Use of Solentim verified in-situ plate seeding (VIPS™) enhances single-cell cloning efficiency
Bibliographic record
Abstract
Abstract The primary goal in cell line development is to establish high-producer recombinant cell line(s) of single-cell origin. Traditionally, these cell lines are developed using limiting dilution cloning (LDC) method of single cell isolation, a rate-limiting, lengthy and labor-intensive process. The Verified-In-Situ-Plate-Seeding (VIPS™) is an automated single-cell seeding and imaging equipment designed to accelerate cell line development workflow. In this study, VIPS™ was tested for efficiency and accuracy of single cell seeding in parallel with limiting dilution cloning (LDC). Three Chinese hamster ovary (CHO) derived cell lines with known clonal properties were tested under six different growth conditions (three growth media and two different kinds of microplates). Data showed VIPS™ and limiting dilution (LDC) have comparable cloning efficiency when CHO-M cells were tested. By contrast, the Verified In-Situ Plate Seeding (VIPS™) produced 6-8-fold more clones of single-cell origin than LDC when CHO-K1 or CHO-S cells were tested. Moreover, the verified In-Situ Plate Seeding (VIPS™) correctly identified single-cell and multiple-cells seeded wells with 65-72% and 52-81% accuracy, respectively. Taken together, the high throughput imaging and single-cell seeding capabilities of VIPS™ outperformed the rate-limiting LDC method and, therefore, has the potential to accelerate cell line development workflow.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".