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bioRxiv · 10.1101/2025.06.04.657821

Cell-free genome-wide transcriptomics through machine learning optimization

Wagner, L.·HOANG, A.·RUE, O.·Delumeau, O.·Loux, V.·Faulon, J.-L.·Jules, M.·Borkowski, O.
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Abstract

1Despite advances in transcriptomics, understanding of genome regulation remains limited by the complex interactions within living cells. To address this, we performed cell-free transcriptomics by developing a platform using an active learning workflow to explore over 1,000,000 buffer conditions. This enabled us to identify a buffer that increased mRNA yield by 20-fold, enabling cell-free transcriptomics. By employing increasingly complex conditions, our approach untangles the regulatory layers controlling genome expression.

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Wagner, L., HOANG, A., RUE, O., Delumeau, O., Loux, V., Faulon, J.-L., Jules, M., Borkowski, O.. 2025-06-04. Cell-free genome-wide transcriptomics through machine learning optimization. https://doi.org/10.1101/2025.06.04.657821

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