Characterizing and mitigating cross-library PCR chimeras in Perturb-seq using Perturb-Audit

Perturb-seq enables high-throughput linkage of CRISPR perturbations to single-cell transcriptomic phenotypes; however, inference quality depends on accurate single-guide RNA (sgRNA) assignment. In 10x Genomics–based single-cell workflows, assignment can be distorted by ambient RNA, overloaded droplets, and amplification artifacts, including cross-library polymerase chain reaction (PCR) chimeras. We demonstrate that standard Cell Ranger processing—with independent correction of gene expression and CRISPR libraries—does not explicitly resolve cross-library molecular collisions, in which a single-cell barcode–unique molecular identifier (CBC–UMI) pair is assigned to discordant features. To address this limitation, we developed Perturb-Audit, a diagnostic and denoising framework that integrates molecule-level collision auditing with statistical background suppression. Across Perturb-seq datasets of T-cell exhaustion, targeted collision removal provides high-specificity cleanup, whereas global denoising with CellBender yields broader improvements in assignment quality and phenotypic separation. Improved assignment fidelity increases detectable perturbation effect sizes and enables the recovery of biologically relevant immune cell signals. Applying this approach, we recapitulated the known Klf2 -deficient phenotype in antiviral CD8+ T-cell Perturb-seq data. Furthermore, we found that suppression of Eomes triggers an exhaustion-biased shift, whereas Tox deficiency promotes effector-like differentiation. Collectively, these findings support an audit-first strategy to improve assignment fidelity and biological interpretability in single-cell CRISPR screens.