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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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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