Optimized CRISPR/Cas9 Electroporation and Single Cell Cloning Protocol for Generating Pure Cellular Models in Human Immortalized Myoblasts
Bibliographic record
Abstract
Abstract Background Genome editing in human skeletal muscle research requires protocols that maximize delivery while preserving viability and clonal outgrowth. We sought to develop a reagent-free workflow for CRISPR/Cas9 editing in human immortalized myoblasts and to demonstrate its performance in two use cases, an IARS1 knockout and an MLIP homozygous knock-in. Methods We optimized electroporation parameters using a green fluorescent protein reporter to compare three electrical settings for transfection and survival in E6/E7 myoblasts, then applied ribonucleoprotein delivery for editing. We evaluated the effect of confluency at electroporation, performed single-cell cloning without antibiotics or fluorescence-activated sorting, and validated edits by high-resolution melting pre-screen followed by Sanger sequencing. Results Electroporation optimization identified one parameter set that maximized delivery while preserving viability. Performing electroporation at low confluency increased clonal outgrowth and editing rates. The workflow yielded an 84% success rate for IARS1 knockout and a 3.3% success rate for MLIP homozygous knock-in. High-resolution melting provided a very sensitive pre-screen, detecting 96% to 100% of actual edits, reducing the number of Sanger sequencing needed. Performance was reproducible across runs and myoblast lines and increasing single-cell seeding scaled yields without compromising purity. Conclusions This work provides a practical and reproducible selection-free protocol that couples electroporation optimization, low confluency editing, single-cell cloning, and high-resolution melting sorting to generate pure edited myoblast lines. The approach is applicable to disease modeling in neuromuscular research and clarifies feasibility boundaries for essential genes and homology-directed repair in these cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".