Author(s): Liu, LX (Liu, Lixing); Yang, HY (Yang, Hongyu); Men, D (Men, Dong); Wang, M (Wang, Meng); Gao, XL (Gao, Xiaolei); Zhang, T (Zhang, Ting); Chen, DY (Chen, Deyong); Xue, CL (Xue, Chunlai); Wang, YX (Wang, Yixiang); Wang, JB (Wang, Junbo); Chen, J (Chen, Jian)
Source: BIOSENSORS & BIOELECTRONICS Volume: 155 Article Number: 112097 DOI: 10.1016/j.bios.2020.112097 Published: MAY 1 2020
Abstract: Quantification of single-cell proteins plays key roles in cell heterogeneity while due to technical limitations absolute numbers of multiple intracellular proteins from large populations of single cells were still missing, leading to compromised results in cell-type classifications. This paper presents a microfluidic platform capable of high-throughput absolute quantification of single-cell multiple types of intracellular proteins where cells stained with fluorescent labelled antibodies are aspirated into the constriction microchannels with excited fluorescent signals detected and translated into numbers of binding sites of targeted proteins based on calibration curves formed by flushing gradient solutions of fluorescent labelled antibodies directly into constriction microchannels. Based on this approach, single-cell numbers of binding sites of beta-actin, alpha-tubulin and beta-tubulin from tens of thousands of five representative tumor cell lines were first quantified, reporting cell-type classification rates of 83.0 +/- 7.1%. Then single-cell numbers of binding sites of beta-actin, biotin and RhoA from thousands of five tumor cell lines with varieties in malignant levels were quantified, reporting cell-type classification rates of 93.7. 2.8%. Furthermore, single-cell numbers of binding sites of Ras, c-Myc and p53 from thousands of cells derived from two oral tumor lines of CAL 27, WSU-HN6 and two oral tumor patient samples were quantified, contributing to high classifications of both tumor cell lines (98.6%) and tumor patient samples (83.4%). In conclusion, the developed microfluidic platform was capable of quantifying multiple intracellular proteins from large populations of single cells, and the collected data of protein expressions enabled effective cell-type classifications.
Accession Number: WOS:000523556800006
PubMed ID: 32090869
ISSN: 0956-5663
eISSN: 1873-4235
Full Text: https://www.sciencedirect.com/science/article/pii/S0956566320300944?via%3Dihub