ArticleNature communications2025
AI-driven high-throughput droplet screening of cell-free gene expression.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- A Review on Micromixers, Microdroplet Generators and Their Integration.Micromachines · 2026Review
- Microbial cell-free protein synthesis and its progression toward industrial use.Microbiology (Reading, England) · 2026Review
- Machine learning-driving optimization and spatial assembly of a cell-free system for high-yield liquiritigenin production.Advanced biotechnology · 2026Article
- Design-driven optimization of low-cost reagent formulations for reproducible and high-yielding cell-free gene expression.Nature communications · 2026Article
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Research Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials.ACS omega · 2026Review
- Antibody screening for tumor and immune hotspot targets: The frontier of new methods and technologies.Journal of pharmaceutical analysis · 2026Review
- Lipopeptide Engineering: From Natural Origins to Rational Design Against Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2026Review
- AI and organoid platforms for brain-targeted theranostics.Theranostics · 2026Review
- High-throughput combinatorial screening of antiplatelet drugs for personalized medicine.Microsystems & nanoengineering · 2026Article
- Reconstituting transcription-translation-coupled DNA replication within complex in vitro biological systems.Nature communications · 2025Article
- Deterministic Co-encapsulation of Microparticles in Droplets via Synchronized Merging for Single-Cell Genomics.Analytical chemistry · 2025Article
- Review
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Microdroplet Systems for Gene Transfer: From Fundamentals to Future Perspectives.Micromachines · 2025Review
- Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.ACS central science · 2025Review
- Sensitive, high-throughput, metabolic analysis by molecular sensors on the membrane surface of mother yeast cells.Nature communications · 2025Article
- Formulation Strategies for Immunomodulatory Natural Products in 3D Tumor Spheroids and Organoids: Current Challenges and Emerging Solutions.Pharmaceutics · 2025Review
- Evo-Inspired Engineering of Radical Phenotypes and Emergent Traits.Integrative and comparative biology · 2025Review
- One-pot cloning and protein expression platform for genetic engineering.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.