Differentiation and Characterization of Dendritic Cells from Human Embryonic Stem Cells
Bibliographic record
Abstract
Human embryonic stem cells (hESCs) offer great hope in regenerative medicine. Their ability to give rise to almost any type of cell present in the adult body makes them an invaluable tool in finding cures for a variety of diseases. While considerable protocols have been devised to efficiently differentiate hESCs into various cells types including cells of hematopoietic origin, this protocol will focus on the derivation of dendritic cells (DC), a potent antigen-presenting cell. DCs are a highly important arm of the immune system, as they represent one of the few cells that bridge the innate and adaptive systems, leading to effective pathogen clearance. The study of DCs has led to potential applications in diverse fields, such as vaccine development, tumor immunology, and transplantation. In this protocol, we describe two different methods of differentiating hESCs into DCs. The first method uses OP9 bone marrow stromal supporting cells as a coculture system, while the second method utilizes the formation of embryoid body (EB, cellular aggregate) as an approach. To assure the successful outcome and subsequent assessment of the differentiated DCs, supporting protocols have been included in this chapter.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".