A Cellular Taxonomy of the Adult Human Spinal Cord
Bibliographic record
Abstract
Abstract The mammalian spinal cord functions as a community of glial and neuronal cell types to accomplish sensory processing, autonomic control, and movement; conversely, the dysfunction of these cell types following spinal cord injury or disease states can lead to chronic pain, paralysis, and death. While we have made great strides in understanding spinal cellular diversity in animal models, it is crucial to characterize human biology directly to uncover specialized features of basic function and to illuminate human pathology. Here, we present a cellular taxonomy of the adult human spinal cord using single nucleus RNA-sequencing with spatial transcriptomics and antibody validation. We observed 29 glial clusters, including rare cell types such as ependymal cells, and 35 neuronal clusters, which we found are organized principally by anatomical location. To demonstrate the potential of this resource for understanding human disease, we analyzed the transcriptome of spinal motoneurons that are prone to degeneration in amyotrophic lateral sclerosis (ALS) and other diseases. We found that, compared with all other spinal neurons, human motoneurons are defined by genes related to cell size, cytoskeletal structure, and ALS, thereby supporting a model of a specialized motoneuron molecular repertoire that underlies their selective vulnerability to disease. We include a publicly available browsable web resource with this work, in the hope that it will catalyze future discoveries about human spinal cord biology.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".