An Open‐Source Tool for Investigation of Differential RNA Expression Between Spinal Cord Cells of Male and Female Mice
Bibliographic record
Abstract
Chronic pain is a highly debilitating condition that differs by type, prevalence, and severity between men and women. To uncover the molecular underpinnings of these differences, it is critical to analyze the transcriptomes of spinal cord pain-processing networks for both sexes. Despite several recently published single-nucleus RNA-sequencing (snRNA-seq) studies on the function and composition of the mouse spinal cord, a gene expression analysis investigating the differences between males and females has yet to be performed. Here, we combined data from three different large-scale snRNA-seq studies, which used sex-identified adult mice. Using SeqSeek, we classified more than 37,000 unique viable cells within predicted cell types with the use of machine learning. We then utilized DESeq2 to identify significant differentially expressed genes (DEGs) between males and females in a variety of cell populations, including superficial dorsal horn (SDH) neurons. We found a large number of DEGs between males and females in all cells, in neurons, and in SDH neurons of the mouse spinal cord, with a greater level of differential expression in inhibitory SDH neurons compared to excitatory SDH neurons. The results of these analyses are available on an open-source web-app: https://justinbellavance.shinyapps.io/snRNA_Visualization/. Lastly, we used gene set enrichment analysis to identify sex-enriched pathways from our previously identified DEGs. Through this, we have identified specific genetic players within the rodent spinal cord that diverge between males and females, which may underlie reported sex differences in spinal nociceptive mechanisms and pain processing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.043 |
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".