ArticleMethodsX2022
A fast open-source Fiji-macro to quantify virus infection and transfection on single-cell level by fluorescence microscopy.
Article in MethodsX, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Functional siRNA Delivery via Jet Nebulization: Proof-of-Concept IL-1ß Silencing in Macrophage-like THP-1 Cells.International journal of molecular sciences · 2026Article
- Andrographolide Inhibits Rabies Virus Replication.Transboundary and emerging diseases · 2026Article
- PyPlaque is an open-source python package for phenotypic analysis of virus plaque assays.Scientific reports · 2025Article
- Cellular sialoglycans are differentially required for endosomal and cell-surface entry of SARS-CoV-2 in lung cell lines.PLoS pathogens · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
The ability to automatically analyze large quantities of image data is a valuable tool for many biochemical assays, as it rapidly provides reliable data. Here, we describe a fast and robust Fiji macro for the analysis of cellular fluorescence microscopy images with single-cell resolution. The macro presented here was validated by successful reconstruction of fluorescent and non-fluorescent cell mixing ratios (for fluorescence fractions ranging between 0 and 100%) and applied to quantify the efficiency of transfection and virus infection inhibition. It performed well compared with manually obtained image quantification data. Its use is not limited to the cases shown here but is applicable for most monolayered cellular assays with nuclei staining. We provide a detailed description of how the macro works and how it is applied to image data. It can be downloaded free of charge and may be used by and modified according to the needs of the user. • Rapid, simple, and reproducible segmentation of eukaryotic cells in confluent cellular assays • Open-source software for use without technical or computational expertise • Single-cell analysis allows identification and quantification of virus infected cell populations and infection inhibition.
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