Assessment of the purity of isolated cell populations for lineage‐specific chimerism monitoring post haematopoietic stem cell transplantation
Bibliographic record
Abstract
Following haematopoietic stem cell transplantation, monitoring the proportion of donor and recipient haematopoiesis in the patient (chimerism) is an influential tool in directing further treatment choices. Short tandem repeat (STR) analysis is a method of chimerism monitoring using DNA isolated from peripheral blood, bone marrow or specific isolated cell lineages such as CD3+ T cells. For lineage-specific STR analysis on cell populations isolated from peripheral blood, a qualitative estimation of the purity of each isolated population is essential for the correct interpretation of the test data. We describe a rapid, inexpensive method for the determination of purity using a simple flow cytometry method. The method described for assessing the purity of sorted CD3+ cells can be applied to any cell population isolated using the same technology. Data obtained were comparable to results from a commercial polymerase chain reaction (PCR)-based method for the assessment of purity (Non-T Genomic Detection Kit, Accumol, Calgary, AB, Canada) (P = 0.59). Of the 303 samples tested by flow cytometry, 290 (95.7%) exceeded 90% purity, and 215 (70.95%) were over 99% pure. There were some outlying samples, showing diversity between samples and the unpredictability of purity of isolated cell populations. This flow cytometry method can be easily assimilated into routine testing protocols, allowing purity assessment in multiple-sorted cell populations for lineage-specific chimerism monitoring using a single secondary antibody and giving results comparable to a PCR-based method. As purity of isolated cell lineages is affected by time after venepuncture and storage temperature, assessment of each sample is recommended to give a reliable indication of sample quality and confidence in the interpretation of the results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".