Accurate Identification of Cell Cycle Stages in RPE1 Cells Using the ImmunoCellCycle-ID Method
Accurate identification of cell cycle stages is essential for investigating fundamental biological processes such as proliferation, differentiation, and tumorigenesis. While flow cytometry remains a widely used technique for such analyses, it is limited by its lack of single-cell resolution and its requirement for large sample sizes due to its population-based approach. These limitations underscore the need for alternative or complementary methods that offer single-cell precision with compatibility for small-scale applications. We present ImmunoCellCycle-ID, an immunofluorescence-based method that leverages the spatial distribution of endogenous markers, such as DNA, proliferating cell nuclear antigen (PCNA), centromere protein F (CENP-F), and centromere protein C (CENP-C), to reliably distinguish G1, early S, late S, early G2, late G2, and all mitotic sub-stages. This technique does not rely on precise signal quantification and utilizes standard immunofluorescence protocols alongside conventional laboratory microscopes, ensuring broad accessibility. Importantly, ImmunoCellCycle-ID detects endogenous proteins without the need for genetic modification, making it readily applicable to a wide range of human cell lines. Beyond its utility for single-cell resolution, the method can be scaled for population-level analyses, similar to flow cytometry. With its precision, versatility, and ease of implementation, ImmunoCellCycle-ID offers a powerful tool for high-resolution cell cycle profiling across diverse experimental platforms. Key features • Enables high-precision identification of cell cycle stages at single-cell resolution. • Broadly applicable to diverse human cell lines without genetic modification. • Fully compatible with standard fluorescence microscopy; no specialized equipment needed. • Requires only minimal image analysis and no complex quantification.
Authors
- Aussie Suzuki (ORCID: https://orcid.org/0000-0001-7390-5116)
- Yulin Chen (ORCID: https://orcid.org/0000-0001-5679-4055)
- Syon Reddy
Institutions
- University of Wisconsin–Madison (US)
- University of Wisconsin Carbone Cancer Center (US)
Publication Details
- Journal
- BIO-PROTOCOL
- Published
- 2025-01-01
- DOI
- https://doi.org/10.21769/bioprotoc.5407
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00