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.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a major class of natural products with diverse chemical structures and potent biological activities. A vast majority of RiPP gene clusters remain unexplored in microbial genomes, which is partially due to the lack of rapid and efficient heterologous expression systems for RiPP characterization and biosynthesis. Here, we report a unified biocatalysis (UniBioCat) system based on cell-free gene expression for rapid biosynthesis and engineering of RiPPs. We demonstrate UniBioCat by reconstituting a full biosynthetic pathway for de novo biosynthesis of salivaricin B, a lanthipeptide RiPP. Next, we delete several protease/peptidase genes from the source strain to enhance the performance of UniBioCat, which then can synthesize and screen salivaricin B variants with enhanced antimicrobial activity. Finally, we show that UniBioCat is generalizable by synthesizing and evaluating the bioactivity of ten uncharacterized lanthipeptides. We expect UniBioCat to accelerate the discovery, characterization, and synthesis of RiPPs.

Simultaneous detection of proteins and mRNA within a single extracellular vesicle (EV) enables comprehensive analysis of specific EVs subpopulations, significantly advancing cancer diagnostics. However, developing a sensitive and user-friendly approach for simultaneously detecting multidimensional biomarkers in single EV is still challenging. To facilitate the analysis of multidimensional biomarkers in EVs and boost its clinical application, we present a versatile droplet digital system facilitating the concurrent detection of membrane proteins and mRNA at the single EV level with high sensitivity and specificity. The antibody-DNA conjugates were firstly prepared for EVs protein biomarkers recognition and signal transformation. Coupling with the assembled triplex droplet digital PCR system, a versatile droplet digital analysis assay for simultaneous detection of membrane protein and mRNA at a single EV level was developed. Our new droplet digital system displayed high sensitivity and specificity. Additionally, its clinical application was validated in a breast cancer cohort. As expected, this assay has demonstrated superior performance in distinguishing breast cancer from healthy individuals and benign controls through combined detection of EVs protein and mRNA markers compared to any single kind marker detections, especially for patients with breast cancer at early stage (AUC=0.9229). Consequently, this study proposes a promising strategy for accurately identifying and analyzing specific EV subgroups through the co-detection of proteins and mRNA at the single EV level, holding significant potential for future clinical applications.