, Volume 23, Issue 5–6, pp 290–298 | Cite as

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques

  • Jingwen Feng
  • Tong Feng
  • Chengwen Yang
  • Wei Wang
  • Yu Sa
  • Yuanming Feng
Original Paper


This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.


Apoptosis Stain-free Machine learning Classification Diffraction image 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10495_2018_1454_MOESM1_ESM.pdf (273 kb)
Supplementary material 1 (PDF 273 KB)
10495_2018_1454_MOESM2_ESM.pdf (315 kb)
Supplementary material 2 (PDF 314 KB)
10495_2018_1454_MOESM3_ESM.pdf (118 kb)
Supplementary material 3 (PDF 117 KB)
10495_2018_1454_MOESM4_ESM.pdf (55 kb)
Supplementary material 4 (PDF 55 KB)


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringTianjin UniversityTianjinChina
  2. 2.Department of Radiation OncologyTianjin Medical University Cancer Institute & HospitalTianjinChina

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