Capturing and Detecting of Extracellular Vesicles Derived from Single <i>Escherichia coli</i> Mother Cells
Bibliographic record
Abstract
Abstract Cells have a phenotypic heterogeneity even in isogeneic populations. Differences in secretion of substances have been well-investigated with single mammalian cells. However, studies on the heterogeneity of secreted substances at the single-bacterial-cell level are challenging due to the small size, motility, and rapid proliferation of bacterial cells such as Escherichia coli . Here, we propose a microfluidic device to achieve an isolated culture of single bacterial cells and capture of extracellular vesicles (EVs) secreted from individuals. The device has winding channels to trap single rod-shaped E. coli cells at their entrances. Isolated single mother cells grew constantly up to 24 h, while their daughter cells were removed by flow. The flow carried EVs of the trapped cells along the channel, whose surface was rendered positively charged to electrostatically capture negatively charged EVs, followed by staining with a lipophilic dye to detect EVs by microscopy. Our results underline that the amounts of segregated EVs vary among cells. Moreover, individual responses to perturbation using a membrane-perturbing antibiotic were observed in growth dynamics and EV secretion of living-alone bacteria. The proposed method can be applied to detect other secreted substances of interest, possibly paving the way for elucidating unknown heterogeneities in bacteria.
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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) | 0.000 | 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 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".