Macrophages amplify the spontaneous activity of damaged sensory neurons in a human co-culture model of neuropathic pain
Bibliographic record
Abstract
Abstract Neuropathic pain is a highly prevalent condition for which treatments are hampered by low efficacy and dose-limiting side-effects. Injury to the somatosensory nervous system causes maladaptive plasticity that initiates and maintains chronic pain. Emerging evidence suggests that inflammatory cells of the innate immune system shape the response of the injured nervous system and thereby contribute to the pathogenesis of pain. Data from preclinical models and human patient biopsies have specifically implicated peripheral macrophage populations for a pro-algesic role, yet how these cell types influence damaged sensory neurons and whether they directly contribute to neuronal hyperexcitability is unclear. Here, we have developed an iPSC co-culture system to study the interactions of macrophages and sensory neurons in a fully humanised experimental model. We found that analogous to endogenous counterparts, iPSC-derived macrophages (iMacs) display a dynamic molecular and functional profile that is highly dependent on neuronal state. Co-culture with injured iPSC-derived sensory neurons (iSNs) induces morphological, gene expression, and secretory profile changes in iMacs that are consistent with the response of macrophages to nerve injury in vivo . iMacs in turn amplify spontaneous firing in damaged sensory neurons, implicating macrophages in this cardinal feature of neuropathic pain. These results illustrate the utility of an iPSC-based model to study signalling between these two cell types; they support a role for macrophages in directly amplifying damaged sensory neuron activity and highlight disrupting pathological signalling between these cell types as a promising strategy for future analgesic drug development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".