Efficient DNA- and virus-free engineering of cellular transcriptomic states using dCas9 ribonucleoprotein (dRNP) complexes
Bibliographic record
Abstract
For genome editing, the use of CRISPR ribonucleoprotein (RNP) complexes is well established and often the superior choice over plasmid-based or viral strategies. RNPs containing dCas9 fusion proteins, which enable the targeted manipulation of transcriptomes and epigenomes, remain significantly less accessible. Here, we describe the production, delivery, and optimization of second generation CRISPRa RNPs (dRNPs). We characterize the transcriptional and cellular consequences of dRNP treatments in a variety of human target cells and show that the uptake is very efficient. The targeted activation of genes demonstrates remarkable potency, even for genes that are strongly silenced, such as developmental master transcription factors. In contrast to DNA-based CRISPRa strategies, gene activation is immediate and characterized by a sharp temporal precision. We also show that dRNPs allow very high-target multiplexing, enabling undiminished gene activation of multiple genes simultaneously. Applying these insights, we find that intensive target multiplexing at single promoters synergistically elevates gene transcription. Finally, we demonstrate in human stem and differentiated cells that the preferable features of dRNPs allow to instruct and convert cell fates efficiently without the need for DNA delivery or viral vectors.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".