Characterization of the NT2‐derived neuronal and astrocytic cell lines as alternative in vitro models for primary human neurons and astrocytes
Bibliographic record
Abstract
Primary human fetal neurons and astrocytes (HFNs and HFAs, respectively) provide relevant cell types with which to study in vitro the mechanisms involved in various human neurological diseases, such as multiple sclerosis, Parkinson's disease, and Alzheimer's disease. However, the limited availability of human fetal cells poses a significant problem for the study of these diseases when a human cell model system is required. Thus, generating a readily available alternative cell source with the essential features of human neurons and astrocytes is necessary. The human teratoma-derived NTera2/D1 (NT2) cell line is a promising tool from which both neuronal and glial cells can be generated. Nevertheless, a direct comparison of NT2 neurons and primary HFNs in terms of their morphology physiological and chemical properties is still missing. This study directly compares NT2-derived neurons and primary HFNs using immunocytochemistry, confocal calcium imaging, high-performance liquid chromatography, and high-content analysis techniques. We investigated the morphological similarities and differences, levels of relevant amino acids, and internal calcium fluctuations in response to certain neurotransmitters/stimuli. We also compared NT2-derived astrocytes and HFAs. In most of the parameters tested, both neuronal and astrocytic cell types exhibited similarities to primary human fetal neurons and astrocytes. NT2-derived neurons and astrocytes are reliable in vitro tools and a renewable cell source that can serve as a valid alternative to HFNs/HFAs for mechanistic studies of neurological diseases.
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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 | 0.000 | 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".