Bone Marrow Transplantation Results in Human Donor Blood Cells Acquiring and Displaying Mouse Recipient Class I MHC and CD45 Antigens on Their Surface
Bibliographic record
Abstract
BACKGROUND: Mouse models of human disease are invaluable for determining the differentiation ability and functional capacity of stem cells. The best example is bone marrow transplants for studies of hematopoietic stem cells. For organ studies, the interpretation of the data can be difficult as transdifferentiation, cell fusion or surface antigen transfer (trogocytosis) can be misinterpreted as differentiation. These events have not been investigated in hematopoietic stem cell transplant models. METHODOLOGY/PRINCIPAL FINDINGS: In this study we investigated fusion and trogocytosis involving blood cells during bone marrow transplantation using a xenograft model. We report that using a standard SCID repopulating assay almost 100% of the human donor cells appear as hybrid blood cells containing both mouse and human surface antigens. CONCLUSION/SIGNIFICANCE: Hybrid cells are not the result of cell-cell fusion events but appear to be due to efficient surface antigen transfer, a process referred to as trogocytosis. Antigen transfer appears to be non-random and includes all donor cells regardless of sub-type. We also demonstrate that irradiation preconditioning enhances the frequency of hybrid cells and that trogocytosis is evident in non-blood cells in chimera mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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".