Active-learning-guided optimization of cell-free systems for genome-wide transcriptomic profiling reveals progressive layers of regulation.

Authors

Léa Wagner, An Hoang, Olivier Rue, Olivier Delumeau, Valentin Loux, Gabin Derache, Jean-Loup Faulon, Matthieu Jules, Olivier Borkowski

Published in

Nature communications. Jun 27, 2026. Epub Jun 27, 2026.

Abstract

Understanding genome regulation is limited by the complexity of molecular interactions in living cells. Cell-free systems provide a simplified platform for studying gene expression, but low mRNA levels have prevented RNA-seq. To address this, we develop an active learning workflow combining Bayesian optimization with automated high-throughput experimentation to systematically explore over 1.6 million buffer compositions, experimentally testing 653. We identify a "mRNA-optimized" buffer (20-fold increase in mRNA yield) and a "trade-off" buffer (13-fold increase while maintaining protein production). Using direct RNA-seq, we profile the T7 phage transcriptome in cell-free systems and compare it with a purified T7-RNAP transcription system and phage-infected bacteria. This comparative analysis reveals distinct regulatory layers: the T7-RNAP system captures promoter-strength hierarchies but lacks RNA degradation, whereas cell-free systems provide an accurate estimation of in vivo expression and reveal mRNA maturation sites. This work establishes cell-free transcriptomics as a controlled approach to study genome regulation.

PMID:
42364991
Bibliographic data and abstract were imported from PubMed on 28 Jun 2026.

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