Transdifferentiation of Human Circulating Monocytes Into Neuronal-Like Cells in 20 Days and Without Reprograming
Bibliographic record
Abstract
Despite progress, our understanding of psychiatric and neurological illnesses remains poor, at least in part due to the inability to access neurons directly from patients. Currently, there are in vitro models available but significant work remains, including the search for a less invasive, inexpensive and rapid method to obtain neuronal-like cells with the capacity to deliver reproducible results. Here we present a new protocol to transdifferentiate human circulating monocytes into neuronal-like cells in 20 days and without the need for viral insertion or reprograming. We have thoroughly characterized these monocyte-derived neuronal-like cells (MDNCs) through various approaches including; single cell mRNA sequencing, flow cytometry, electrophysiology, western blots, immunofluorescence and pharmacological techniques. These MDNCs resemble human neurons early in development, express a variety of neuronal genes as well as several neuronal proteins and also present electrical activity. In addition, when these neuronal-like cells are exposed to either dopamine or colchicine, they respond similarly to neurons by retracting their neuronal arborizations. More importantly, MDNCs exhibit reproducible differentiation rates, arborizations and expression of dopamine 1 receptors on separate sequential samples from the same individual. To provide context and help researchers decide which in vitro model of neuronal development is best suited to address their scientific question, we compare our results with those of other in vitro models currently available and expose advantages and disadvantages of each paradigm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".