Evaluation and Design of Genome-wide CRISPR/Cas9 Knockout Screens

Hart, T.; Tong, A.; Chan, K.; van Leeuwen, J.; Seetharaman, A.; Aregger, M.; Chandrashekhar, M.; Hustedt, N.; Seth, S.; Noonan, A.; Habsid, A.; Sizova, O.; Nedyalkova, L.; Climie, R.; Lawson, K.; Sartori, M. A.; Alibai, S.; Tieu, D.; Masud, S.; Mero, P.; Weiss, A.; Brown, K. R.; Ušaj, M.; Billmann, M.; Rahman, M.; Costanzo, M.; Myers, C. L.; Andrews, B.; Boone, C.; Durocher, D.; Moffat, J.

doi:10.1101/117341

Home/Archive/bioRxiv

bioRxiv · 10.1101/117341

Evaluation and Design of Genome-wide CRISPR/Cas9 Knockout Screens

View original source ↗Export metadata ↓

Abstract

The adaptation of CRISPR/Cas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs), targeting human protein-coding genes and encoded in viral vectors, have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we re-analyze 17 genome-scale knockout screens in human cell lines from three research groups using three different genome-scale gRNA libraries, using the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm to identify essential genes, to refine and expand our previously defined set of human core essential genes, from 360 to 684 genes. We use this expanded set of reference Core Essential Genes (CEG2), plus empirical data from six CRISPR knockout screens, to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.

Explore related subjects

systems biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Hart, T., Tong, A., Chan, K., van Leeuwen, J., Seetharaman, A., Aregger, M., Chandrashekhar, M., Hustedt, N., Seth, S., Noonan, A., Habsid, A., Sizova, O., Nedyalkova, L., Climie, R., Lawson, K., Sartori, M. A., Alibai, S., Tieu, D., Masud, S., Mero, P., Weiss, A., Brown, K. R., Ušaj, M., Billmann, M., Rahman, M., Costanzo, M., Myers, C. L., Andrews, B., Boone, C., Durocher, D., Moffat, J.. 2017-03-16. Evaluation and Design of Genome-wide CRISPR/Cas9 Knockout Screens. https://doi.org/10.1101/117341

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Uncovering disease progression trajectories across multiple timescales with hybrid ODE-mixture models

Biological systems are capable of evolving across multiple timescales, yet most modeling approaches capture either short-term physiological responses or long-term disease progression. Additionally, methods that combine these time scales either struggle with inter-individual variation, or require large volumes of data to work. We present Latent Class Parameter Trajectory Analysis (LCPTA), a hybrid framework that combines mechanistic ordinary differential equation (ODE) modeling of short-term dynamics with latent class trajectory modeling of long-term parameter evolution. To support this approach, we developed TemporalMixtureModels.jl, a Julia package for univariate and multivariate temporal mixture modeling. We evaluated LCPTA in two synthetic longitudinal studies, covering a simulation of a heterogeneous population developing hypertension, as well as a synthetic longitudinal study of 75 virtual individuals followed over 30 years, representing muscle insulin resistance, liver insulin resistance, and healthy trajectories. We compared LCPTA with a conditional neural ODE and a conditional neural network for reconstruction of long-term parameter trajectories and subgroup separation. In both studies LCPTA recovered relevant subgroup structure and trajectory patterns. In the cardiovascular study, the neural approaches were able to distinguish inter-individual differences, but in the insulin sensitivity study, neural approaches failed to represent parameter trajectories faithfully, primarily separating individuals along a single dominant parameter and showing weaker multivariate trajectory reconstruction. In additional experiments with sparse and irregularly sampled unseen individuals, LCPTA maintained strong classification performance. We show that LCPTA is an efficient and interpretable strategy for multiscale modeling of heterogeneous metabolic disease progression, with potential utility for data-efficient digital twin applications.

systems biology↗

bioRxiv2026

The Global Phosphorylation Landscape of Acute Heat Stress in Reef-Building Corals

Warming oceans disrupt the coral-algal symbiosis that sustains reefs, yet the signaling mechanisms initiating bleaching remain poorly understood. Integrating global phosphoproteomics across three reef-building corals with protein structure modeling revealed conservation between coral and human kinases, enabling inference of coral kinase activities and uncovering a shared signaling program induced by acute heat stress. Heat rapidly activated ERK, JNK, and p38 MAP kinases, suppressed cell cycle signaling, and remodeled pathways controlling chromatin, RNA processing, and metabolism. Most strikingly, thermal stress rewired immune receptor signaling networks associated with non-self recognition, suggesting that heat shifts the coral host from tolerance to immune recognition of its algal symbionts. These findings define conserved signaling mechanisms that precede bleaching, revealing actionable molecular targets for enhancing coral resilience.

systems biology↗

bioRxiv2026

Probing bacterial fitness in virtual chemostats

The growth rate of a bacterial population is a fundamental quantity in microbiology, but hard to predict from mechanistic single-cell models. Simulating exponentially growing cell populations is computationally infeasible, and single-lineage estimates are biased as they do not account for phenotypic selection between cells. We present computational algorithms to efficiently estimate growth rates with arbitrary accuracy by combining ideas from branching processes and particle filtering. These can be used with general agent-based cell models at a cost quadratic in the desired accuracy, bypassing the exponential complexity of naive approaches. Applying these methods to single-cell models of Escherichia coli, we study how single-cell heterogeneity in antibiotic responses shapes population dynamics, and how bacterial locomotion and chemotaxis in fluctuating environments affect their long-term growth.

systems biology↗