Characterization of Human iPSC-derived Spinal Motor Neurons by Single-cell RNA Sequencing
Bibliographic record
Abstract
Human induced pluripotent stem cells (iPSCs) offer the opportunity to generate specific cell types from healthy and diseased individuals, allowing the study of mechanisms of early human development, modelling a variety of human diseases, and facilitating the development of new therapeutics. Human iPSC-based applications are often limited by the variability among iPSC lines originating from a single donor, as well as the heterogeneity among specific cell types that can be derived from iPSCs. The ability to deeply phenotype different iPSC-derived cell types is therefore of primary importance to the successful and informative application of this technology. Here we describe a combination of motor neuron (MN) derivation and single-cell RNA sequencing approaches to generate and characterize specific MN subtypes obtained from human iPSCs. Our studies provide evidence for rapid and robust generation of MN progenitor cells that can give rise to a heterogenous population of MNs. Approximately 58% of human iPSC-derived MNs display molecular characteristics of lateral motor column MNs, with a number of molecularly distinct subpopulations present within this MN group. Roughly 19% of induced MNs resemble hypaxial motor column MNs, while ∼6% of induced MNs have features of median motor column MNs. The present study has the potential to improve our understanding of iPSC-derived MN subtype function and dysfunction, possibly leading to improved iPSC-based applications for the study of human MN biology and disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".