GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE305nnn/GSE305128/primaryOK200TranscriptomicsHomo sapiensExpression profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305128GEOGSEfalsePreciCE: Precision engineering of cell fates via data-driven multi-gene control of transcriptional networksThe 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 technology 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.2026/04/01GSE305128GSM9162575GSM9162576GSM916257728038305128Homo sapiens