Single-cell analysis using capillary electrophoresis: Influence of surface support properties on cell injection into the capillary
Bibliographic record
Abstract
Capillary electrophoresis (CE) is an important tool of chemical cytometry. Whole-cell analysis using CE starts with cell injection into the capillary by either siphoning or electroosmosis. However, strong adherence of the cell to the support surface can prevent efficient cell injection and lead to irreproducible analysis. Here we evaluated several surfaces as potential cell supports for HT29 cells (human colon adenocarcinoma). These cells strongly adhered to the surface of untreated glass or polystyrene. Hydrophobic coating with dimethyldichlorosilane (DMS) or Sigmacote did not significantly reduce cell adhesion. In contrast, cell adhesion was reduced significantly when the surface was modified with hydrophilic polymers (hydrogels) such as poly(2-hydrohyethyl methacrylate) (PHEMA) and polyvinyl alcohol (PVA). In addition to their pronounced antiadhesive properties, PHEMA and PVA coatings were the most biocompatible (had highest survival of cells in contact with surface). Hydrogel-coated polystyrene plates were tested as a commercial alternative to hydrogel-coated glass slides. The cell adhesive properties of such plates were similar to those of PHEMA and PVA. However, the biocompatibility of the plates was lower than that of the other surfaces tested. Moreover, in contrast to PHEMA- and PVA-coated glass slides, the plates were sensitive to UV light and therefore should not be used when fluorescent image microscopy with UV excitation precedes CE. The analyses of the data obtained showed that PHEMA- and PVA-coated glass slides were the most suitable cell supports for cell injection into the capillary.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".