Comparing <scp>RNA</scp>‐sequencing datasets from astrocytes, oligodendrocytes, and microglia in multiple sclerosis identifies novel dysregulated genes relevant to inflammation and myelination
Bibliographic record
Abstract
Central nervous system (CNS) inflammation is a key factor in multiple sclerosis (MS). Invasion of peripheral immune cells into the CNS resulting from an unknown signal or combination of signals results in activation of resident immune cells and the hallmark feature of the disease: demyelinating lesions. These lesion sites are an amalgam of reactive peripheral and central immune cells, astrocytes, damaged and dying oligodendrocytes, and injured neurons and axons. Sustained inflammation affects cells directly located within the lesion site and further abnormalities are apparent diffusely throughout normal-appearing white matter and grey matter. It is only relatively recently, using animal models, new tissue sampling techniques, and next-generation sequencing, that molecular changes occurring in CNS resident cells have been broadly captured. Advances in cell isolation through Fluorescence Activated Cell Sorting (FACS) and laser-capture microdissection together with the emergence of single-cell sequencing have enabled researchers to investigate changes in gene expression in astrocytes, microglia, and oligodendrocytes derived from animal models of MS as well as from primary patient tissue. The contribution of some dysregulated pathways has been followed up in individual studies; however, corroborating results often go unreported between sequencing studies. To this end, we have consolidated results from numerous RNA-sequencing studies to identify and review novel patterns of differentially regulated genes and pathways occurring within CNS glial cells in MS. This article is categorized under: Neurological Diseases > Molecular and Cellular Physiology.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".