We sought to better understand the distinct transcriptional phenotypes induced by loss of Integrator modules.
Although this study was in progress, another group identified C1orf131 as a structural component of the pre-A1 small subunit processome by cryo-EM, complementing our functional work ( In every case, the affected ribosomal subunit corresponded to the Perturb-seq clustering across two independent sgRNAs. CRISPRi-mediated depletion of nine of the ten candidate factors led to substantial defects in ribosome biogenesis, with the exception of ABCF1, as assessed by the ratio of 28S to 18S rRNA ( Figure 2E). This included genes that had no previous association with ribosome biogenesis ( CCDC86, CINP, SPATA5L1, ZNF236, and C1orf131) as well as genes that had not been associated with functional defects in a particular subunit ( SPOUT1, TMA16, NOPCHAP1, ABCF1, and NEPRO). To test these predictions, we selected ten poorly annotated genes whose perturbation response correlated with subunits and biogenesis factors of either the large or small subunit of the cytosolic ribosome ( Figure S4F). In our dataset, perturbation of many poorly annotated genes led to similar transcriptional responses to genes of known function, naturally predicting a role for these uncharacterized genes. In general, we chose to use conservative, nonparametric statistical tests to detect transcriptional changes rather than making specific assumptions about the underlying distribution of gene expression levels. As Perturb-seq captures single-cell genetic perturbation identities in a pooled format, we can use statistical approaches that treat cells as independent samples. These allowed for internal z-normalization of expression measurements to correct for batch effects resulting that resulted from parallelized scRNA-seq and sequencing ( Data S1 ). Our experimental design included many control cells bearing diverse non-targeting sgRNAs. To contend with this diversity, we created a robust framework capable of detecting transcriptional changes. Significant transcriptional phenotypes can take many forms, ranging from altered occupancy of cell states to focused changes in the expression of a small number of target genes. The scale of our experiment provided a unique opportunity to ask what fraction of genetic perturbations cause a transcriptional phenotype. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.
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We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. 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 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).
Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales.
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