Optimization of long-term human iPSC-derived spinal motor neuron culture using a dendritic polyglycerol amine-based substrate
Bibliographic record
Abstract
Abstract Human induced pluripotent stem cells (h-iPSCs) derived from healthy and diseased individuals can give rise to many cell types, facilitating the study of mechanisms of development, human disease modeling, and early drug target validation. In this context, experimental model systems based on h-iPSC-derived motor neurons (MNs) have been used to study MN diseases such as spinal muscular atrophy and amyotrophic lateral sclerosis. Modeling MN disease using h-iPSC-based approaches requires culture conditions that can recapitulate in a dish the events underlying differentiation, maturation, aging, and death of MNs. Current h-iPSC-derived MN-based applications are often hampered by limitations in our ability to monitor MN morphology, survival, and other functional properties over a prolonged timeframe, underscoring the need for improved long-term culture conditions. Here we describe a cytocompatible dendritic polyglycerol amine (dPGA) substrate-based method for prolonged culture of h-iPSC-derived MNs. We provide evidence that MNs cultured on dPGA-coated dishes are more amenable to long-term study of cell viability, molecular identity, and spontaneous network electrophysiological activity. The present study has the potential to improve iPSC-based studies of human MN biology and disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".