Cerebrospinal fluid immune cells appear similar across neuropathic and non-neuropathic pain conditions
Bibliographic record
Abstract
Background Microglia have been implicated in the pathophysiology of neuropathic pain. Here, we sought to investigate whether cerebrospinal fluid (CSF) might be used as a proxy-measure of microglial activation in human participants. Methods We preformed fluorescence-activated cell sorting (FACS) of CSF immune cell populations derived from individuals who experienced pain with neuropathic features. We sorted CD4+, CD8+ T cells and monocytes and analyzed their transcriptome using RNA sequencing. We also performed Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing to characterize the expression of all CSF immune cells in a patient with postherpetic neuralgia and in a patient with neuropathic pain after failed back surgery. Results Immune cell numbers and phenotypes were not obviously different between individuals regardless of the etiology of their pain. This was true when examining our own dataset, as well as when comparing it to previously published single-cell RNA sequencing data of human CSF. In all instances, CSF monocytes showed expression of myeloid cell markers commonly associated with microglia ( P2RY12 , TMEM119 and OLFML3 ), which will make it difficult to ascertain the origin of CSF proteins: do they derive directly from circulating CSF monocytes or could some originate in spinal cord microglia in the parenchyma? Conclusions We conclude that it will not be straightforward to use CSF as a biomarker for microglial function in humans.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".