Time-Resolved Fluorescence Imaging and Correlative Cryo-Electron Tomography to Study Structural Changes of the HIV-1 Capsid
Bibliographic record
Abstract
The conical HIV-1 capsid protects the internal viral genome and facilitates the infection of target cells. Highly potent antivirals, such as the clinically approved drug Lenacapavir (LEN), block HIV-1 replication by changing the capsid structure and modulating its function. However, structural studies of the HIV-1 capsid, its disassembly, or stabilization by antivirals have been challenging. Here, we developed a correlative light and cryo-electron microscopy (CLEM) workflow to characterize HIV-1 capsid morphology, starting from a small volume of viral particles harvested from cellular supernatants. We report two critical improvements in sample preparation, namely, (1) affinity capture and retention of fluorescent HIV-1 particles on cryo-EM grids to enable mapping virus/capsid location prior to sample vitrification and (2) streamlined alignment protocols to subsequently identify and correlate regions of interest in fluorescence and cryo-EM images. These improvements enable a reproducible CLEM workflow to accurately locate capsids for cryo-electron tomography (cryo-ET) studies. Using this approach, we resolved ultrastructures of HIV-1 capsids treated with LEN and the cellular metabolite inositol hexaphosphate (IP6), revealing distinct modes of capsid lattice stabilization. Finally, using our CLEM workflow, we demonstrate the feasibility of correlating time-resolved fluorescence imaging of capsid disassembly to end point cryo-ET structures. These advances will facilitate in vitro structural studies to define the mechanisms of HIV-1 capsid stabilization and disassembly. The CLEM workflow developed here can also be extended to studying structural changes in other viruses in response to diverse stimuli.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".