Mechanical property characterization of hundreds of single nuclei based on microfluidic constriction channel. Issue 8 (31st July 2018)

• Record Type: Journal Article
• Authors: Chang, Chun‐Chieh; Wang, Ke; Zhang, Yi; Chen, Deyong; Fan, Beiyuan; Hsieh, Chia‐Hsun; Wang, Junbo; Wu, Min‐Hsien; Chen, Jian
• Is Part Of: Cytometry. Volume 93:Issue 8(2018)
• Page Start: 822
• Page End: 828
• Publication Date: 2018-07-31
• Subjects: single‐nucleus analysis -- microfluidics -- mechanical properties -- nucleus isolation -- constriction channel; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.23386
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: As label‐free biomarkers, the mechanical properties of nuclei are widely treated as promising biomechanical markers for cell type classification and cellular status evaluation. However, previously reported mechanical parameters were derived from only around 10 nuclei, lacking statistical significances due to low sample numbers. To address this issue, nuclei were first isolated from SW620 and A549 cells, respectively, using a chemical treatment method. This was followed by aspirating them through two types of microfluidic constriction channels for mechanical property characterization. In this study, hundreds of nuclei were characterized, producing passage times of 0.5 ± 1.2 s for SW620 nuclei in type I constriction channel ( n = 153), 0.045 ± 0.047 s for SW620 nuclei in type II constriction channel ( n = 215) and 0.50 ± 0.86 s for A549 nuclei in type II constriction channel. In addition, neural network based pattern recognition was used to classify the nuclei isolated from SW620 and A549 cells, producing successful classification rates of 87.2% for diameters of nuclei, 85.5% for passage times of nuclei and 89.3% for both passage times and diameters of nuclei. These results indicate that the characterization of the mechanical properties of nuclei may contribute to the classification of different tumor cells.