A Lentiviral Fluorescent Genetic Barcoding System for Flow Cytometry-Based Multiplex Tracking
Bibliographic record
Abstract
Retroviral integration site analysis and barcoding have been instrumental for multiplex clonal fate mapping, although their use imposes an inherent delay between sample acquisition and data analysis. Monitoring of multiple cell populations in real time would be advantageous, but multiplex assays compatible with flow cytometric tracking of competitive growth behavior are currently limited. We here describe the development and initial validation of three generations of lentiviral fluorescent genetic barcoding (FGB) systems that allow the creation of 26, 14, or 6 unique labels. Color-coded populations could be tracked in multiplex in vitro assays for up to 28 days by flow cytometry using all three vector systems. Those involving lower levels of multiplexing eased color-code generation and the reliability of vector expression and enabled functional in vitro and in vivo studies. In proof-of-principle experiments, FGB vectors facilitated in vitro multiplex screening of microRNA (miRNA)-induced growth advantages, as well as the in vivo recovery of color-coded progeny of murine and human hematopoietic stem cells. This novel series of FGB vectors provides new tools for assessing comparative growth properties in in vitro and in vivo multiplexing experiments, while simultaneously allowing for a reduction in sample numbers by up to 26-fold.
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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 | 0.001 |
| 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".