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Atlas of Computational Cell Reprogramming

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C0 · Target feature discovery T

REPROcode

Kurochkin I, Altman AR, Caiado I, Pértiga-Cabral D, Halitzki E, Minaeva M, Zimmermannová O, Henriques-Oliveira L, Klein D, Nair M, Oliveira D, Cajal LR, Knittel R, Feick C, Ringnér M, Martin M, Cirovic B, Pires CF, Rosa FF, Sitnicka E, Theis FJ, Pereira CF

2026 · Cell systems

Combinatorial barcoded TF screening in HEFs with 3′ scRNA-seq readout to discover immune reprogramming cocktails.

Abstract

From the original paper, Cell systems · PubMed

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.

Summary

Level-0 experimental perturbation-screen discovery: lentiviral barcoded TF pools in HEFs, FACS enrichment, and 3′ scRNA-seq with barcode capture rank combinatorial cocktails (PIB, TENE, and broader immune modules) via post-hoc demultiplexing, CellTypist/scTab annotation, and decision-tree/stepwise models, screening platform (not GRN dynamics); no forward inverse-design simulator.

Why this class

Level 0 because the method discovers candidate TF combinations from pooled functional screen outcomes rather than computing a post-intervention state P^S,u\hat P_{S,u} or inverting a forward model. Representation family is signature / state-matching (atlas projection of reprogrammed cells). Cited in §3.1 of the review as a borderline experimental platform.

Classification

Class
C0 · Target feature discovery
Representation
Signature / state-matching
Modalities
T
Intervention
Transcription factors
Framework
Information-theoretic

Software

Reproducibility
4/4
FAIR4RS
3/5

Last audited 2026-06-01

Citation

Kurochkin I et al. (2026). A combinatorial transcription factor screening platform for immune cell reprogramming., Cell systems.

DOI: 10.1016/j.cels.2025.101457

PMID: 41539305

BibTeX
@article{reprocode2026,
  title  = {A combinatorial transcription factor screening platform for immune cell reprogramming.},
  author = {Kurochkin I et al.},
  year   = {2026},
  journal = {Cell systems},
  pmid = {41539305},
  doi  = {10.1016/j.cels.2025.101457}
}

Validation datasets

Datasets used to validate the method's predictions in the original paper. Solid borders are linked to the source archive; dashed borders denote identifiers without a canonical URL on record.