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Updated: Jan 7, 2026

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
Published on: May 23, 2018
High-throughput generic single-entity sequencing using droplet microfluidics
Guoping Wang1, Liuyang Zhao1, Yu Shi1
1Institute of Digestive Diseases and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences The Chinese University of Hong Kong Hong Kong SAR China.
Abstract:
Single-cell sequencing has revolutionized our understanding of cellular heterogeneity by providing a micro-level perspective in the past decade. While heterogeneity is fundamental to diverse biological communities, existing platforms are primarily designed for eukaryotic cells, leaving significant gaps in the study of other single biological entities, such as viruses and bacteria. Current methodologies for single-entity sequencing remain limited by low throughput, inefficient lysis, and highly fragmented genomes. Here, we present the Generic Single-Entity Sequencing (GSE-Seq), a versatile and high-throughput framework that overcomes key limitations in single-entity sequencing through an integrated workflow. GSE-Seq combines (1) one-step generation of massive barcodes, (2) degradable hydrogel-based in situ sample processing and whole genome amplification, (3) integrated in-droplet library preparation, and (4) long-read sequencing. We applied GSE-Seq to profile viral communities from human fecal and marine sediment samples, generating thousands of high-quality single-entity genomes and revealing that most are novel. GSE-Seq identified not only dsDNA and ssDNA viruses, but also hard-to-detect giant viruses and crAssphages. GSE-Seq of bacterial genomes also revealed putative novel bacterial species, validating the versatility of this platform across different microbial kingdoms. Collectively, GSE-Seq represents a robust framework that addresses persistent challenges in high-throughput profiling for generic applications and holds immense promise for single-cell deconvolution of diverse biological entities.
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