Single cell molecular alterations reveal target cells and pathways of concussive brain injury
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Abstract
The complex neuropathology of traumatic brain injury (TBI) is difficult to dissect, given the convoluted cytoarchitecture of affected brain regions such as the hippocampus. Hippocampal dysfunction during TBI results in cognitive decline that may escalate to other neurological disorders, the molecular basis of which is hidden in the genomic programs of individual cells. Using the unbiased single cell sequencing method Drop-seq, we report that concussive TBI affects previously undefined cell populations, in addition to classical hippocampal cell types. TBI also impacts cell type-specific genes and pathways and alters gene co-expression across cell types, suggesting hidden pathogenic mechanisms and therapeutic target pathways. Modulating the thyroid hormone pathway as informed by the T4 transporter transthyretin Ttr mitigates TBI-associated genomic and behavioral abnormalities. Thus, single cell genomics provides unique information about how TBI impacts diverse hippocampal cell types, adding new insights into the pathogenic pathways amenable to therapeutics in TBI and related disorders.
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Within the compendium, fourteen studies had control groups that included six or more samples.
“Within our compendium, we identified a total of fourteen studies with control groups that included six or more samples5,6,15,49–59.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [51]
The Fig. 9 caption reports six Sham/Veh controls for behavioral tests, so this paper does contain a control group of six. But the Methods report only three Sham samples in the Drop-seq data and four per group selected for T4-treatment sequencing. The paper cannot establish the citing authors’ count of fourteen studies or show that this study had six control sequencing samples. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCell clusters were annotated manually using marker genes conserved between both conditions as astrocytes, microglia, neurons, endothelial cells, ependymal cells, and mixed glia.
“The cell clusters were annotated manually using marker genes that were conserved between both conditions as astrocytes, microglia, neurons, endothelial cells, ependymal cells, and mixed glia (Supplementary Data 1) (Arneson et al., 2018;McKenzie et al., 2018).”
From Single-cell resolution analysis of the crosstalk between chemogenically activated astrocytes and microglia (2020) · cites this paper as Arneson et al., 2018
The Methods section, “Resolving cell identities of the cell clusters” (page unavailable), says the authors assigned cluster identities using known cell-type markers and marker-gene overlap. The Results section, “Unbiased identification of cell identities in hippocampus” (page unavailable), identifies astrocytes, microglia, neurons, endothelial cells, and ependymal cells. Our copy does not say the markers were selected for being conserved between conditions or identify a cluster as “mixed glia.” (LLM, conf=0.88)
Experimental — accuracy not yet measuredAll cell clusters were annotated manually based on marker genes that were conserved between the saccharin group and the CNO group and gene lists present in literature.
“All cell clusters were annotated manually based on marker genes that were conserved between the saccharin group and the CNO group and gene lists present in literature (Arneson et al., 2018;McKenzie et al., 2018).”
From Single-cell resolution analysis of the crosstalk between chemogenically activated astrocytes and microglia (2020) · cites this paper as Arneson et al., 2018
The Methods section, “Resolving cell identities of the cell clusters” (page unavailable), describes assigning cluster identities using curated markers from previous studies and checking their expression in the clusters. It does not describe markers conserved between saccharin and CNO groups, so the cited paper supports the literature-marker approach but not that group-specific detail. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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- Authors
- Arneson, D., Zhang, G., Ying, Z., Zhuang, Y., Byun, H. R., Ahn, I. S., Gomez-Pinilla, F., & Yang, X.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Yang, Xia
- Venue
- Nature Communications
- Published
- 2018-09-25
- Volume
- 9
- Issue
- 1
- Pages
- 3894-3894
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6156584
- OpenAlex
- W2892146033
- Cited by
- 193
- References
- 66
- Citations by year
- 2026: 14 · 2025: 27 · 2024: 30 · 2023: 24 · 2022: 28 · 2021: 34 · 2020: 24 · 2019: 11
- FWCI
- 13.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (R01 DK104363, R21 NS103088, R01 NS50465)
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (T32CA201160)
- UCLA Hyde Fellowship
- Topics
- Health Sciences › Medicine › Epidemiology › Traumatic Brain Injury Research
- Health Sciences › Medicine › Neurology › Traumatic Brain Injury and Neurovascular Disturbances
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- traumatic brain injury, concussion, hippocampus, single-cell transcriptomics, Drop-seq, gene co-expression, prealbumin, cognitive decline, therapeutic target
- MeSH terms
- Animals, Brain Concussion, Gene Expression Regulation, Hippocampus, Male, Mice, Inbred C57BL, Prealbumin, Thyroxine, Signal Transduction, Maze Learning, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Gene Ontology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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