CRISPR-Cas9-driven antigen conversion of clinically relevant blood group systems
Bibliographic record
Abstract
The common practice of blood transfusion entirely relies on blood donations from the population. Ensuring blood group compatibility between a donor and a recipient is paramount to prevent critical adverse reactions. Finding compatible blood can be challenging given the high diversity of blood group antigens, especially for chronically transfused patients at higher risk of alloimmunization owing to repeated exposures to foreign RBCs. In addition, due to the immunogenicity of the ABO blood group and the highly polymorphic nature of the Rhesus (Rh) system, they both remain of prime importance in transfusion medicine. Cultured red blood cells (cRBCs) may eventually provide an alternative for blood donations-at least in some circumstances. Combining cRBCs with blood group gene editing could broaden transfusion accessibility by making antigen expression compatible with rare phenotypes, thus meeting the needs of more patients. Starting from mobilized, erythroid-primed hematopoietic stem and progenitor cells (HSPCs), we used virus- and selection-free, CRISPR-Cas9-mediated knockouts to produce erythroid cells devoid of AB and Rh antigen. The approach yielded almost complete conversion to O- and RhNull phenotypes, as determined by standard hemagglutination and flow cytometry analyses. Combined with robust cRBC protocols, these clinically relevant phenotypic changes could eventually expand the accessibility of blood transfusion for specific and unmet clinical needs.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".