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Scalable whole-genome single-cell library preparation without pre-amplification

Single-cell genomics is critical for understanding cellular heterogeneity in cancer, but existing library preparation methods are expensive, require sample preamplification and introduce coverage bias. Here we describe direct library preparation (DLP), a robust, scalable, and high-fidelity method that uses nanoliter-volume transposition reactions for single-cell whole-genome library preparation without preamplification. We examined 782 cells from cell lines and triple-negative breast xenograft tumors. Low-depth sequencing, compared with existing methods, revealed greater coverage uniformity and more reliable detection of copy-number alterations. Using phylogenetic analysis, we found minor xenograft subpopulations that were undetectable by bulk sequencing, as well as dynamic clonal expansion and diversification between passages. Merging single-cell genomes in silico, we generated "bulk-equivalent" genomes with high depth and uniform coverage. Thus, low-depth sequencing of DLP libraries may provide an attractive replacement for conventional bulk sequencing methods, permitting analysis of copy number at the cell level and of other genomic variants at the population level.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001003148 NextSeq 500 192
EGAD00001003149 Illumina HiSeq 2500 384
EGAD00001003150 Illumina HiSeq 2500 384
EGAD00001003151 Illumina HiSeq 2500 3
EGAD00001003152 Illumina HiSeq 2500 192
Publications Citations
Scalable whole-genome single-cell library preparation without preamplification.
Nat Methods 14: 2017 167-173
clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers.
Genome Biol 20: 2019 54
Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.
Brief Bioinform 23: 2022 bbab531
CONET: copy number event tree model of evolutionary tumor history for single-cell data.
Genome Biol 23: 2022 128
Computational Methods for Single-cell Multi-omics Integration and Alignment.
Genomics Proteomics Bioinformatics 20: 2022 836-849