Direct reprogramming of immune cells holds promise for immunotherapy but is constrained by limited knowledge of transcription factor (TF) networks. Here, we developed REPROcode, a combinatorial single-cell screening platform to identify TF combinations for immune cell reprogramming. We first validated REPROcode by inducing type-1 conventional dendritic cells (cDC1s) with multiplexed sets of 9, 22, and 42 factors. With cDC1-enriched TFs, REPROcode enabled identification of optimal TF stoichiometry, fidelity enhancers, and regulators of cDC1 states. We then constructed an arrayed lentiviral library of 408 barcoded immune TFs to explore broader reprogramming capacity. Screening 48 TFs enriched in dendritic cell subsets yielded myeloid and lymphoid phenotypes and enabled the construction of a TF hierarchy map to guide immune reprogramming. Finally, we validated REPROcode's discovery power by inducing natural killer (NK)-like cells. This study deepens our understanding of immune transcriptional control and provides a versatile toolbox for engineering immune cells to advance immunotherapy.
Keywords: antigen-presenting cells; barcoding; cellular reprogramming; combinatorial screening; dendritic cells; innate lymphoid cells; machine learning; transcription factor; transcriptional hierarchy.
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