Global Transcriptional Response to CRISPR/Cas9-AAV6-Based Genome Editing in CD34+ Hematopoietic Stem and Progenitor Cells
Bibliographic record
Abstract
Genome-editing technologies are currently being translated to the clinic. However, cellular effects of the editing machinery have yet to be fully elucidated. Here, we performed global microarray-based gene expression measurements on human CD34 + hematopoietic stem and progenitor cells that underwent editing. We probed effects of the entire editing process as well as each component individually, including electroporation, Cas9 (mRNA or protein) with chemically modified sgRNA, and AAV6 transduction. We identified differentially expressed genes relative to control treatments, which displayed enrichment for particular biological processes. All editing machinery components elicited immune, stress, and apoptotic responses. Cas9 mRNA invoked the greatest amount of transcriptional change, eliciting a distinct viral response and global transcriptional downregulation, particularly of metabolic and cell cycle processes. Electroporation also induced significant transcriptional change, with notable downregulation of metabolic processes. Surprisingly, AAV6 evoked no detectable viral response. We also found Cas9/sgRNA ribonucleoprotein treatment to be well tolerated, in spite of eliciting a DNA damage signature. Overall, this data establishes a benchmark for cellular tolerance of CRISPR/Cas9-AAV6-based genome editing, ensuring that the clinical protocol is as safe and efficient as possible.
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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) | 0.003 | 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".