Confocal Imaging of Double-Stranded RNA and Pattern Recognition Receptors in Negative-Sense RNA Virus Infection
Bibliographic record
Abstract
Double-stranded (ds) RNA is produced as a replicative intermediate during RNA virus infection. Recognition of dsRNA by host pattern recognition receptors (PRRs) such as the retinoic acid (RIG-I) like receptors (RLRs) RIG-I and melanoma differentiation-associated protein 5 (MDA-5) leads to the induction of the innate immune response. The formation and intracellular distribution of dsRNA in positive-sense RNA virus infection has been well characterized by microscopy. Many negative-sense RNA viruses, including some arenaviruses, trigger the innate immune response during infection. However, negative-sense RNA viruses were thought to produce low levels of dsRNA, which hinders the imaging study of PRR recognition of viral dsRNA. Additionally, infection experiments with highly pathogenic arenaviruses must be performed in high containment biosafety level facilities (BSL-4). The interaction between viral RNA and PRRs for highly pathogenic RNA virus is largely unknown due to the additional technical challenges that researchers need to face in the BSL-4 facilities. Recently, a monoclonal antibody (Mab) (clone 9D5) originally used for pan-enterovirus detection has been found to specifically detect dsRNA with a higher sensitivity than the traditional J2 or K1 anti-dsRNA antibodies. Herein, by utilizing the 9D5 antibody, we describe a confocal microscopy protocol that has been used successfully to visualize dsRNA, viral protein and PRR simultaneously in individual cells infected by arenavirus. The protocol is also suitable for imaging studies of dsRNA and PRR distribution in pathogenic arenavirus infected cells in BSL4 facilities.
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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 | 0.001 | 0.000 |
| 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".