A genome-scale CRISPR interference guide library enables comprehensive phenotypic profiling in yeast

Abstract

Background: CRISPR/Cas9-mediated transcriptional interference (CRISPRi) enables programmable gene knock-down, yielding loss-of-function phenotypes for nearly any gene. Effective, inducible CRISPRi has been demonstrated in budding yeast, and genome-scale guide libraries enable systematic, genome-wide genetic analysis.

Results: We present a comprehensive yeast CRISPRi library, based on empirical design rules, containing 10 distinct guides for most genes. Competitive growth after pooled transformation revealed strong fitness defects for most essential genes, verifying that the library provides comprehensive genome coverage. We used the relative growth defects caused by different guides targeting essential genes to further refine yeast CRISPRi design rules. In order to obtain more accurate and robust guide abundance measurements in pooled screens, we link guides with random nucleotide barcodes and carry out linear amplification by in vitro transcription.

Conclusions: Taken together, we demonstrate a broadly useful platform for comprehensive, high-precision CRISPRi screening in yeast.

Keywords: Budding yeast; CRISPR interference; Pooled screening.

Fig. 1

Design of a genome-wide yeast CRISPRi library. a Candidate guide RNA sites in promoter regions were identified and scored to prioritize target sites that were unique in the genome, specific to one promoter, and located in accessible chromatin. b Promoter regions targeted by guide RNAs. When transcription start sites were known, we selected one guide each from three separate regions around the transcription start site, along with seven others in the overall promoter. When transcription start sites were not known, we targeted a wider area upstream of the start of the coding sequence. c Cumulative distribution of genes targeted by up to ten distinct guides. Unambiguous guides do not fall within the potentially active region for any other gene. Assigned guides have a single likely target based on empirical measurements of guide activity and location. d Schematic of an ambiguous guide at a divergent promoter

Fig. 2

Linear amplification by in vitro transcription improves precision of barcode abundance measurements. a, b Schematics of barcode library generation by a in vitro transcription followed by RT-PCR and b direct PCR amplification. c, d Barcode read counts in libraries prepared from replicate DNA samples by c IVT-RT-PCR and d direct PCR. e Dispersion between replicate measurements as a function of read count

Fig. 3

Construction of a barcoded library for inducible guide RNA expression. a Schematic of the guide RNA expression library. The RPR1 promoter is regulated by two tetO operator sites. In the presence of tetracycline, this promoter is de-repressed and drives expression of a variable guide RNA sequence in a constant sgRNA scaffold. A random nucleotide barcode with an adjacent T7 RNA polymerase promoter is embedded elsewhere in the plasmid. b Schematic of the process for generating the guide RNA library. Guides are cloned first, and then barcodes are added in a transformation with controlled diversity. c Distribution of barcode-to-guide assignment results, illustrating the high frequency of errors in cloned guide RNAs. d Cumulative distribution of the number of barcodes assigned to each guide RNA

Fig. 4

Pooled competitive growth of a diverse guide RNA library. a Schematic of the competitive growth experiment. b Cells expressing the dCas9-Mxi effector protein are transformed with guide RNA expression plasmids and selected under non-inducing conditions. c Upon guide induction, dCas9-Mxi binds target gene promoters and reduces transcription. d, e Replicate competitive growth experiments. Dilution rate corresponds to growth rate after cultures reach the target cell density. Timepoints for guide RNA induction and initial sampling is shown, along with subsequent sampling timepoints

Fig. 5

Inferring fitness effects from guide RNA abundance changes. a, b Consistent rate of exponential decay in abundance for guides targeting a SUI3, which encodes eIF2β, and b STV1 during pooled, competitive growth. Two distinct barcodes are shown for each guide in two replicate cultures. c As in (a) and (b), showing the constant or slightly increasing abundance for three distinct barcodes linked to no guide RNA. d Barcode abundance changes across replicate cultures. Fitness estimates of associated guide RNAs are shown as well. e Enrichment of strong negative fitness effects in guides targeting essential genes. Guide-level fitness estimates are shown for all unambiguous promoters, as well as classification according to essentiality. Barcode-level analysis is shown for all barcodes linked to non-targeting guides. f Most essential genes have at least one strongly deleterious guide. The most negative fitness effect across all guides is shown for genes with unambiguous promoters. The cumulative distribution of barcode fitness effects is shown for non-targeting barcodes

Fig. 6

Accurate predictions of guide activity. a Highly active guides against essential genes bind ~ 50 bp upstream of the transcription start site. The fitness effect produced by guides targeting essential genes serves as a proxy for their activity. Individual guides targeting non-divergent essential genes with at least two independent barcodes are plotted according to their position, fitness effect, and absolute chromatin occupancy as determined by DNA methylation accessibility, along with a local regression of fitness effect against position. b Guide activity varies according to the guide strand, as seen in local regressions for guides on the same (Fwd) or opposite (Rev) strand as the target gene. c Receiver operating characteristic for logistic regression models of guide activity. The full model includes methylation-based accessibility data (ODM-Seq) and guide sequence along with the position of the guide relative to the transcription start site. d Guide score predicts fitness effects in a held-out test set of guides targeting essential genes