Estimation of Cell Cycle States of Human Melanoma Cells with Quantitative Phase Imaging and Deep Learning

Visualization and classification of cell cycle stages in live cells requires the introduction of transient or stably expressing fluorescent markers. This is not feasible for all cell types, and can be time consuming to implement. Labelling of living cells also has the potential to perturb normal cellular function. Here we describe a computational strategy to estimate core cell cycle stages without markers by taking advantage of features extracted from information-rich ptychographic time-lapse movies. We show that a deep-learning approach can estimate the cell cycle trajectories of individual human melanoma cells from short 3-frame (~23 minute) snapshots, and can identify cell cycle arrest induced by chemotherapeutic agents targeting melanoma driver mutations

Target-Specific Precision of CRISPR-Mediated Genome Editing

The CRISPR-Cas9 system has successfully been adapted to edit the genome of various organisms. However, our ability to predict the editing outcome at specific sites is limited. Here, we examined indel profiles at over 1,000 genomic sites in human cells and uncovered general principles guiding CRISPR-mediated DNA editing. We find that precision of DNA editing (i.e., recurrence of a specific indel) varies considerably among sites, with some targets showing one highly preferred indel and others displaying numerous infrequent indels. Editing precision correlates with editing efficiency and a preference for single-nucleotide homologous insertions. Precise targets and editing outcome can be predicted based on simple rules that mainly depend on the fourth nucleotide upstream of the protospacer adjacent motif (PAM). Indel profiles are robust, but they can be influenced by chromatin features. Our findings have important implications for clinical applications of CRISPR technology and reveal general patterns of broken end joining that can provide insights into DNA repair mechanisms

Generation of an arrayed CRISPR-Cas9 library targeting epigenetic regulators: from high-content screens to <i>in vivo</i> assays

<p>The CRISPR-Cas9 system has revolutionized genome engineering, allowing precise modification of DNA in various organisms. The most popular method for conducting CRISPR-based functional screens involves the use of pooled lentiviral libraries in selection screens coupled with next-generation sequencing. Screens employing genome-scale pooled small guide RNA (sgRNA) libraries are demanding, particularly when complex assays are used. Furthermore, pooled libraries are not suitable for microscopy-based high-content screens or for systematic interrogation of protein function. To overcome these limitations and exploit CRISPR-based technologies to comprehensively investigate epigenetic mechanisms, we have generated a focused sgRNA library targeting 450 epigenetic regulators with multiple sgRNAs in human cells. The lentiviral library is available both in an arrayed and pooled format and allows temporally-controlled induction of gene knock-out. Characterization of the library showed high editing activity of most sgRNAs and efficient knock-out at the protein level in polyclonal populations. The sgRNA library can be used for both selection and high-content screens, as well as for targeted investigation of selected proteins without requiring isolation of knock-out clones. Using a variety of functional assays we show that the library is suitable for both <i>in vitro</i> and <i>in vivo</i> applications, representing a unique resource to study epigenetic mechanisms in physiological and pathological conditions.</p