An automated system for delivery of an unstable transcription factor to hematopoietic stem cell cultures
Bibliographic record
Abstract
An automated delivery system for cell culture applications would permit studying more complex culture strategies and simplify measures taken to expose cells to unstable molecules. We are interested in understanding how intracellular TAT-HOXB4 protein concentration affects hematopoietic stem cell (HSC) fate; however, current manual dosing strategies of this unstable protein are labor intensive and produce wide concentration ranges which may not promote optimal growth. In this study we describe a programmable automated delivery system that was designed to integrate into a clinically relevant, single-use, closed-system bioprocess and facilitate transcription factor delivery studies. The development of a reporter cell assay allowed for kinetic studies to determine the intracellular (1.4 +/- 0.2 h) and extracellular (3.7 +/- 1.8 h and 78 +/- 27 h at 37 degrees C and 4 degrees C, respectively) half-lives of TAT-HOXB4 activity. These kinetic parameters were incorporated into a mathematical model, which was used to predict the dynamic intracellular concentration of TAT-HOXB4 and optimize the delivery of the protein. The automated system was validated for primary cell culture using human peripheral blood patient samples. Significant expansion of human primitive progenitor cells was obtained upon addition of TAT-HOXB4 without user intervention. The delivery system is thus capable of being used as a clinically relevant tool for the exploration and optimization of temporally sensitive stem cell culture systems.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".