Single-Cell RNA-Seq Analysis of Cells from Degenerating and Non-Degenerating Intervertebral Discs from the Same Individual Reveals New Biomarkers for Intervertebral Disc Degeneration
Bibliographic record
Abstract
In this study, we used single-cell transcriptomic analysis to identify new specific biomarkers for nucleus pulposus (NP) and inner annulus fibrosis (iAF) cells, and to define cell populations within non-degenerating (nD) and degenerating (D) human intervertebral discs (IVD) of the same individual. Cluster analysis based on differential gene expression delineated 14 cell clusters. Gene expression profiles at single-cell resolution revealed the potential functional differences linked to degeneration, and among NP and iAF subpopulations. GO and KEGG analyses discovered molecular functions, biological processes, and transcription factors linked to cell type and degeneration state. We propose two lists of biomarkers, one as specific cell type, including C2orf40, MGP, MSMP, CD44, EIF1, LGALS1, RGCC, EPYC, HILPDA, ACAN, MT1F, CHI3L1, ID1, ID3 and TMED2. The second list proposes predictive IVD degeneration genes, including MT1G, SPP1, HMGA1, FN1, FBXO2, SPARC, VIM, CTGF, MGST1, TAF1D, CAPS, SPTSSB, S100A1, CHI3L2, PLA2G2A, TNRSF11B, FGFBP2, MGP, SLPI, DCN, MT-ND2, MTCYB, ADIRF, FRZB, CLEC3A, UPP1, S100A2, PRG4, COL2A1, SOD2 and MT2A. Protein and mRNA expression of MGST1, vimentin, SOD2 and SYF2 (p29) genes validated our scRNA-seq findings. Our data provide new insights into disc cells phenotypes and biomarkers of IVD degeneration that could improve diagnostic and therapeutic options.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".