Active-learning-guided optimization of cell-free systems for genome-wide transcriptomic profiling reveals progressive layers of regulation
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.
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BibTeX
@article{wagner2026activelearningguided,
title = {{Active-learning-guided optimization of cell-free systems for genome-wide transcriptomic profiling reveals progressive layers of regulation}},
author = {Léa Wagner and An Hoang and Olivier Rue and Olivier Delumeau and Valentin Loux and Gabin Derache and Jean-Loup Faulon and Matthieu Jules and Olivier Borkowski},
journal = {Nature Communications},
year = {2026},
month = jun,
doi = {10.1038/s41467-026-74559-y},
url = {https://hal.inrae.fr/hal-05802646v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Active-learning-guided optimization of cell-free systems for genome-wide transcriptomic profiling reveals progressive layers of regulation
AU - Léa Wagner
AU - An Hoang
AU - Olivier Rue
AU - Olivier Delumeau
AU - Valentin Loux
AU - Gabin Derache
AU - Jean-Loup Faulon
AU - Matthieu Jules
AU - Olivier Borkowski
PY - 2026
DA - 2026/06/27
JO - Nature Communications
DO - 10.1038/s41467-026-74559-y
UR - https://hal.inrae.fr/hal-05802646v1
AB - 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.
SN - 2041-1723
SN - 2041-1723
ER -