Connected component masking accurately identifies the ratio of phagocytosed and cell‐bound particles in individual cells by imaging flow cytometry
Bibliographic record
Abstract
Innate immune cell-mediated recognition, capture, and engulfment of large particulate targets such as bacteria is known as phagocytosis. This highly dynamic cellular process involves a series of steps including receptor-mediated target binding, phagocytic cup formation, pseudopod extension, and phagosome closure, which depend on distinct actin polymerization events. Using flow cytometry, precise determination of target locations relative to cell membranes (i.e., surface-bound vs. fully engulfed/internalized) during the phagocytic process is difficult to quantify. Here, we describe the application of new analysis features within the IDEAS® software to distinguish internalized and surface-bound particles on individual cells with a high degree of accuracy and reproducibility. Through the use of connected component masks, the accurate discrimination of surface-bound beads versus those internalized is clearly demonstrated. In addition, we were able to further analyze the ratio of beads that had been surface-bound or internalized within individual cells. This novel method of analyzing the phagocytic process provides more accurate determination of target-cell interactions that will assist in examination of the signalling events that occur during the various stages of phagocytosis. © 2017 International Society for Advancement of Cytometry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".