AI-driven high-throughput droplet screening of cell-free gene expression

Jiawei Zhu, Yaru Meng, Wenli Gao, Shuo Yang, Wenjie Zhu, Xiangyang Ji, Xuanpei Zhai, Wan-Qiu Liu, Yuan Luo, Shengjie Ling (), Jian Li () and Yifan Liu ()
Additional contact information
Jiawei Zhu: ShanghaiTech University
Yaru Meng: ShanghaiTech University
Wenli Gao: ShanghaiTech University
Shuo Yang: ShanghaiTech University
Wenjie Zhu: ShanghaiTech University
Xiangyang Ji: ShanghaiTech University
Xuanpei Zhai: ShanghaiTech University
Wan-Qiu Liu: ShanghaiTech University
Yuan Luo: Chinese Academy of Sciences
Shengjie Ling: ShanghaiTech University
Jian Li: ShanghaiTech University
Yifan Liu: ShanghaiTech University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Cell-free gene expression (CFE) systems enable transcription and translation using crude cellular extracts, offering a versatile platform for synthetic biology by eliminating the need to maintain living cells. However, Such systems are constrained by cumbersome composition, high costs, and limited yields due to numerous additional components required to maintain biocatalytic efficiency. Here, we introduce DropAI, a droplet-based, AI-driven screening strategy designed to optimize CFE systems with high throughput and economic efficiency. DropAI employs microfluidics to generate picoliter reactors and utilizes a fluorescent color-coding system to address and screen massive chemical combinations. The in-droplet screening is complemented by in silico optimization, where experimental results train a machine-learning model to estimate the contribution of the components and predict high-yield combinations. By applying DropAI, we significantly simplified the composition of an Escherichia coli-based CFE system, achieving a fourfold reduction in the unit cost of expressed superfolder green fluorescent protein (sfGFP). This optimized formulation was further validated across 12 different proteins. Notably, the established E. coli model is successfully adapted to a Bacillus subtilis-based system through transfer learning, leading to doubled yield through prediction. Beyond CFE, DropAI offers a high-throughput and scalable solution for combinatorial screening and optimization of biochemical systems.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-58139-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58139-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-58139-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().