Quantitative characterization of variability in cortical neurons between healthy human induced pluripotent stem cell lines
Bibliographic record
Abstract
When studying developmental neurological diseases such as autism spectrum disorder, human brain tissue samples are only available postmortem. The breakthrough advances allowing reprogramming of human blood or skin cells into pluripotent stem cells (iPSCs), which can be differentiated into numerous cell types including neurons and astrocytes, allow for human cell models of neurodevelopmental disorders. How different control iPSCs behave under different culturing conditions has not been compared in parallel. Here, we characterize 11 healthy control human iPSC lines differentiated into cortical neurons, 2 commercial and 9 in house lines using a combination qPCR and immunofluorescence (IF). We first tested if cell lines could be distinguished based on IF in microscopy images using a Random Forest classifier. We find that cell lines can be clearly distinguished. We next used unsupervised machine learning to cluster neurons into groups. We measure a panel of genes using qPCR across 11 cell lines from NPC and 4 weeks cortical neurons derived from iPSC grown in two types of media. We further characterize the cell lines by quantifying the proportion of cells expressing neuronal precursor and cortical neuron markers using an automated image analysis macro.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".