Substantially Improved Electrofusion Efficiency of Hybridoma Cells: Based on the Combination of Nanosecond and Microsecond Pulses
Bibliographic record
Abstract
As the initial antibody technology, the preparation of hybridoma cells has been widely used in discovering antibody drugs and is still in use. Various antibody drugs obtained through this technology have been approved for treating human diseases. However, the key to producing hybridoma cells is efficient cell fusion. High-voltage microsecond pulsed electric fields (μsHVPEFs) are currently one of the most common methods used for cell electrofusion. Nevertheless, the membrane potential induced by the external microsecond pulse is proportional to the diameter of the cell, making it difficult to fuse cells of different sizes. Although nanosecond pulsed electric fields (nsPEFs) can achieve the fusion of cells of different sizes, due to the limitation of pore size, deoxyribonucleic acid (DNA) cannot efficiently pass through the cell pores produced by nsPEFs. This directly causes the significant loss of the target gene and reduces the proportion of positive cells after fusion. To achieve an electric field environment independent of cell size and enable efficient cell fusion, we propose a combination of nanosecond pulsed electric fields and low-voltage microsecond pulsed electric fields (ns/μsLVPEFs) to balance the advantages and disadvantages of the two techniques. The results of fluorescence experiments and hybridoma culture experiments showed that after lymphocytes and myeloma cells were stimulated by a pulse (ns/μsLVPEF, μsHVPEF, and control), compared with μsHVPEF, applying ns/μsLVPEF at the same energy could increase the cell fusion efficiency by 1.5-3.0 times. Thus far, we have combined nanosecond and microsecond pulses and provided a practical solution that can significantly increase cell fusion efficiency. This efficient cell fusion method may contribute to the further development of hybridoma technology in electrofusion.
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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 | 0.000 | 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".