Comprehensive untargeted lipidomic profiling of third generation lentiviral vectors and packaging cells
Bibliographic record
Abstract
Abstract Lentiviral vectors (LV) are emerging tools for genetic therapies and novel cancer treatments. While effective, LV-based therapies have extremely large costs associated with their manufacturing and delivery. LV technology descends from human immunodeficiency virus (HIV), whose lipid envelope has been previously measured and shown to have a direct impact on its transduction efficiency. We developed a rapid, robust, and sensitive untargeted lipidomics pipeline to analyze novel LV biotherapeutic products and demonstrate its utility on HEK 293T packaging cells and concentrated culture media containing LV. The impact of 48 hours of LV production on the lipidome of HEK 293T cells was measured and compared to the expression of vesicular stomatitis virus G protein (VSV G) over the same timeframe. 151 lipids were identified in HEK 293T packaging cells, 84 of which had fold changes with FDR-corrected P < 0.05 compared to HEK 293T treated with media. It was found that fold changes with FDR-adjusted P < 0.05 after VSV G expression and LV production were highly correlated (R2 = 0.89). Concentrating LV in culture media led to the identification of 102 lipids, half of which were determined to be unique LV virion lipids after subtracting the media lipidome. Our approach can be readily used to study the lipid dynamics of large-scale LV production and be rapidly translated into targeted methods to quantify individual lipid components or applied to other viral vector platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".