ArticleNucleic acids research2025
Single-cell genome and transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Exploring single-cell and multi-omics technologies and their role in unraveling tumor heterogeneity of hepatocellular carcinoma.Journal of liver cancer · 2026Review
- Article
- Epigenetic Modulators and Immunotherapy in Malignant Melanoma.Oncology research · 2026Review
- Absolute copy number aware CNV calling of sub-megabase segments in ultra-low coverage single-cell DNA sequencing data.Nucleic acids research · 2025Article
- Opportunities, obstacles and challenges of nano-immunotherapy in melanoma.Frontiers in immunology · 2025Review
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Authors and funding
15 authors.
Funding
Abstract
Single-cell multi-omics methods enable the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at single-cell and pseudo-bulk levels compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited resolution and accuracy, underlining the importance of affordable multi-omic approaches. Applying Gtag&T-seq to a melanoma xenograft model before treatment and at minimal residual disease revealed differential cell state plasticity and treatment response between cancer subclones. In summary, Gtag&T-seq is a low-cost and accurate single-cell multi-omics method that explores genetic alterations and their functional consequences in single cells at scale.
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Registered trials
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