Recipient-specific T-cell repertoire reconstitution in the gut following murine hematopoietic cell transplant
Bibliographic record
Abstract
Graft-versus-host disease (GVHD) is a complication of hematopoietic cell transplantation (HCT) caused by alloreactive T cells. Murine models of HCT are used to understand GVHD and T-cell reconstitution in GVHD target organs, most notably the gastrointestinal (GI) tract where the disease contributes most to patient mortality. T-cell receptor (TCR) repertoire sequencing was used to measure T-cell reconstitution from the same donor graft (C57BL/6 H-2b) in the GI tract of different recipients across a spectrum of matching, from syngeneic (C57BL/6), to minor histocompatibility (MHC) antigen mismatch BALB.B (H-2b), to major MHC mismatched B10.BR (H-2k) and BALB/c (H-2d). Although the donor T-cell pools had highly similar TCR, the TCR repertoire after HCT was very specific to recipients in each experiment independent of geography. A single invariant natural killer T clone was identifiable in every recipient group and was enriched in syngeneic recipients according to clonal count and confirmatory flow cytometry. Using a novel cluster analysis of the TCR repertoire, we could classify recipient groups based only on their CDR3 size distribution or TCR repertoire relatedness. Using a method for evaluating the contribution of common TCR motifs to relatedness, we found that reproducible sets of clones were associated with specific recipient groups within each experiment and that relatedness did not necessarily depend on the most common clones in allogeneic recipients. This finding suggests that TCR reconstitution is highly stochastic and likely does not depend on the evaluation of the most expanded TCR clones in any individual recipient but instead depends on a complex polyclonal architecture.
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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) | 0.001 | 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 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".