SUMMARY Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ~100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses.

A transcribed, multi-channel, and continuously evolving molecular recorder 77 To achieve our goal of a tunable, high information content molecular recorder, we 78 utilized Cas9 to generate insertions or deletions (indels) upon repair of double-stranded breaks, 79 which are inherited in the next generation of cells 11-16. We record within a 205 base pair, synthetic DNA "target site" containing three "cut sites" and a static 8 base pair "integration barcode" (intBC), which are delivered in multiple copies via piggyBac transposition (Fig. 1a, b). We embedded this sequence into the 3'UTR of a constitutively transcribed fluorescent protein to enable profiling from the transcriptome. A second cassette encodes three independently transcribed and complementary guide RNAs to permit recording of multiple, distinct signals (Fig. 1a, b) 18. Our system is capable of high information storage due to the diversity of heritable repair outcomes, and the large number of targeted sites, which can be distinguished by the intBC (Fig. 1c). DNA repair generates hundreds of unique indels, and the distribution for each cut site is different and nonuniform: some produce highly biased outcomes while others create a diverse series (Fig. 1c, Extended Data Fig. 1) 19-21. To identify sequences that can tune the mutation rate of our recorder for timescales that are not pre-defined, and may extend from days to months, we screened several guide RNA series containing mismatches to their targets 22 by monitoring their activity on a GFP reporter over a 20-day timecourse and selected those that demonstrated a broad dynamic range (Fig. 1d). Slower cutting rates may improve viability in vivo, as frequent Cas9mediated double-strand breaks can cause cellular toxicity 23,24. To demonstrate information recovery from single cell transcriptomes, we stably transduced K562 cells with our technology and generated a primary, cell-barcoded cDNA pool via the 10x Genomics platform, allowing us * * *

SummaryPhotoreceptor proteins enable organisms to sense and respond to light. The newly discovered CarH-type photoreceptors use a vitamin B12 derivative, adenosylcobalamin, as the light-sensing chromophore to mediate light-dependent gene regulation. Here, we present crystal structures of Thermus thermophilus CarH in all three relevant states: in the dark, both free and bound to operator DNA, and after light exposure. These structures provide a visualization of how adenosylcobalamin mediates CarH tetramer formation in the dark, how this tetramer binds to the promoter −35 element to repress transcription, and how light exposure leads to a large-scale conformational change that activates transcription. In addition to the remarkable functional repurposing of adenosylcobalamin from an enzyme cofactor to a light sensor, we find that nature also repurposed two independent protein modules in assembling CarH. These results expand the biological role of vitamin B12 and provide fundamental insight into a new mode of light-dependent gene regulation.

How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

Summary Chemical libraries paired with phenotypic screens can now readily identify compounds with therapeutic potential. A central limitation to exploiting these compounds, however, has been in identifying their relevant cellular targets. Here, we present a two-tiered CRISPR-mediated chemical-genetic strategy for target identification: combined genome-wide knockdown and overexpression screening as well as focused, comparative chemical-genetic profiling. Application of these strategies to rigosertib, a drug in phase III clinical trials for high-risk myelodysplastic syndrome whose molecular target had remained controversial, pointed singularly to microtubules as rigosertib’s target. We showed that rigosertib indeed directly binds to and destabilizes microtubules using cell biological, in vitro, and structural approaches. Finally, expression of tubulin with a structure-guided mutation in the rigosertib-binding pocket conferred resistance to rigosertib, establishing that rigosertib kills cancer cells by destabilizing microtubules. These results demonstrate the power of our chemical-genetic screening strategies for pinpointing the physiologically relevant targets of chemical agents.

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Fungal indole prenyltransferases participate in a multitude of biosynthetic pathways. Their ability to prenylate diverse substrates has attracted interest for potential use in chemoenzymatic synthesis. The fungal indole prenyltransferase FtmPT1 catalyzes the prenylation of brevianamide F in the biosynthesis of fumitremorgin-type alkaloids, which show diverse pharmacological activities and are promising candidates for the development of antitumor agents. Here, we report crystal structures of unliganded Aspergillus fumigatus FtmPT1 as well as of a ternary complex of FtmPT1 bound to brevianamide F and an analogue of its isoprenoid substrate dimethylallyl diphosphate. FtmPT1 assumes a rare α/β-barrel fold, consisting of 10 circularly arranged β-strands surrounded by α-helices. Catalysis is performed in a hydrophobic reaction chamber at the center of the barrel. In combination with mutagenesis experiments, our analysis of the liganded and unliganded structures provides insight into the mechanism of catalysis and the determinants of regiospecificity. Sequence conservation of key features indicates that all fungal indole prenyltransferases possess similar active site architectures. However, while the dimethylallyl diphosphate binding site is strictly conserved in these enzymes, subtle changes in the reaction chamber likely allow for the accommodation of diverse aromatic substrates for prenylation. In support of this concept, we were able to redirect the regioselectivity of FtmPT1 by a single mutation of glycine 115 to threonine. This finding provides support for a potential use of fungal indole prenyltransferases as modifiable bioreactors that can be engineered to catalyze highly specific prenyl transfer reactions.

A lack of tools to precisely control gene expression has limited our ability to evaluate relationships between expression levels and phenotypes. Here, we describe an approach to titrate expression of human genes using CRISPR interference and series of single guide RNAs (sgRNAs) with systematically modulated activities. We used large-scale measurements across multiple cell models to characterize activities of sgRNAs containing mismatches to their target sites and derived rules governing mismatched sgRNA activity using deep learning. These rules enabled us to synthesize a compact sgRNA library to titrate expression of ~2,400 genes essential for robust cell growth and to construct an in silico sgRNA library spanning the human genome. Staging cells along a continuum of gene expression levels combined with single-cell RNA-seq readout revealed sharp transitions in cellular behaviors at gene-specific expression thresholds. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.