Localized, Macromolecular Transport for Thin, Adherent, Single Cells Via an Automated, Single Cell Electroporation Biomanipulator
Bibliographic record
Abstract
Single cell electroporation (SCE), via microcapillary, is an effective method for molecular, transmembrane transport used to gain insight on cell processes with minimal preparation. Although possessing great potential, SCE is difficult to execute and the technology spans broad fields within cell biology and engineering. The technical complexities, the focus and expertise demanded during manual operation, and the lack of an automated SCE platform limit the widespread use of this technique, thus the potential of SCE has not been realized. In this study, an automated biomanipulator for SCE is presented. Our system is capable of delivering molecules into the cytoplasm of extremely thin cellular features of adherent cells. The intent of the system is to abstract the technical challenges and exploit the accuracy and repeatability of automated instrumentation, leaving only the focus of the experimental design to the operator. Each sequence of SCE including cell and SCE site localization, tip-membrane contact detection, and SCE has been automated. Positions of low-contrast cells are localized and "SCE sites" for microcapillary tip placement are determined using machine vision. In addition, new milestones within automated cell manipulation have been achieved. The system described herein has the capability of automated SCE of "thin" cell features less than 10 μm in thickness. Finally, SCE events are anticipated using visual feedback, while monitoring fluorescing dye entering the cytoplasm of a cell. The execution is demonstrated by inserting a combination of a fluorescing dye and a reporter gene into NIH/3T3 fibroblast cells.
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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 | 0.001 | 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".