Major efforts using loss-of-function genetic screens to systematically identify genes essential to the proliferation and survival of cancer cells have been reported [1][2][3][4][5][6][7][8][9] . Genes identified by these approaches may represent specific genetic vulnerabilities of cancer cells, suggesting treatment strategies and directing the development of novel therapeutics. The CRISPR-Cas9 genome editing system has proven to be a powerful tool to interrogate gene essentiality in cancer cell lines. Its relative ease of application, high rates of target validation, and increased specificity compared to RNA interference technology make it an ideal instrument for use in high-throughput functional genomic screening 10 .However, we and others have recently observed that measurements of genetic dependency in genome-scale CRISPR-Cas9 loss-of-function screens are influenced by the genomic copy number (CN) of the region targeted by the sgRNA-Cas9 complex [1][2][3][4] . Targeting Cas9 to DNA sequences within regions of high CN gain creates multiple DNA double-strand breaks (DSBs), inducing a gene-independent DNA damage response and a G2 cell-cycle arrest phenotype 2 .This systematic, sequence-independent effect due to DNA cleavage (copy-number effect)confounds the measurement of the consequences of gene deletion on cell viability (geneknockout effect) and is detectable even among low-level CN amplifications and deletions. In particular, this phenomenon hinders interpretation of CRISPR-Cas9 experiments in cancer cell

Cultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors. To address this, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines from 22 cancer types. We uncovered 12 expression programs that are recurrently heterogeneous within many cancer cell lines. These programs are associated with diverse biological processes including cell cycle, senescence, stress and interferon responses, epithelial-mesenchymal transition, and protein maturation and degradation. Notably, most of these recurrent programs of heterogeneity recapitulate those recently observed within human tumors. The similarity to tumors allowed us to prioritize specific cell lines as model systems of cellular heterogeneity. We used two such models

U. (2021).Aneuploidy renders cancer cells vulnerable to mitotic checkpoint inhibition. Nature, 590(7846).

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of singlecell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as posttreatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from shortterm transcriptional responses to treatment.

Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines.

The availability of multiple datasets together comprising hundreds of genome-scale RNAi viability screens across a diverse range of cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated estimation of cell line screen quality parameters and hierarchical Bayesian inference into an analytical framework for analyzing RNAi screens (DEMETER2; https://depmap.org/R2-D2). We applied this model to individual large-scale datasets and show that it substantially improves estimates of gene dependency across a range of performance measures, including identification of goldstandard essential genes as well as agreement with CRISPR-Cas9-based viability screens. This model also allows us to effectively integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

Saccadic eye movements play a central role in primate vision. Yet, relatively little is known about their effects on the neural processing of visual inputs. Here, we examine this question in primary visual cortex (V1) using receptive-field-based models, combined with an experimental design that leaves the retinal stimulus unaffected by saccades. This approach allows us to analyze V1 stimulus processing during saccades with unprecedented detail, revealing robust perisaccadic modulation. In particular, saccades produce biphasic firing rate changes that are composed of divisive gain suppression followed by an additive rate increase. Microsaccades produce similar, though smaller, modulations. We furthermore demonstrate that this modulation is likely inherited from the LGN, and is driven largely by extra-retinal signals. These results establish a foundation for integrating saccades into existing models of visual cortical stimulus processing, and highlight the importance of studying visual neuron function in the context of eye movements.

The responses of sensory neurons can be quite different to repeated presentations of the same stimulus. Here, we demonstrate a direct link between the trial-to-trial variability of cortical neuron responses and network activity that is reflected in local field potentials (LFPs). Spikes and LFPs were recorded with a multielectrode array from the middle temporal (MT) area of the visual cortex of macaques during the presentation of continuous optic flow stimuli. A maximum likelihood-based modeling framework was used to predict single-neuron spiking responses using the stimulus, the LFPs, and the activity of other recorded neurons. MT neuron responses were strongly linked to gamma oscillations (maximum at 40 Hz) as well as to lower-frequency delta oscillations (1-4 Hz), with consistent phase preferences across neurons. The predicted modulation associated with the LFP was largely complementary to that driven by visual stimulation, as well as the activity of other neurons, and accounted for nearly half of the trial-to-trial variability in the spiking responses. Moreover, the LFP model predictions accurately captured the temporal structure of noise correlations between pairs of simultaneously recorded neurons, and explained the variation in correlation magnitudes observed across the population. These results therefore identify signatures of network activity related to the variability of cortical neuron responses, and suggest their central role in sensory cortical function.