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Updated: Aug 8, 2026
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
Published on: November 19, 2018
Three-marker phenotypic analysis of lymphocytes based on two-color immunofluorescence using a multinomial model for
W L van Putten1, J Kortboyer, R L Bolhuis
1Department of Statistics, Dr Daniel den Hoed Cancer Center, Rotterdam, The Netherlands.
Insights
This study introduces a statistical model for analyzing multi-marker flow cytometry data, enabling more accurate cell classification. The method improves the analysis of complex cell phenotypes by combining multiple two-marker classifications.
Area of Science:
- Immunology
- Biostatistics
- Computational Biology
Background:
- Simultaneous analysis of multiple markers in flow cytometry is technically limited.
- Existing methods struggle with high-dimensional cell phenotyping.
- Need for robust statistical approaches to interpret complex flow cytometry data.
Purpose of the Study:
- To develop a statistical model for analyzing multi-marker flow cytometry data derived from multiple two-way classifications.
- To enable accurate n-way cell classification (n >= 3) from simpler experiments.
- To address technical limitations in high-parameter flow cytometry.
Main Methods:
- A formal statistical model based on multinomial distribution for quadrant counts.
- Utilizing multiple two-way classifications to derive n-way classifications.
- Application of the Expectation-Maximization (EM) algorithm for maximum likelihood estimation.
- Testing the model on peripheral blood mononuclear cells for coexpression analysis of CD45RA, CD45RO, and Leu 8 on CD56+ and CD3+ cell subsets.
Main Results:
- The statistical model demonstrated an excellent fit for analyzing three-marker phenotypic data.
- The approach successfully derived three-marker classifications from multiple two-marker classifications.
- The model reduced inter-assay variation and identified potential outliers due to staining errors.
- Accurate coexpression analysis of specific markers on lymphocyte subsets was achieved.
Conclusions:
- The proposed statistical model provides a robust framework for analyzing complex flow cytometry data.
- This method overcomes technical limitations, enabling more precise multi-marker cell analysis.
- The model enhances the reliability and accuracy of phenotypic classification in immunology research.
- It offers a valuable tool for studying cell subsets and their marker expression patterns.
Abstract:
The simultaneous flow cytometric study of multiple (> or = 3) markers on individual cells is restricted by technical reasons, e.g., the type of flow cytometer, and the availability of (monoclonal) antibodies (mAb) conjugated with the appropriate fluorochromes. However, a n-way classification (n > or = 3) may be derived from multiple 2-way classifications. The 2-way classifications are obtained by the use of only two fluorochromes, but each fluorochrome may be used for the simultaneous labelling of 2 or more mAb. We present a formal statistical model, based on an underlying multinomial distribution for the observed quadrant counts, by which data from such multiple 2-way classifications can be analyzed. The model is restricted to 3-marker phenotypic analyses, but can be extended to n-way classifications (n > 3). Maximum likelihood estimates are obtained by the application of the EM algorithm. The model was tested on 20 samples of peripheral blood mononuclear cells to study the coexpression of CD45RA, CD45RO, and Leu 8 by lymphocyte subsets defined by the CD56 (MHC-unrestricted cytotoxic cells) and CD3 (T cells) markers. Application of the model gave an excellent fit in all but one cases. In addition, the model reduced the effect of inter-assay variation on the estimates and it provided an analysis of consistency over the data, which allows the detection of outliers due to staining errors.
