An optimized workflow to generate and characterize iPSC-derived motor neuron (MN) spheroids
Bibliographic record
Abstract
Abstract Motor neuron diseases (MNDs) are characterized by the progressive degeneration of motor neurons (MNs) from the cortex, brainstem and/or the spinal cord. In an effort to understand the underlying causes of this selective degeneration, a multitude of in vitro models based on induced pluripotent stem cell (iPSC)-derived MNs have been developed. Moreover, different groups have started to use advanced 3D structures, composed of MNs and other cell types to increase the physiological relevance of such in vitro models. For instance, spheroids are simple 3D models that have the potential to be generated in large numbers that can be used across different assays. In this study, we generated MN spheroids and developed a workflow to analyze them. We confirmed the expression of different MN markers as the MN spheroids differentiate, at both the transcript and protein level, as well as their capacity to display functional activity in the form of action potentials (APs) and bursts. We also identified the presence of other cell types, namely interneurons and oligodendrocytes, which share the same neural progenitor pool with MNs. In summary, we successfully developed a MN 3D model, and we optimized a workflow that can be applied to their characterization and analysis. In the future, we will apply this model and workflow to the study of MNDs by generating MN spheroids from patient-derived iPSC lines, aiming to contribute to the development of more advance and physiological in vitro disease models.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".