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Joint analysis of multiply perturbed cells improves statistical power and cost efficiency in Perturb-seq screens

ABSTRACT: Perturb-seq measures transcriptomic responses to genetic perturbations at scale, but standard low-MOI designs remain resource-intensive. Conventional designs aim to recover one guide RNA per cell and often discard cells carrying multiple guides during analysis. Here, we characterize how overloading cells with guides affects signal recovery, information loss, and cost reduction. Increasing guide RNA dosage eventually induced cellular stress and suppressed cell-cycle progression. Among multiplets, cells carrying two or three guides recovered perturbation responses better than higher-order guide multiplets. In a 5000-gene Perturb-seq screen across MOI levels, high MOI reduced per-cell costs by 81% while keeping information loss within 1.5-fold of a low-MOI baseline. In existing genome-wide Perturb-seq data, incorporating previously discarded multiplets increased usable cell numbers and improved statistical power. Compared with a singlet-only holdout set, adding multiplets moved signal recovery closer to the theoretical reproducibility limit. Together, these results provide an experimental and computational framework for designing cost-efficient Perturb-seq screens at scale.

ORGANISM(S): Homo sapiens

PROVIDER: GSE337988 | GEO | 2026/07/10

REPOSITORIES: GEO

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Project description:Similar to other droplet-based single cell assays, single nucleus ATAC-seq (snATAC-seq) data harbor multiplets that confound downstream analyses. Detecting multiplets in snATAC-seq data is particularly challenging due to data sparsity and limited dynamic range (0 reads: closed chromatin, 1: open on in one parental chromosome allele, 2: open on in both alleles chromosomes). Yet, these unique data features offer an opportunity to identify multiplets. ATAC-DoubletDetector (https://ucarlab.github.io/ATAC-DoubletDetector/) AMULET (Atac MULtiplet Estimation Tool) exploits these unique features to detect multiplets by studying enumerates the number of regions with >2 uniquely aligned reads across the genome to effectively detect multiplets - an effective alternative to methods based on artificially-generated multiplets. We evaluated the method by generating snATAC-seq data (e.g., state-of-the-art ArchR). For benchmarking we generated snATAC-seq data and generated data fromeasured the efficacy of AMULET inm in two primary human tissues: peripheral human blood mononuclear cells (PBMCs) and pancreatic islet samples. AMULET detects had high multiplets with an estimated precision (estimated via donor-based multiplexing) and high recall (estimated via simulated doublets) compared to alternatives 0.57 precision and achieves 0.85 recall. When and was the most effective when a certain read depth is achieved (a certain read depth per nucleus is achieved samples are sequenced deeply (e.g., median read count per nucleus >20K25K) reads per nucleus in PBMCs), ATAC-DoubletDetector captured 85% of simulated doublets (i.e., recall), significantly outperforming ArchR (24%). For lower read depth, ATAC-DoubletDetector and ArchR produced complementary results. Moreover, ATAC-DoubletDetector was equally effective in identifying homotypic multiplets (i.e., multiplets from the same cell type), which are missed by simulation-based methods. Cell-specific marker peaks enabled accurate (85%) tracing of cellular origins of snATAC-seq multiplets. Accordingly, more abundant cells within a tissue are more likely to form multiplets and the majority of multiplets are homotypic. ATAC-DoubletDetector is a fast and effective multiplet detection/annotation tool for improved single cell epigenomic data analyses across diverse biological systems and conditions.

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Joint analysis of multiply perturbed cells improves statistical power and cost efficiency in Perturb-seq screens

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