such as GFAP, cathepsin-D, and Iba1 were significantly increased in the optic tract. In thalamic nuclei, the neuroinflammatory marker GPNMB was increased significantly, and the cell proliferation marker Ki-67 was decreased in the rmTBI group. Our data suggest that rmTBI significantly alters brain functional connectivity and causes a profound inflammatory response in gray matter regions, beyond chronic white matter damage.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ravi, S., Criado-Marrero, M., Barroso, D., Braga, I. M., Bolen, M., Rubinovich, U., Hery, G. P., Grudny, M. M., Koren, J., Prokop, S., Febo, M., Abisambra, J. F.. 2022-09-22. Brain mapping and spatial protein profiling reveal functional connectivity deficits and molecular changes following repetitive mild traumatic brain injury in wild-type mice.. https://doi.org/10.1101/2022.09.21.508917

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

KEEP EXPLORING

Related preprints

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↗

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↗

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↗