Transcriptomic and Functional Landscape of Adult Human Spinal Cord NSPCs Compared to iPSC-Derived Neural Progenitor Cells
Bibliographic record
Abstract
The adult human spinal cord harbors diverse populations of neural stem/progenitor cells (NSPCs) essential for neuroregeneration and central nervous system repair. While induced pluripotent stem cell (iPSC)-derived NSPCs offer significant therapeutic potential, understanding their molecular and functional alignment with bona fide spinal cord NSPCs is crucial for developing autologous cell therapies that enhance spinal cord regeneration and minimize immune rejection. In this study, we present the first direct transcriptomic and functional comparison of syngeneic adult human NSPC populations, including bona fide spinal cord NSPCs and iPSC-derived NSPCs regionalized to the spinal cord (iPSC-SC) and forebrain (iPSC-Br). RNA sequencing analysis revealed distinct transcriptomic profiles and functional disparities among NSPC types. iPSC-Br NSPCs exhibited a close resemblance to bona fide spinal cord NSPCs, characterized by enriched expression of neurogenesis, axon guidance, synaptic signaling, and voltage-gated calcium channel activity pathways. Conversely, iPSC-SC NSPCs displayed significant heterogeneity, suboptimal regional specification, and elevated expression of neural crest and immune response-associated genes. Functional assays corroborated the transcriptomic findings, demonstrating superior neurogenic potential in iPSC-Br NSPCs. Additionally, we assessed donor-specific influences on NSPC behavior by analyzing gene expression and differentiation outcomes across syngeneic populations from multiple individuals. Donor-specific factors significantly modulated transcriptomic profiles, with notable variability in the alignment of iPSC-derived NSPCs to bona fide spinal cord NSPCs. Enrichment of pathways related to neurogenesis, axon guidance, and synaptic signaling varied across donors, highlighting the impact of genetic and epigenetic individuality on NSPC behavior.
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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 | 0.000 | 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".