Overlap Intensity: An ImageJ Macro for Analyzing the HIV-1 In Situ Uncoating Assay
Zachary Ingram1, Hannah Matheney1, Emma Wise1
1Department of Biomedical Sciences, Missouri State University, Springfield, MO 65987, USA.
Insights
This study introduces an ImageJ macro to automate HIV-1 capsid uncoating analysis, improving accuracy and accessibility. The new method replaces manual counting, reducing bias and saving time in HIV-1 replication studies.
Area of Science:
- Virology
- Molecular Biology
- Biophysics
Background:
- Capsid uncoating is a critical stage in HIV-1 replication.
- Novel assays have advanced the study of HIV-1 uncoating.
- Current manual analysis methods for in situ uncoating assays can be time-consuming and introduce bias.
Purpose of the Study:
- To develop an automated ImageJ macro for quantifying HIV-1 capsid uncoating.
- To improve the accuracy and efficiency of the in situ uncoating assay.
- To provide a versatile tool for analyzing overlapping fluorescent signals in biological imaging.
Main Methods:
- Development of the Overlap Intensity macro for ImageJ.
- Automation of viral core detection and signal quantification.
- Comparison of macro performance against manual counting methods.
- Validation using an in situ uncoating assay to observe progressive uncoating.
Main Results:
- The Overlap Intensity macro accurately detects viral cores and quantifies overlapping signals.
- Automated analysis showed high correlation with manual methods.
- The macro successfully detected progressive capsid uncoating in the assay.
- The macro offers adjustable settings for broader applications.
Conclusions:
- The Overlap Intensity macro enhances the accessibility and reliability of the in situ uncoating assay.
- This tool reduces the need for manual labor and specialized software.
- The macro is a valuable asset for HIV-1 research and other imaging analyses requiring quantification of overlapping fluorescent signals.
Abstract:
Capsid uncoating is at the crossroads of early steps in HIV-1 replication. In recent years, the development of novel assays has expanded how HIV-1 uncoating can be studied. In the in situ uncoating assay, dual fluorescently labelled virus allows for the identification of fused viral cores. Antibody staining then detects the amount of capsid associated with each viral core at different times post-infection. Following fixed cell imaging, manual counting can be used to assess the fusion state and capsid signal for each viral core, but this method can introduce bias with increased time of analysis. To address these limitations, we developed the Overlap Intensity macro in ImageJ. This macro automates the detection of viral cores and quantification of overlapping fusion and capsid signals. We demonstrated the high accuracy of the macro by comparing core detection to manual methods. Analysis of an in situ uncoating assay further verified the macro by detecting progressive uncoating as expected. Therefore, this macro improves the accessibility of the in situ uncoating assay by replacing time-consuming manual methods or the need for expensive data analysis software. Beyond the described assay, the Overlap Intensity macro includes adjustable settings for use in other methods requiring quantification of overlapping fluorescent signals.
