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Record W4406325056 · doi:10.1016/j.neuron.2024.12.012

Clonally expanded, targetable, natural killer-like NKG7 T cells seed the aged spinal cord to disrupt myeloid-dependent wound healing

2025· article· en· W4406325056 on OpenAlexaff
Guiping Kong, Yayue Song, Yuyang Yan, Samantha Calderazzo, Madhu Sudhana Saddala, Fabian de Labastida Rivera, Jonathan D. Cherry, Noah Eckman, Eric A. Appel, Adam Velenosi, Vivek Swarup, Riki Kawaguchi, Susanna S. Ng, Brian Kwon, David Gate, Christian Engwerda, Luming Zhou, Simone Di Giovanni

Bibliographic record

VenueNeuron · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInternational Collaboration On Repair Discoveries
FundersMedical Research Council
KeywordsBiologyWound healingMyeloidImmunologySpinal cord injuryChemokineCD8InflammationCytotoxic T cellT cellCell biologyCancer researchSpinal cordImmune systemNeuroscience

Abstract

fetched live from OpenAlex

Spinal cord injury (SCI) increasingly affects aged individuals, where functional impairment and mortality are highest. However, the aging-dependent mechanisms underpinning tissue damage remain elusive. Here, we find that natural killer-like T (NKLT) cells seed the intact aged human and murine spinal cord and multiply further after injury. NKLT cells accumulate in the spinal cord via C-X-C motif chemokine receptor 6 and ligand 16 signaling to clonally expand by engaging with major histocompatibility complex (MHC)-I-expressing myeloid cells. NKLT cells expressing natural killer cell granule protein 7 ( Nkg7 ) disrupt myeloid-cell-dependent wound healing in the aged injured cord. Nkg7 deletion in mice curbs NKLT cell degranulation to normalize the myeloid cell phenotype, thus promoting tissue repair and axonal integrity. Monoclonal antibodies neutralizing CD8 + T cells after SCI enhance neurological recovery by promoting wound healing. Our results unveil a reversible role for NKG7 + CD8 + NKLT cells in exacerbating tissue damage, suggesting a clinically relevant treatment for SCI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.258
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

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