Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
By J. Replogle, R. Saunders, Angela N. Pogson, Jeffrey A. Hussmann, A. LeNail, Alina Guna, Lauren G. Mascibroda, Eric J. Wagner, K. Adelman, G. Lithwick-Yanai and 7 more. bioRxiv, 2021
669 citations recorded by the source corpus.
A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (pooled CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells and present a framework to power biological discovery with the resulting genotype-phenotype map. We use transcriptional phenotypes to predict the function of poorly-characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena – from RNA processing to differentiation. We leverage this ability to systematically identify the genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene function and cellular behavior.
Subfields: Molecular Biology, Plant Science
View Paper: https://www.semanticscholar.org/paper/064ced50b61d6071f1ecaaea7e49d6cca208da41
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Kexin Huang
- Yusuf H. Roohani
- Jure Leskovec
- William L. Hamilton
- Stephan Günnemann
- Marinka Zitnik
- Petar Velickovic
- Aleksandar Bojchevski
Citations in the corpus
- Predicting transcriptional outcomes of novel multigene perturbations with GEARS (2023, positive): “Moreover, the precision–recall curves for all observed https://doi.org/10.1038/s41587-023-01905-6 genetic interaction subtypes exhibited a higher area under the curve than other methods (Supplementary Fig.”
- Methods and applications for single-cell and spatial multi-omics (2023, background): “This strategy revealed the effect of inactivation of each expressed gene on the expression of all other genes, and thus represents a valuable resource for the in silico modelling of genetic perturbations 94 .”