Techniques to study chimerism at the tissue level in humanized mice
Bibliographic record
Abstract
Understanding the origin, distribution, and biology of different cell populations in chimeric mice is critical for interpreting the pathological changes developed in these models. To this aim, the methodological work presented here illustrates the validation and application of a collection of labeling techniques to differentiate between specific mouse and human tissue/cell components in formalin-fixed paraffin-embedded samples from chimeric mice, especially those bearing human tumor and immune cells. First, broad approaches to identify cells of human origin using ubiquitous immunohistochemical targets such as HLA-A, Ku80, and human mitochondrial 60 kDa protein (hMito) were established using specimens from humanized mice and a human tissue microarray including both normal and neoplastic samples. Due to its crisp membranous immunoreactivity, HLA-A was the most useful marker for visual human cell identification; however, Ku80 and hMito may be suitable options when HLA-A is not expressed in the cells of interest. Importantly, using one or more of these markers provides a broad range of coverage for the vast majority of human-derived cells in chimeric mice. Second, tailored immunohistochemical or in situ hybridization methodologies to distinguish specific human or mouse cell subsets are presented, focusing on immune/inflammatory cells and human chimeric antigen receptor (CAR) T-cells. These diverse approaches are accompanied by descriptions of case examples highlighting practical diagnostic and experimental applications in the context of various humanized mouse models. While not comprehensive, this work represents a valuable starting reference for pathologists and investigators working with humanized mouse models and seeking to add spatial resolution to the complex landscape of chimeric tissues.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".