UC San Diego

Transcription factors (TFs) are primary regulators of cell identity, and understanding how their disruption affects cell state is a core objective of functional genomics. Perturb-seq, which combines gene perturbations with single-cell transcriptomic readouts, enables high-throughput measurement of the downstream responses to perturbations. This work reanalyzes two published genome-scale Perturb-seq datasets - one in KOLF2.1J human induced pluripotent stem cells, one in the K562 leukemia cell line - using a single analytical workflow. Building upon these studies, this work interprets the downstream programs each TF knockdown engages, at the level of the whole dataset, individual clusters, and single knockdowns. In both datasets, TF knockdowns reshaped cell states along lineage-associated axes. In KOLF2.1J, disruption of the pluripotency network destabilized the pluripotent cell state and revealed a bias towards a neuroectoderm lineage. In K562, TF knockdown responses highlighted distinct regulators of the erythroid and myeloid programs. Lineage specification emerged as the clearest biological signal above a generic TF knockdown program in both datasets. Cross-dataset comparison further identified regulators whose transcriptional responses depended on the cell state, providing a demonstration of context-dependent regulation across two cell lines with differing lineage potential. Together, these results characterize how TF knockdowns engage and destabilize lineage specification programs. Further, this work demonstrates that published Perturb-seq datasets yield interpretable biology beyond the original analysis when the downstream effects of perturbations are examined directly.