The local molecular signature of human peripheral neuropathic pain
Bibliographic record
Abstract
ABSTRACT: Focal nerve injuries are often associated with neuropathic pain. Preclinical research suggests altered neuroimmune signalling underlies such neuropathic pain; however, its cause remains poorly understood in humans. In this multicentre cohort study, we describe the local cellular and molecular signature of neuropathic pain at the lesion site, using Morton's neuroma as a human model system of neuropathic pain (n = 22; 18 women) compared with nerves from participants without nerve injury (n = 11; 4 women). Immunofluorescent staining revealed demyelination and chronic infiltration of immune cells in Morton's neuroma. RNA bulk sequencing identified 3349 differentially expressed genes between Morton's neuroma and controls. Gene ontology enrichment analysis and weighted gene co-expression network analyses revealed modules specific for host defence and neurogenesis. Deconvolution analysis confirmed higher densities of macrophages and B cells in Morton's neuroma than control samples. Modules associated with defence response, neurogenesis, and muscle system development as well as macrophage cell populations identified by deconvolution correlated with patients' paroxysmal or evoked pain. Of note, we identified a consistently differentially expressed gene signature ( MARCO, CD163, STAB1 ) , indicating the presence of a specific M(GC) subset of macrophages. MARCO gene expression correlated with paroxysmal pain. Targeted immunofluorescent analyses confirmed higher densities of intraneural CD163 + MARCO + macrophage subsets in Morton's neuroma. Our findings provide detailed insight into the local molecular signature in the context of human focal nerve injury. There is clear evidence for an ongoing role of the immune system in chronic peripheral neuropathic pain in humans, with macrophages and specifically the M(GC) MARCO + subset implicated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".