Lentiviral Fluorescent Genetic Barcoding for Multiplex Fate Tracking of Leukemic Cells
Bibliographic record
Abstract
Tracking the behavior of leukemic samples both in vitro and in vivo plays an increasingly large role in efforts to better understand the leukemogenic processes and the effects of potential new therapies. Such work can be accelerated and made more efficient by methodologies enabling the characterization of leukemia samples in multiplex assays. We recently developed three sets of lentiviral fluorescent genetic barcoding (FGB) vectors that create 26, 14, and 6 unique immunophenotyping-compatible color codes from GFP-, yellow fluorescent protein (YFP)-, and monomeric kusabira orange 2 (mKO2)-derived fluorescent proteins. These vectors allow for labeling and tracking of individual color-coded cell populations in mixed samples by real-time flow cytometry. Using the prototypical Hoxa9/Meis1 murine model of acute myeloid leukemia, we describe the application of the 6xFGB vector system for assessing leukemic cell characteristics in multiplex assays. By transplanting color-coded cell mixes, we investigated the competitive growth behavior of individual color-coded populations, determined leukemia-initiating cell frequencies, and assessed the dose-dependent potential of cells exposed to the histone deacetylase inhibitor Entinostat for bone marrow homing. Thus, FGB provides a useful tool for the multiplex characterization of leukemia samples in a wide variety of applications with a concomitant reduction in workload, processing times, and mouse utilization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".