Reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 fitness screens

The identification of essential cellular genes is critical for validating drug targets

Accurately identifying biological targets is crucial for advancing treatment options. Essential genes, vital for cell or organism survival, hold promise as potential drug targets in disease treatment. Although many studies have sought to identify essential genes as therapeutic targets in medicine and bioinformatics, systematic reviews on their relationship with drug targets are relatively rare. This work presents a comprehensive analysis to aid in identifying essential genes as potential targets for drug discovery, encompassing their relevance, identification methods, successful case studies, and challenges. This work will facilitate the identification of essential genes as therapeutic targets, thereby boosting new drug development.

Benchmark Software and Data for Evaluating CRISPR-Cas9 Experimental Pipelines Through the Assessment of a Calibration Screen

Genome-wide genetic screens using CRISPR-guide RNA libraries are widely performed in mammalian cells to functionally characterize individual genes and for the discovery of new anticancer therapeutic targets. As the effectiveness of such powerful and precise tools for cancer pharmacogenomics is emerging, tools and methods for their quality assessment are becoming increasingly necessary. Here, we provide an R package and a high-quality reference data set for the assessment of novel experimental pipelines through which a single calibration experiment has been executed: a screen of the HT-29 human colorectal cancer cell line with a commercially available genome-wide library of single-guide RNAs. This package and data allow experimental researchers to benchmark their screens and produce a quality-control report, encompassing several quality and validation metrics. The R code used for processing the reference data set, for its quality assessment, as well as to evaluate the quality of a user-provided screen, and to reproduce the figures presented in this article is available at https://github.com/DepMap-Analytics/HT29benchmark. The reference data is publicly available on FigShare.

Pooled Genome-Scale CRISPR Screens in Single Cells

Assigning functions to genes and learning how to control their expression are part of the foundation of cell biology and therapeutic development. An efficient and unbiased method to accomplish this is genetic screening, which historically required laborious clone generation and phenotyping and is still limited by scale today. The rapid technological progress on modulating gene function with CRISPR-Cas and measuring it in individual cells has now relaxed the major experimental constraints and enabled pooled screening with complex readouts from single cells. Here, we review the principles and practical considerations for pooled single-cell CRISPR screening. We discuss perturbation strategies, experimental model systems, matching the perturbation to the individual cells, reading out cell phenotypes, and data analysis. Our focus is on single-cell RNA sequencing and cell sorting-based readouts, including image-enabled cell sorting. We expect this transformative approach to fuel biomedical research for the next several decades.