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Updated: Feb 20, 2026

A Flow Cytometry-Based Cell Surface Protein Binding Assay for Assessing Selectivity and Specificity of an Anticancer Aptamer
Published on: September 13, 2022
High Throughput Flow Cytometry for Cell Surface Profiling.
Joshua Paterson1, Laurie E Ailles2
1Princess Margaret Cancer Centre, University Health Network, 610 University Ave., Toronto, ON, Canada, M5G 1L7.
High-throughput flow cytometry enables comprehensive cell surface protein profiling, revealing novel biomarkers and cell populations in complex samples. This cost-effective method enhances biomarker discovery and understanding of cellular heterogeneity.
Area of Science:
- Cellular and Molecular Biology
- Immunology
- Biomarker Discovery
Background:
- Cell surface proteins are crucial for identifying biomarkers and therapeutic targets.
- Limited experimental data exists on the surfaceome of individual cells, especially in heterogeneous samples.
- Transcriptome and surfaceome often show poor correlation, necessitating direct surface protein analysis.
Purpose of the Study:
- To develop and describe a method for high-throughput cell surface profiling using flow cytometry.
- To enable the discovery of novel cell surface markers and subpopulations.
- To provide a cost-effective, scalable approach for analyzing the surfaceome.
Main Methods:
- Utilized conventional single or multicolor flow cytometry for cell surface protein analysis.
- Developed a microarray-like screening platform for comprehensive surfaceome interrogation.
- Adapted the method for flexible antibody panel sizes, enabling customized screening.
Main Results:
- Demonstrated the capability of flow cytometry to profile cell surface proteins at single-cell resolution.
- Showcased the platform's ability to reveal unexpected markers and rare cell subpopulations.
- Generated reliable, cost-effective data from diverse sample types.
Conclusions:
- High-throughput flow cytometry offers a powerful, scalable solution for cell surface profiling.
- This approach facilitates the discovery of novel biomarkers and enhances the understanding of cellular heterogeneity.
- The method supports advanced bioinformatics analyses, such as hierarchical clustering, for deeper insights.
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