Induced pluripotent stem cells reprogrammed from primary dendritic cells provide an abundant source of immunostimulatory dendritic cells for use in immunotherapy
Bibliographic record
Abstract
Cell types differentiated from induced pluripotent stem cells (iPSCs) are frequently arrested in their development program, more closely resembling a fetal rather than an adult phenotype, potentially limiting their utility for downstream clinical applications. The fetal phenotype of iPSC-derived dendritic cells (ipDCs) is evidenced by their low expression of MHC class II and costimulatory molecules, impaired secretion of IL-12, and poor responsiveness to conventional maturation stimuli, undermining their use for applications such as immune-oncology. Given that iPSCs display an epigenetic memory of the cell type from which they were originally derived, we investigated the feasibility of reprogramming adult DCs to pluripotency to determine the impact on the phenotype and function of ipDCs differentiated from them. Using murine bone marrow-derived DCs (bmDCs) as proof of principle, we show here that immature DCs are tractable candidates for reprogramming using non-integrating Sendai virus for the delivery of Oct4, Sox2, Klf4, and c-Myc transcription factors. Reprogramming efficiency of DCs was lower than mouse embryonic fibroblasts (MEFs) and highly dependent on their maturation status. Although control iPSCs derived from conventional MEFs yielded DCs that displayed a predictable fetal phenotype and impaired immunostimulatory capacity in vitro and in vivo, DCs differentiated from DC-derived iPSCs exhibited a surface phenotype, immunostimulatory capacity, and responsiveness to maturation stimuli indistinguishable from the source DCs, a phenotype that was retained for 15 passages of the parent iPSCs. Our results suggest that the epigenetic memory of iPSCs may be productively exploited for the generation of potently immunogenic DCs for immunotherapeutic applications.
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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 | 0.001 |
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".