Protocol for the recovery and deep sequencing of short ssDNA pools from transient, fuel-dependent coacervate droplets

Complex coacervate droplets are synthetic cell models that sequester nucleic acids. Here, we present a protocol for the recovery and deep sequencing of single-stranded DNA (ssDNA) from metabolically active coacervate droplets. We describe time-resolved harvesting, ssDNA recovery, and sequence-independent library preparation for next generation sequencing (NGS), along with an analysis pipeline to assess enrichment dynamics and sequence distributions. The protocol is adaptable to diverse droplet systems. For complete details on the use and execution of this protocol, please refer to Machatzke et al.<sup>1</sup>.

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Publication Details

Journal
STAR Protocols
Published
2025-12-23
DOI
https://doi.org/10.1016/j.xpro.2025.104293
Primary Topic
Cancer Genomics and Diagnostics
Type
article
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article

Protocol for the recovery and deep sequencing of short ssDNA pools from transient, fuel-dependent coacervate droplets

Hannes Mutschler, Anna-Lena Holtmannspötter, Corbin Machatzke, Job Boekhoven
STAR Protocols
Cancer Genomics and Diagnostics
article

Protocol for the recovery and deep sequencing of short ssDNA pools from transient, fuel-dependent coacervate droplets

Hannes Mutschler, Anna-Lena Holtmannspötter, Corbin Machatzke, Job Boekhoven
article en

Abstract

Complex coacervate droplets are synthetic cell models that sequester nucleic acids. Here, we present a protocol for the recovery and deep sequencing of single-stranded DNA (ssDNA) from metabolically active coacervate droplets. We describe time-resolved harvesting, ssDNA recovery, and sequence-independent library preparation for next generation sequencing (NGS), along with an analysis pipeline to assess enrichment dynamics and sequence distributions. The protocol is adaptable to diverse droplet systems. For complete details on the use and execution of this protocol, please refer to Machatzke et al.<sup>1</sup>.

STAR ProtocolsVol. 7(1)
TU Dortmund University (DE), Technical University of Munich (DE)
Openalex Percentile: Top 34%
Cancer Genomics and Diagnostics
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