Optogenetic single-cell control of transcription achieves mRNA tunability and reduced variability
Bibliographic record
Abstract
Abstract The study of gene expression at the single-cell level has exposed the importance of stochasticity for the behavior of cellular systems. Research on cellular variability has mostly relied on observing expression either in response to natural stimuli or to constant gene regulators. However, the ability to probe cells individually can lead to a deeper understanding of the underlying process. Here, we propose an experimental platform for optogenetic feedback control of individual cells. It consists of a digital micromirror device that, coupled to a microscope, can target light-responsive cells with individualized illumination profiles, thereby exploiting the good spatial resolution of optogenetic induction. Together with an automated software pipeline for segmentation, quantification and tracking of single cells, the platform enables independent and real-time control of numerous cells. We demonstrate our platform by regulating transcription in over a hundred yeast cells simultaneously, while achieving tunability of mRNA abundance. Using a novel technique to measure extrinsic variation, we further show that single cell feedback regulation of this highly stochastic process achieves a 10-fold reduction of extrinsic variation in nascent mRNA over population control, with superior control loop properties. Our platform establishes a new, flexible method for studying transcriptional dynamics in single cells.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".