Cellular Exploration and REsolution of BRain Injury
This interactive single-cell atlas is the culmination of an extensive collaborative research effort investigating
heterogeneity across three well established and clinically relevant murine traumatic brain injury (TBI) models.
In addition to extrinsic-model based differences, we also explore intrinsic differences in the response to TBI as foundational steps
demonstrating the cell-specific complexity of response to a primary TBI- these include sex differences, the effect of distance from the impact site,
and time from injury. In this interactive web-based platform, we have documented several key variables across all these variables that can be explored
and customized based on individual preferences. Please click the button below to read our Abstract, or select one of the tabs in the navigation bar to
explore the atlas. This is a living-atlas in that we are continuing to build upon this with additional samples and variables.
Lead Contact and Corresponding Author: Ruchira M. Jha, MD MSc
Division Chief | Neurocritical Care
Barrow Neurological Institute
St. Joseph's Hospital and Medical Center
240 W Thomas Road
Phoenix, AZ 85013
Email: Ruchira.jha@barrowneuro.org
Co-Corresponding Author and Shiny App Maintenance: Dhivyaa Rajasundaram, PhD
Director | Bioinformatics Core
Division of Health Informatics
University of Pittsburgh School of Medicine, Pittsburgh
Email: dhr11@pitt.edu
Co-Corresponding Authors: Gary Kohanbash, PhD
Assistant Professor of Neurological Surgery
UPMC Children's Hospital of Pittsburgh
Email: gary.kohanbash2@chp.edu
Patrick M. Kochanek, M.D., MCCM
Distinguished Professor of Critical Care Medicine
Ake N. Grenvik Professor and Vice Chairman of Critical Care Medicine
Professor of Anesthesiology and Perioperative Medicine, Pediatrics, and Clinical and Translational Science
Director | Safar Center for Resuscitation Research
UPMC Children's Hospital of Pittsburgh
Email: kochanekpm@pitt.edu
Sample Metadata
Reference
Jha et al., A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets, Neuron (2024), https://doi.org/10.1016/j.neuron.2024.06.021
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Abstract
SUMMARY: The problem of traumatic brain injury (TBI) heterogeneity has been a critical barrier to successful translation of therapies in the field. TBI heterogeneity exists in the patient substrate pre-injury (genetics, sex, comorbidities), external injury characteristics (severity, mechanism), and resultant post-injury host response that is responsible for deleterious secondary injury processes (seizures, neuroinflammation, neurodegeneration) and repair/regeneration. Identification of final common molecular pathways and signatures that integrate this vast heterogeneity could be valuable for guiding biomarkers, therapeutic targets, and predictive enrichment. In this study, we present the first large-scale searchable murine single-cell atlas of the transcriptomic response to TBI in 339,357 cells across three levels of clinically relevant injury models, sex, distance and time from injury, as a foundational step in molecularly deconstructing TBI heterogeneity. We identify 23 cell types with massive heterogeneity in the single-cell response across these extrinsic (severity) and intrinsic (sex, brain region, time) factors, that has been underestimated. Majority of response to TBI was unique to individual cell populations with minimal overlap even within a single injury-model thus highlighting the importance of cell-level resolution. Through this effort, we report novel cell-specific targets and a previously unrecognized role for specific microglial and ependymal subtypes in post-TBI pathophysiology that is highly variable depending on the extrinsic and intrinsic factors studied. One ependymal subtype was a hub of neuroinflammatory signaling after contusional-TBI, particularly related to Il-1b. A single microglial-lineage along pseudotime (comprising 3 microglial subtypes) was a key mediator of host-response after TBI, and shared features with disease associated microglia noted in Alzheimer's disease and other neurodegenerative disorders, potentially providing a link between TBI and accelerated neurodegeneration. One microglial subtype within this lineage emerged as a key target – it was the only cell type of all 23 that retained persistent and marked gene expression changes 6 months post contusional-TBI. We identify sexually dimorphic gene expression and pathway vulnerabilities with cell-specific differences in both immune and non-immune biological processes. These likely contribute to sex-based outcome and warrant further study to facilitate discovery of cell- and sex-specific druggable targets. Active changes in brain regions distal from the site of primary TBI impact included infiltration of specific microglial populations as well as cell-specific transcriptomic changes in several genes and inflammatory processes distinct from both the peri-contusional and naïve signatures. This atlas validates several known contributors in TBI pathophysiology, and also identifies previously unrecognized targets and avenues for further research. Beyond our presented exemplar analyses (including pathways of clinical interest like sulfonylurea-receptor-1), the companion searchable atlas serves as a foundation for countless future efforts to understand cell-specific heterogeneity after TBI (https://shiny.crc.pitt.edu/cerebri/) as well as numerous other neurological diseases with overlapping pathophysiology.
-Jha et al. (in preparation, 2024)
CCI = controlled cortical impact,rCHI = repetitive closed head injury,HS = hemorrhagic shock,24h = 24 hours after primary impact,6mo = 6 months after primary impact,7days = 7 days after primary impact,
Cell Type and Gene Expression on reduced dimensions
In this tab, users can visualise both cell information and gene
expression side-by-side on low-dimensional representions.
In this tab, users can visualise the gene expression patterns of
multiple genes grouped by clusters.
The normalised expression are averaged, log-transformed and then plotted.
In this tab, users can visualise the gene expression patterns of
multiple genes grouped by clusters.
The normalised expression are averaged, log-transformed and then plotted.
In this tab, users can visualise the gene expression patterns of
multiple genes grouped by clusters.
The normalised expression are averaged, log-transformed and then plotted.
In this tab, users can visualise the gene expression patterns of
multiple genes grouped by clusters.
The normalised expression are averaged, log-transformed and then plotted.