High content quantitative imaging of <i>Mycobacterium tuberculosis</i> responses to acidic microenvironments within human macrophages
Bibliographic record
Abstract
Intracellular pathogens such as Mycobacterium tuberculosis (Mtb) have evolved diverse strategies to counteract macrophage defence mechanisms including phagolysosomal biogenesis. Within macrophages, Mtb initially resides inside membrane-bound phagosomes that interact with lysosomes and become acidified. The ability of Mtb to control and subvert the fusion between phagosomes and lysosomes plays a key role in the pathogenesis of tuberculosis. Therefore, understanding how pathogens interact with the endolysosomal network and cope with intracellular acidification is important to better understand the disease. Here, we describe in detail the use of fluorescence microscopy-based approaches to investigate Mtb responses to acidic environments in cellulo. We report high-content imaging modalities to probe Mtb sensing of external pH or visualise in real-time Mtb intrabacterial pH within infected human macrophages. We discuss various methodologies with step-by-step analyses that enable robust image-based quantifications. Finally, we highlight the advantages and limitations of these different approaches and discuss potential alternatives that can be applied to further investigate Mtb-host cell interactions. These methods can be adapted to study host-pathogen interactions in different biological systems and experimental settings. Altogether, these approaches represent a valuable tool to further broaden our understanding of the cellular and molecular mechanisms underlying intracellular pathogen survival.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".