Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.

By Atray C Dixit, Oren Parnas, Biyu Li, Jenny Chen, C. Fulco, Livnat Jerby-Arnon, Nemanja Marjanović, Danielle Dionne, Tyler Burks, Raktima Raychndhury and 7 more. 2016

1,739 citations recorded by the source corpus.

SUMMARY Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes – such as transcriptional profiles – at scale. Here, we develop Perturb-seq, combining single cell RNA-seq and CRISPR based perturbations to perform many such assays in a pool. We demonstrate Perturb-seq by analyzing 200,000 cells in immune cells and cell lines, focusing on transcription factors regulating the response of dendritic cells to lipopolysaccharide (LPS). Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions. We posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation. By decomposing many high content measurements into the effects of perturbations, their interactions, and diverse cell metadata, Perturb-seq dramatically increases the scope of pooled genomic assays.

Subfields: Biophysics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Differentiable Causal Discovery from Interventional Data (2020, background): “For instance, in genomics, recent advances in gene editing technologies have given rise to high-throughput methods for interventional gene expression data [4].”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “…P HENO G RAPH (https://github.com/jacoblevine/ PhenoGraph) [19], the single-cell experiment design tool MIMOSCA (https://github.com/asncd/MIMOSCA )[50], UMIS (https://github.com/vals/umis) for handling raw read data [51], the tree-inference tool ECLAIR (https:// github.com/GGiecold/ECLAIR) [52],…”
Spatial components of molecular tissue biology (2022, positive): “Finally, recent advances in pooled perturbation screens with high content readouts, either optically 42,43 or by single-cell profiling 16,44 , open the way to inform and test such causal models, by observing both cell biology and molecular features under a large number of perturbations in cell…”
Permutation-based Causal Inference Algorithms with Interventions (2017, background): “[21], and the second is the single-cell gene expression data generated using perturb-seq in [4].”
Empowering Biomedical Discovery with AI Agents (2024, neutral): “The ability of level 3 agents to drive the discovery of novel biological mechanisms and therapeutic strategies shifts the role of scientists from performing operational tasks to ideation and managing hybrid cell models.”
Predicting cellular responses to complex perturbations in high‐throughput screens (2023, background): “To study such genetic interactions (GIs), recent perturbation scRNA-seq assays allow us to measure the gene expression response of a cell to the perturbation of genes alone or in combination (Dixit et al , 2016; Datlinger et al , 2017).”
Predicting transcriptional outcomes of novel multigene perturbations with GEARS (2023, neutral): “We defined five types of genetic interactions (Supplementary Note 15): synergy, suppression, neomorphism, redundancy and epistasis (Supplementary Note 16).”
Massively-parallel single nucleus RNA-seq with DroNc-seq (2017, neutral): “In addition, we identified signatures of up-regulated genes for each cluster (Supplementary Tables 4,5,8 and 9), which we used to further validate the identity of the cluster by matching these signatures with canonical cell-type marker genes and by testing for enriched pathways.”
Computational principles and challenges in single-cell data integration (2021, methodology): “Similar methods can be applied to test for regulatory effects of genetic perturbations (rather than natural variation) on different molecular readouts, through the use of assays such as Perturb-seq 58,59 ,CROP-seq 60 and TAP-seq 61 .”
A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response (2016, background): “Our success here and the parallel success in understanding dendritic cell activation (Dixit et al., 2016) speak well to the potential of the Perturb-seq approach to become a standard strategy for understanding regulatory interactions in the cell.”