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# Cell-free genome-wide transcriptomics through machine learning optimization

Léa Wagner, An Hoang, Olivier Rue, Olivier Delumeau, Valentin Loux,

Jean-Loup Faulon, Matthieu Jules, Olivier Borkowski

► To cite this version:

Léa Wagner, An Hoang, Olivier Rue, Olivier Delumeau, Valentin Loux, et al. Cell-free genome-wide transcriptomics through machine learning optimization. SEED 2025, Jun 2025, Houston, United States. hal-05155548

HAL Id: hal-05155548

https://hal.inrae.fr/hal-05155548v1

Submitted on 10 Jul 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Cell-free genome-wide transcriptomics through machine learning optimization

Léa WAGNER¹, An HOANG¹, Olivier RUE²,³, Olivier DELUMEAU¹, Valentin LOUX²,³, Jean-Loup FAULON¹, Matthieu JULES¹*, Olivier BORKOWSKI¹*

¹ Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350, Jouy-en-Josas, France

² Université Paris-Saclay, INRAE, MalAGE, 78350 Jouy-en-Josas, France

³ Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, 78350, Jouy-en-Josas, France

*Corresponding authors: matthieu.jules@inrae.fr & olivier.borkowski@inrae.fr

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## Introduction

* Lysate-based cell-free systems (CFS) are valuable platforms for studying the living and elucidating molecular mechanisms (Nirenberg and Matthaei, 1961; Wagner et al., 2023; Giaveri et al., 2024; Kosaka et al., 2025). CFS are simpler than living cells in terms of molecular interactions as they eliminate complex and interfering biological processes such as cell division and membrane-related functions.

* Genome expression in living cells is routinely analyzed using RNA-seq (Zhu et al., 2020); however, low transcript yields in CFS have so far limited the application of this technique in vitro (Fujiwara et al., 2017).

Here, we developed a general methodological pipeline that integrates:

1. active learning loops to maximize transcript abundance for a target RNA polymerase (RNAP) in CFS until RNA yields are sufficient for RNA-seq

2. genome-wide transcriptomic analysis of a DNA template transcribed by this RNAP

As a proof of concept, we applied the pipeline to enhance RNA synthesis by T7 RNAP and performed in vitro transcriptomic profiling of the phage T7 genome.

## 1 Optimization of cell-free buffer by active learning

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## 2 Cell-free transcriptomics

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## Perspectives

### Expand the pipeline to other organisms & dissect genome regulation

Our work establishes a foundation for **cell-free transcriptomics of any genome of interest**, whether natural or synthetic, using its cognate RNAP. This pipeline provides access to transcriptional landscapes of **unculturable bacteria**, which account for ≈97% of bacterial and archaeal species that have not yet been cultivated and validly described (Lewis *et al.*, 2021).

Leveraging the openness of cell-free systems (CFS), we can probe the effects of putative regulators on the transcriptional landscape, providing a **high-throughput in vitro** platform for **bacterial gene regulation studies**. This pipeline provides access to regulatory networks of any organism.

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### Expand the pipeline to proteomics

The active learning workflow enables simultaneous optimization of mRNA and protein yields, providing **access to both transcriptomics and proteomics**. Protein sequencing flow cells, under development by Oxford Nanopore Technologies, could be used like mRNA flow cells.

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