Single Transcription Factor Conversion of Human Blood Fate to NPCs with CNS and PNS Developmental Capacity
Bibliographic record
Abstract
The clinical applicability of direct cell fate conversion depends on obtaining tissue from patients that is easy to harvest, store, and manipulate for reprogramming. Here, we generate induced neural progenitor cells (iNPCs) from neonatal and adult peripheral blood using single-factor OCT4 reprogramming. Unlike fibroblasts that share molecular hallmarks of neural crest, OCT4 reprogramming of blood was facilitated by SMAD+GSK-3 inhibition to overcome restrictions on neural fate conversion. Blood-derived (BD) iNPCs differentiate in vivo and respond to guided differentiation in vitro, producing glia (astrocytes and oligodendrocytes) and multiple neuronal subtypes, including dopaminergic (CNS related) and nociceptive neurons (peripheral nervous system [PNS]). Furthermore, nociceptive neurons phenocopy chemotherapy-induced neurotoxicity in a system suitable for high-throughput drug screening. Our findings provide an easily accessible approach for generating human NPCs that harbor extensive developmental potential, enabling the study of clinically relevant neural diseases directly from patient cohorts.
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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) | 0.002 | 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".