Molecular controls of T-lineage differentiation and thymus engraftment from human induced pluripotent stem cells 2056
Bibliographic record
Abstract
Abstract Description The primary cell sources of T cell-based immunotherapies are autologous mature T cells from patients. Mature T cells are expanded in vitro and infused back for treatment after being armed by genes like chimeric antigen receptors. T cell resources limit this strategy. Human induced pluripotent stem cells (iPSC), which can be differentiated into mature T lineage cells in vitro, provide a potential alternative cellular source. We envision a strategy of transplanting iPSC-derived progenitor T cells (Pro-T) that are able to engraft the host thymus and develop into self-tolerant and self-MHC-restricted mature T cells via intrathymic selection steps. However, the successful engraftment of either iPSC-derived hematopoietic stem cells or Pro-T remains a major unresolved challenge. Our research aims to determine the necessary molecular cues required for iPSC-derived Pro-T thymus engraftment to enable the strategy. Our single-cell RNA-seq data shows that iPSC-derived Pro-T are more similar to fetal Pro-T than adult thymus, with a high signature Lin28b RNA expression. We used a fetal thymic organ culture (FTOC) approach to test thymus engraftment and RNA-seq to show that knocking-out LIN28B expression allows iPSC-derived Pro-T to engraft FTOCs, similar to cord blood-derived Pro-T. By further exploring LIN28B’s function during human T cell development, targeted LIN28B pathway inhibition could be used to enhance the iPSC-derived Pro-T engraftment ability for future clinical applications. Funding Sources Funding was provided by Grant 1158417 from the Cancer Research Society and the Leukemia & Lymphoma Society of Canada. Topic Categories Hematopoiesis and Immune System Development (HEM)
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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) | 0.002 | 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".