Rxivist logo

Dean flow assisted single cell and bead encapsulation for high performance single cell expression profiling

By Luoquan Li, Ping Wu, Zhaofeng Luo, Lei Wang, Weiping Ding, Tao Wu, Jinyu Chen, Jinlong He, Yi He, Heran Wang, Ying Chen, Guibo Li, Zida Li, Liqun He

Posted 16 Jan 2019
bioRxiv DOI: 10.1101/520858 (published DOI: 10.1021/acssensors.9b00171)

Single-cell RNA sequencing examines the transcriptome of individual cells and reveals the inter-cell transcription heterogeneity, playing a critical role in both scientific research and clinical applications. Recently, droplet microfluidics-based platform for expression profiling has been shown as a powerful tool to capture of the transcriptional information on single cell level. Despite the breakthrough this platform brought about, it required the simultaneous encapsulation of single cell and single barcoded bead, the incidence of which was very low. Suboptimal capturing efficiency limited the throughput of the Drop-seq platform. In this work, we leveraged the advance in inertial microfluidics-based cell sorting and designed a microfluidic chip for high efficiency cell-bead co-encapsulation, increasing the capturing rate by more than four folds. Specifically, we adopted spiral and serpentine channels and ordered cells/beads before the encapsulation region. We characterized the effect of cell concentration on the capturing rate and achieved a cell-bead co-capturing rate up to 3%. We tested this platform by co-encapsulating barcoded beads and human-mouse cell mixtures. The sequencing data distinguished the majority of human and mice expressions, with the doublet rate being as low as 5.8%, indicating that the simultaneous capturing of two or more cells in one droplet was minimal even when using high cell concentration. This chip design showed great potential in improving the efficiency for future single cell expression profiling.

Download data

  • Downloaded 784 times
  • Download rankings, all-time:
    • Site-wide: 33,555
    • In bioengineering: 607
  • Year to date:
    • Site-wide: 67,047
  • Since beginning of last month:
    • Site-wide: 37,766

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide