Generation of mouse-zebrafish hematopoietic tissue chimeric embryos for hematopoiesis and host-pathogen interaction studies
Bibliographic record
Abstract
ABSTRACT Xenografts of the hematopoietic system are extremely useful as disease models and for translational research. Zebrafish xenografts have been widely used to monitor blood cancer cell dissemination and homing due to the optical clarity of embryos and larvae, which allow unrestricted in vivo visualization of migratory events. Here, we have developed a xenotransplantation technique that transiently generates hundreds of hematopoietic tissue chimeric embryos by transplanting murine bone marrow cells into zebrafish blastulae. In contrast to previous methods, this procedure allows mammalian cell integration into the fish developmental hematopoietic program, which results in chimeric animals containing distinct phenotypes of murine blood cells in both circulation and the hematopoietic niche. Murine cells in chimeric animals express antigens related to (i) hematopoietic stem and progenitor cells, (ii) active cell proliferation and (iii) myeloid cell lineages. We verified the utility of this method by monitoring zebrafish chimeras during development using in vivo non-invasive imaging to show novel murine cell behaviors, such as homing to primitive and definitive hematopoietic tissues, dynamic hematopoietic cell and hematopoietic niche interactions, and response to bacterial infection. Overall, transplantation into the zebrafish blastula provides a useful method that simplifies the generation of numerous chimeric animals and expands the range of murine cell behaviors that can be studied in zebrafish chimeras. In addition, integration of murine cells into the host hematopoietic system during development suggests highly conserved molecular mechanisms of hematopoiesis between zebrafish and mammals. This article has an associated First Person interview with the first author of the paper.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".