Derivation and Culture of Enriched Phrenic-Like Motor Neurons From Human iPSCs
Bibliographic record
Abstract
The fatal motor neuron (MN) disease amyotrophic lateral sclerosis (ALS) is characterized by progressive degeneration of the phrenic MNs (phMNs) controlling the activity of the diaphragm, leading to death by respiratory failure. Human experimental models to study phMNs are lacking, hindering the understanding of the mechanisms of phMN degeneration in ALS. Here, we describe a protocol to derive phrenic-like MNs from human induced pluripotent stem cells (hiPSC-phMNs) within 30 days. During spinal cord development, phMNs emerge from specific MN progenitors located in the dorsalmost MN progenitor (pMN) domain at cervical levels, under the control of a ventral-to-dorsal gradient of Sonic hedgehog (SHH) signaling and a rostro-caudal gradient of retinoic acid (RA). The method presented here uses optimized concentrations of RA and the SHH agonist purmorphamine, followed by fluorescence-activated cell sorting (FACS) of the resulting MN progenitor cells (MNPCs) based on a cell-surface protein (IGDCC3) enriched in hiPSC-phMNs. The resulting cultures are highly enriched in MNs expressing typical phMN markers. This protocol enables the generation of hiPSC-phMNs and is highly reproducible using several hiPSC lines, offering a disease-relevant system to study mechanisms of respiratory MN dysfunction. While the protocol has been validated in the context of ALS research, it can be adopted to study human phrenic MNs in other research fields where these neurons are of interest. Key features • This protocol generates enriched hiPSC-derived phrenic motor neuron cultures. • The protocol can be used to develop models to study human respiratory motor neuron disease. • The protocol allows the generation of phrenic motor neuron preparations with potential for motor neuron replacement strategies. • The protocol requires experience in hiPSC culturing and FACS-based cell sorting for a successful outcome.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".