An Optimized Workflow to Generate and Characterize iPSC-Derived Motor Neuron (MN) Spheroids
Bibliographic record
Abstract
A multitude of in vitro models based on induced pluripotent stem cell (iPSC)-derived motor neurons (MNs) have been developed to investigate the underlying causes of selective MN degeneration in motor neuron diseases (MNDs). 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. To start, the morphological profiling of the spheroids was achieved by developing a pipeline to obtain measurements of their size and shape. Next, we confirmed the expression of different MN markers at the transcript and protein levels by qPCR and immunocytochemistry of tissue-cleared samples, respectively. Finally, we assessed the capacity of the MN spheroids to display functional activity in the form of action potentials and bursts using a microelectrode array approach. Although most of the cells displayed an MN identity, we also characterized the presence of other cell types, namely interneurons and oligodendrocytes, which share the same neural progenitor pool with MNs. In summary, we successfully developed an MN 3D model, and we optimized a workflow that can be applied to perform its morphological, gene expression, protein, and functional profiling over time.
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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 | 0.005 |
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".