Open AccessDevelopment of droplet microfluidics capable of quantitative estimation of single-cell multiplex proteins
Author(s) -
Hongbin Yang,
Guang Yang,
Ting Zhang,
Deyong Chen,
Junbo Wang,
Jian Chen
Publication year - 2021
Publication title -
journal of micromechanics and microengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.494
H-Index - 132
eISSN - 1361-6439
pISSN - 0960-1317
DOI - 10.1088/1361-6439/ac4008
Subject(s) - microfluidics , multiplex , tubulin , microchannel , chemistry , cell , fluorescence , biophysics , actin , nanotechnology , biochemistry , materials science , microtubule , biology , bioinformatics , microbiology and biotechnology , physics , quantum mechanics
This study presented constriction microchannel based droplet microfluidics realizing quantitative measurements of multiplex types of single-cell proteins with high throughput. Cell encapsulation with evenly distributed fluorescence labelled antibodies stripped from targeted proteins by proteinase K was injected into the constriction microchannel with the generated fluorescence signals captured and translated into protein numbers leveraging the equivalent detection volume formed by constriction microchannels in both droplet measurements and fluorescence calibration. In order to form the even distribution of fluorescence molecules within each droplet, the stripping effect of proteinase K to decouple binding forces between targeted proteins and fluorescence labelled antibodies was investigated and optimized. Using this microfluidic system, binding sites for beta-actin, alpha-tubulin, and beta-tubulin were measured as 1.15 ± 0.59 × 10 6 , 2.49 ± 1.44 × 10 5 , and 2.16 ± 1.01 × 10 5 per cell of CAL 27 ( N cell = 15 486), 0.98 ± 0.58 × 10 6 , 1.76 ± 1.34 × 10 5 and 0.74 ± 0.74 × 10 5 per cell of Hep G2 ( N cell = 18 266). Neural net pattern recognition was used to differentiate CAL 27 and Hep G2 cells, producing successful rates of 59.4% (beta-actin), 64.9% (alpha-tubulin), 88.8% (beta-tubulin), and 93.0% in combination, validating the importance of quantifying multiple types of proteins. As a quantitative tool, this approach could provide a new perspective for single-cell proteomic analysis.