High-throughput assays of phagocytosis, phagosome maturation, and bacterial invasion
Bibliographic record
Abstract
Ingestion of foreign particles by macrophages and neutrophils and the fate of the vacuole that contains the ingested material are generally monitored by optical microscopy. Invasion of host cells by pathogenic bacteria and their intracellular proliferation are similarly studied by microscopy or by plating assays. These labor-intensive and time-consuming methods limit the number of assays that can be performed. The effort required to test multiple reagents or conditions can be prohibitive. We describe high-throughput assays of phagocytosis and of phagosomal maturation. An automated fluorescence microscope-based platform and associated analysis software were used to study Fcgamma receptor-mediated phagocytosis of IgG-opsonized particles by cultured murine macrophages. Phagosomal acidification was measured as an index of maturation. The same platform was similarly used to implement high-throughput assays of invasion of mammalian cells by pathogenic bacteria. The invasion of HeLa cells by Salmonella and the subsequent intracellular proliferation of the bacteria were measured rapidly and reliably in large populations of cells. These high-throughput methods are ideally suited for the efficient screening of chemical libraries to select potential drugs and of small interference RNA libraries to identify essential molecules involved in critical steps of the immune response.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".