Research ArticleGENETICS

Digital-WGS: Automated, highly efficient whole-genome sequencing of single cells by digital microfluidics

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Science Advances  09 Dec 2020:
Vol. 6, no. 50, eabd6454
DOI: 10.1126/sciadv.abd6454
  • Fig. 1 Design and operation of the Digital-WGS platform.

    (A) Schematic representation of the experimental setup. A snapshot of three units of the microfluidic device is shown. After a single cell is trapped in the butterfly structure, it is mixed with the cell lysis solution, followed by consecutive reactions of WGA for sequencing. (B) Top-view schematic of single dyed K562 cell captured in the butterfly structure. (C) Finite-element analysis of the cell dynamics in the Digital-WGS chip. Individual cells are 15 μm wide, and the openings are 10 μm wide. Scale bars, 50 μm.

  • Fig. 2 Characterization of the Digital-WGS for single-cell isolation.

    (A) Procedure used to test for single-cell isolation on the chip (top) and serial images captured from one trapping cycle (bottom). (B) Comparison of cell viability between before (left) and after on-chip droplet actuation of 30 min (right). Merged image of living cells stained with calcein AM (green), while dead cells were stained with propidium iodide (red). Scale bars, 50 μm. (C) Quantification of DNA integrity assayed using the single-cell gel electrophoresis comet assay for DNA damage of percent fragmented DNA and the Olive moment. (D) Capture efficiency at different settling times and cell concentrations. (E) Capture efficiency for rare cells completed in a single separation (26 cells in one droplet). (F) Comparison of capture efficiency of various cell lines. These cell lines have different cell sizes and surface properties, which may affect single-cell isolation.

  • Fig. 3 Comparison of WGA methods using low-depth and high-depth WGS.

    (A) Normalized read depth plots with a mean bin size of 52.4 kb for the three-sample samples with the lowest SD from each method in the 0.75× depth. (B) Coverage and mapping rate for different amplification methods in the 0.75× depth. (C) Lorenz curves depicting uniformity of coverage for samples in reads with a mean bin size of 52.4 kb from each amplification method in the 10× depth. (D) CV for read depths along the genome as a function of bin size from 1 b to 100 Mb in the 10× depth, showing amplification noise on all scales for single-cell MDA methods. (E) Coverage breadth as a function of sequencing depth for the sample with the lowest CV from each method.

  • Fig. 4 Summary of the comparison between different methods for SNV detection in single MRC-5 cells and CNV detection in single K562 cells.

    (A) Single-cell SNVs including heterozygous and homozygous SNVs detected by MDA, Digital-WGS, and unamplified bulk for single MRC-5 cells. (B) False-negative rate and ADO rate of SNV detection in single-cell MDA, single-cell Digital-WGS, and unamplified bulk for MRC-5 cells. (C) False-positive rates of SNV detection in single-cell MDA, single-cell Digital-WGS, two-cell Digital-WGS, three-cell Digital-WGS, and unamplified bulk for MRC-5 cells. (D) Circos map for CNV analysis in the K562 genome including tracks as follows: Odd tracks from inside out respectively exhibited the raw coverage depths of bulk, Digital-WGS, and MDA across the whole genome, while the even tracks showed the CNV results estimated by Ginkgo using 50-kb variable bins. (E) Copy number distribution of single K562 cell with a binning size of 52.4 kb. (F) The heat map showed CNV patterns from two parallel contrast groups of Digital-WGS and MDA assays. The cluster dendrogram on the left was generated with the Euclidean distances.

Supplementary Materials

  • Supplementary Materials

    Digital-WGS: Automated, highly efficient whole-genome sequencing of single cells by digital microfluidics

    Qingyu Ruan, Weidong Ruan, Xiaoye Lin, Yang Wang, Fenxiang Zou, Leiji Zhou, Zhi Zhu, Chaoyong Yang

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