High-throughput generic single-entity sequencing using droplet microfluidics Article Swipe
Guoping Wang
,
Liuyang Zhao
,
Yu Shi
,
Fuyang Qu
,
Yanqiang Ding
,
Weixin Liu
,
Changan Liu
,
Gang Luo
,
Meiyi Li
,
Xiaowu Bai
,
Luoquan Li
,
Yi‐Ping Ho
,
Jun Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.08.15.549386
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.08.15.549386
Summary Single-cell sequencing has revolutionized our understanding of cellular heterogeneity by providing a micro-level perspective over the past decades. Although heterogeneity is essential for various biological communities, the currently demonstrated platform predominantly focuses on eukaryotic cells without cell walls and their transcriptomics 1,2 , leaving significant gaps in the study of omics from other single biological entities such as bacteria and viruses. Due to the difficulty of isolating and acquiring their DNA 3 , contemporary methodologies for the characterization of generic biological entities remain conspicuously constrained, with low throughput 4 , compromised lysis efficiency 5 , and highly fragmented genomes 6 . Herein, we present the Generic Single Entity Sequencing platform (GSE-Seq), which boasts ample versatility, high throughput, and high coverage, and is enabled by an innovative workflow, addressing the critical challenges in single entities sequencing: (1) one-step manufacturing of massive barcodes, (2) degradable hydrogel-based in situ sample processing and whole genome amplification, (3) integrated in-drop library preparation, (4) compatible long-read sequencing. By GSE-Seq, we have achieved a significant milestone by enabling high-throughput, long-read single-entity profiling of dsDNA and ssDNA from single virus sequencing (SV-seq) and single bacteria sequencing (SB-seq) of the human gut and marine sediment for the first time. Notably, our analysis uncovered previously overlooked viral and bacterial dark matter and phage-host interactions. In summary, the presented conceptually new workflow offers a toolbox based on droplet microfluidics to tackle the persistent challenges in high-throughput profiling to generic applications, which hold immense promise for diverse biological entities, especially hard-to-lyse cells.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.08.15.549386
- https://www.biorxiv.org/content/biorxiv/early/2023/08/15/2023.08.15.549386.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385875706
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385875706Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.08.15.549386Digital Object Identifier
- Title
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High-throughput generic single-entity sequencing using droplet microfluidicsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-15Full publication date if available
- Authors
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Guoping Wang, Liuyang Zhao, Yu Shi, Fuyang Qu, Yanqiang Ding, Weixin Liu, Changan Liu, Gang Luo, Meiyi Li, Xiaowu Bai, Luoquan Li, Yi‐Ping Ho, Jun YuList of authors in order
- Landing page
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https://doi.org/10.1101/2023.08.15.549386Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/08/15/2023.08.15.549386.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2023/08/15/2023.08.15.549386.full.pdfDirect OA link when available
- Concepts
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Workflow, DNA sequencing, Computational biology, Microfluidics, Profiling (computer programming), Throughput, Biology, Single-cell analysis, Genome, Computer science, Nanotechnology, DNA, Genetics, Gene, Cell, Database, Telecommunications, Wireless, Materials science, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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