Expression of CCR2 in Both Resident and Bone Marrow-Derived Microglia Plays a Critical Role in Neuropathic Pain
Bibliographic record
Abstract
Neuropathic pain resulting from damage to or dysfunction of peripheral nerves is not well understood and difficult to treat. Although CNS hyperexcitability is a critical component, recent findings challenge the neuron-centric view of neuropathic pain etiology and pathology. Indeed, glial cells were shown to play an active role in the initiation and maintenance of pain hypersensitivity. However, the origins of these cells and the triggers that induce their activation have yet to be elucidated. Here we show that, after peripheral nerve injury induced by a partial ligation on the sciatic nerve, in addition to activation of microglia resident to the CNS, hematogenous macrophage/monocyte infiltrate the spinal cord, proliferate, and differentiate into microglia. Signaling from chemokine monocyte chemoattractant protein-1 (MCP-1, CCL2) to its receptor CCR2 is critical in the spinal microglial activation. Indeed, intrathecal injection of MCP-1 caused activation of microglia in wild-type but not in CCR2-deficient mice. Furthermore, treatment with an MCP-1 neutralizing antibody prevented bone marrow-derived microglia (BMDM) infiltration into the spinal cord after nerve injury. In addition, using selective knock-out of CCR2 in resident microglia or BMDM, we found that, although total CCR2 knock-out mice did not develop microglial activation or mechanical allodynia, CCR2 expression in either resident microglia or BMDM is sufficient for the development of mechanical allodynia. Thus, to effectively relieve neuropathic pain, both CNS resident microglia and blood-borne macrophages need to be targeted. These findings also open the door for a novel therapeutic strategy: to take advantage of the natural ability of bone marrow-derived cells to infiltrate selectively affected CNS regions by using these cells as vehicle for targeted drug delivery to inhibit hypersensitivity and chronic pain.
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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) | 0.000 | 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) | 0.001 | 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 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".