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Updated: Jan 18, 2026
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
Published on: November 19, 2018
Reconstruction of three human lymphocyte subtypes for benchmarking in 3D morphology and modeling
Jiahong Jin1, Tianshuai Li2, Hongda Liu2
1Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China; Department of Physics, East Carolina University, Greenville, NC 27858, USA; School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China.
Background And Objective:
Lymphocytes play critical roles in human immune response. Reconstruction of human primary cells from confocal image stacks provides important benchmark data for phenotype comparison and enables optical modeling to understand, for example, label-free classification.
Methods:
We present a novel method of section-for-clustering (SFC) to automate organelle segmentation in all slices of a fluorescence confocal image stack by taking the advantage of spatial correlation among slices for reconstruction of live primary cells.
Results:
A total of 217 live CD4+ T, CD8+ T and CD19+ B cells have been isolated from human spleen tissues for staining and confocal imaging. The SFC method has been applied to determine 24 cellular, nuclear and mitochondrial parameters for comparison of 3D morphology and all lymphocytes have been found to possess large nucleus-to-cell volume ratios. Although CD4+ T and CD8+ T cells exhibit high morphological similarity as expected, multiple parameters reveal statistically significant differences between CD4+ T and CD19+ B cells. The subtypes were classified by morphological parameters using a support vector machine method with accuracies much less than those by diffraction images. To illustrate the difference, we derived realistic optical cell models from the reconstructed lymphocytes to demonstrate that varied refractive index within organelles can supply intriguing features for accurate classification.
Conclusions:
The presented method provides an accurate, efficient and robust approach to automate organelle segmentation of fluorescence confocal image stacks and yields one of the largest morphological databases on primary human lymphocytes for quantitative 3D assay and optical modeling.
