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bioRxiv · 10.1101/2024.11.04.621938

PreciCE: Precision engineering of cell fates via data-driven multi-gene control of transcriptional networks

Magnusson, J. P.·Roohani, Y.·Stauber, D.·Situ, Y.·Teba, P. R. d. C.·Sandberg, R.·Leskovec, J.·Qi, L. S.
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Abstract

The directed differentiation of stem cells into specific cell types is critical for regenerative medicine and cell-based applications. However, current methods for cell fate control are inefficient, imprecise, and rely on laborious trial-and-error. To address these limitations, we present a method for data-driven multi-gene modulation of transcriptional networks. We develop bidirectional CRISPR-based tools based on dCas12a, Cas13d, and dCas9 for simultaneously activating and repressing many genes. Due to the vast combinatorial complexity of multi-gene regulation, we introduce a machine learning-based computational algorithm that uses single-cell RNA sequencing data to predict multi-gene perturbation sets for converting a starting cell type into a desired target cell type. By combining these technologies, we establish a unified workflow for data-driven cell fate engineering and demonstrate its efficacy in controlling early stem cell differentiation while suppressing alternative lineages through logic-based cell fate operations. This approach represents a significant advancement in the use of synthetic biology to engineer cell identity.

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Magnusson, J. P., Roohani, Y., Stauber, D., Situ, Y., Teba, P. R. d. C., Sandberg, R., Leskovec, J., Qi, L. S.. 2024-11-04. PreciCE: Precision engineering of cell fates via data-driven multi-gene control of transcriptional networks. https://doi.org/10.1101/2024.11.04.621938

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