Expanding Insights: Harnessing Expansion Microscopy for Super-Resolution Analysis of HIV-1-Cell Interactions
- PMID: 39459943
- PMCID: PMC11512423
- DOI: 10.3390/v16101610
Expanding Insights: Harnessing Expansion Microscopy for Super-Resolution Analysis of HIV-1-Cell Interactions
Abstract
Expansion microscopy has recently emerged as an alternative technique for achieving high-resolution imaging of biological structures. Improvements in resolution are achieved by physically expanding samples through embedding in a swellable hydrogel before microscopy. However, expansion microscopy has been rarely used in the field of virology. Here, we evaluate and characterize the ultrastructure expansion microscopy (U-ExM) protocol, which facilitates approximately four-fold sample expansion, enabling the visualization of different post-entry stages of the HIV-1 life cycle, focusing on nuclear events. Our findings demonstrate that U-ExM provides robust sample expansion and preservation across different cell types, including cell-culture-adapted and primary CD4+ T-cells as well as monocyte-derived macrophages, which are known HIV-1 reservoirs. Notably, cellular targets such as nuclear bodies and the chromatin landscape remain well preserved after expansion, allowing for detailed investigation of HIV-1-cell interactions at high resolution. Our data indicate that morphologically distinct HIV-1 capsid assemblies can be differentiated within the nuclei of infected cells and that U-ExM enables detection of targets that are masked in commonly used immunofluorescence protocols. In conclusion, we advocate for U-ExM as a valuable new tool for studying virus-host interactions with enhanced spatial resolution.
Keywords: HIV-1; HIV-1 capsid; HIV-1 nuclear import; HIV-1 post-entry; expansion microscopy; super-resolution microscopy; ultrastructure expansion microscopy; virus–host interaction.
Conflict of interest statement
The authors declare no conflicts of interest. The funders had no role in the design of this study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
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