ArticleNature methods2017
Scalable whole-genome single-cell library preparation without preamplification.
Article in Nature methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers.
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Who cites it
118 citing papers in PubMed.
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- A medium throughput approach for single cell copy number variation sequencing towards efficient application in clinics.Journal of advanced research · 2026Article
- LeafRank: A phylodynamic framework for inferring relative fitness from single-cell phylogenies in chromosomally unstable tumors.bioRxiv : the preprint server for biology · 2026Article
- Single-cell aneuploidy and chromosomal arm imbalances define subclones with divergent transcriptomic phenotypes.NAR genomics and bioinformatics · 2025Article
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Absolute copy number aware CNV calling of sub-megabase segments in ultra-low coverage single-cell DNA sequencing data.Nucleic acids research · 2025Article
- Article
- PhyClone: accurate Bayesian reconstruction of cancer phylogenies from bulk sequencing.Bioinformatics (Oxford, England) · 2025Article
- High-resolution detection of copy number alterations in single cells with HiScanner.Nature communications · 2025Article
- The Somatic Mosaicism across Human Tissues Network.Nature · 2025Review
- Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding.Nature communications · 2025Article
- Engineering next-generation microfluidic technologies for single-cell phenomics.Nature genetics · 2025Review
- Trait-space patterning is dictated by the tempo and mode of mutation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Differential performance of strategies for single-cell whole-genome amplification.Cell reports methods · 2025Article
- CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.Genome biology · 2025Article
- High-resolution detection of copy number alterations in single cells with HiScanner.bioRxiv : the preprint server for biology · 2025Article
- Single-cell genome and transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones.Nucleic acids research · 2025Article
- Single-cell copy number calling and event history reconstruction.Bioinformatics (Oxford, England) · 2025Article
- Scalable co-sequencing of RNA and DNA from individual nuclei.Nature methods · 2025Article
- Review
58 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.