Localized, Macromolecular Transport for Thin, Adherent, Single Cells Via an Automated, Single Cell Electroporation Biomanipulator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it