Engineering digitizer circuits for chemical and genetic screens in human cells

AbstractCell-based transcriptional reporters are invaluable in high-throughput compound and CRISPR screens for identifying compounds or genes that can impact a pathway of interest. However, many transcriptional reporters have weak activities and transient responses. This can result in overlooking therapeutic targets and compounds that are difficult to detect, necessitating the resource-consuming process of running multiple screens at various timepoints. Here, we present RADAR, a digitizer circuit for amplifying reporter activity and retaining memory of pathway activation. Reporting on the AP-1 pathway, our circuit identifies compounds with known activity against PKC-related pathways and shows an enhanced dynamic range with improved sensitivity compared to a classical reporter in compound screens. In the first genome-wide pooled CRISPR screen for the AP-1 pathway, RADAR identifies canonical genes from the MAPK and PKC pathways, as well as non-canonical regulators. Thus, our scalable system highlights the benefit and versatility of using genetic circuits in large-scale cell-based screening.

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A large-scale resource for tissue-specific CRISPR mutagenesis in Drosophila

eLife ◽

10.7554/elife.53865 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 25

Author(s):

Fillip Port ◽

Claudia Strein ◽

Mona Stricker ◽

Benedikt Rauscher ◽

Florian Heigwer ◽

...

Keyword(s):

Large Scale ◽

Target Genes ◽

High Efficiency ◽

Direct Sequencing ◽

Fine Tuning ◽

Genetic Screens ◽

Efficient Gene ◽

Crispr Screen ◽

High Gene ◽

Multicellular Organisms

Genetic screens are powerful tools for the functional annotation of genomes. In the context of multicellular organisms, interrogation of gene function is greatly facilitated by methods that allow spatial and temporal control of gene abrogation. Here, we describe a large-scale transgenic short guide (sg) RNA library for efficient CRISPR-based disruption of specific target genes in a constitutive or conditional manner. The library consists currently of more than 2600 plasmids and 1700 fly lines with a focus on targeting kinases, phosphatases and transcription factors, each expressing two sgRNAs under control of the Gal4/UAS system. We show that conditional CRISPR mutagenesis is robust across many target genes and can be efficiently employed in various somatic tissues, as well as the germline. In order to prevent artefacts commonly associated with excessive amounts of Cas9 protein, we have developed a series of novel UAS-Cas9 transgenes, which allow fine tuning of Cas9 expression to achieve high gene editing activity without detectable toxicity. Functional assays, as well as direct sequencing of genomic sgRNA target sites, indicates that the vast majority of transgenic sgRNA lines mediate efficient gene disruption. Furthermore, we conducted the so far largest fully transgenic CRISPR screen in any metazoan organism, which further supported the high efficiency and accuracy of our library and revealed many so far uncharacterized genes essential for development.

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Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier

10.1101/2020.05.30.125526 ◽

2020 ◽

Cited By ~ 2

Author(s):

Eiru Kim ◽

Traver Hart

Keyword(s):

Dynamic Range ◽

Quality Data ◽

Loss Of Function ◽

Genetic Screens ◽

Gene Essentiality ◽

Guide Rna ◽

Genome Wide ◽

Improved Model ◽

Target Effects

AbstractIdentifying essential genes in genome-wide loss of function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome wide genetic screens. Here, we introduce an updated version, BAGEL2, which employs an improved model that offers greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes, and a multi-target correction that reduces false positives from off-target CRISPR guide RNA. We also suggest a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower-quality data in substantially different ways. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).

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A large-scale resource for tissue-specific CRISPR mutagenesis in Drosophila

10.1101/636076 ◽

2019 ◽

Cited By ~ 3

Author(s):

Fillip Port ◽

Claudia Strein ◽

Mona Stricker ◽

Benedikt Rauscher ◽

Florian Heigwer ◽

...

Keyword(s):

Large Scale ◽

Target Genes ◽

High Efficiency ◽

Direct Sequencing ◽

Fine Tuning ◽

Genetic Screens ◽

Efficient Gene ◽

Crispr Screen ◽

High Gene ◽

Multicellular Organisms

SUMMARYGenetic screens are powerful tools for the functional annotation of genomes. In the context of multicellular organisms, interrogation of gene function is greatly facilitated by methods that allow spatial and temporal control of gene abrogation. Here, we describe a large-scale transgenic short guide (sg) RNA library for efficient CRISPR-based disruption of specific target genes in a constitutive or conditional manner. The library consists currently of more than 2600 plasmids and 1600 fly lines with a focus on targeting kinases, phosphatases and transcription factors, each expressing two sgRNAs under control of the Gal4/UAS system. We show that conditional CRISPR mutagenesis is robust across many target genes and can be efficiently employed in various somatic tissues, as well as the germline. In order to prevent artefacts commonly associated with excessive amounts of Cas9 protein, we have developed a series of novel UAS-Cas9 transgenes, which allow fine tuning of Cas9 expression to achieve high gene editing activity without detectable toxicity. Functional assays, as well as direct sequencing of genomic sgRNA target sites, indicates that the vast majority of transgenic sgRNA lines mediate efficient gene disruption. Furthermore, we conducted the so far largest fully transgenic CRISPR screen in any metazoan organism, which further supported the high efficiency and accuracy of our library and revealed many so far uncharacterized genes essential for development.

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Efficient strategies for screening large-scale genetic interaction networks

10.1101/159632 ◽

2017 ◽

Cited By ~ 6

Author(s):

Raamesh Deshpande ◽

Justin Nelson ◽

Scott W. Simpkins ◽

Michael Costanzo ◽

Jeff S. Piotrowski ◽

...

Keyword(s):

Large Scale ◽

Optimal Algorithm ◽

Genetic Interaction ◽

Genetic Screens ◽

Chemical Genetic ◽

Genome Wide ◽

Powerful Approach ◽

Interaction Screening ◽

Genetic Interaction Data

Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.To address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

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Up, down, and out: optimized libraries for CRISPRa, CRISPRi, and CRISPR-knockout genetic screens

10.1101/356626 ◽

2018 ◽

Cited By ~ 3

Author(s):

Kendall R Sanson ◽

Ruth E Hanna ◽

Mudra Hegde ◽

Katherine F Donovan ◽

Christine Strand ◽

...

Keyword(s):

Gene Function ◽

Large Scale ◽

Essential Genes ◽

Open Reading Frames ◽

Genetic Screens ◽

Genome Wide ◽

Comparable Performance ◽

Modulation Of Gene Expression ◽

Genome Scale ◽

Level Performance

ABSTRACTAdvances in CRISPR-Cas9 technology have enabled the flexible modulation of gene expression at large scale. In particular, the creation of genome-wide libraries for CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi), and CRISPR activation (CRISPRa) has allowed gene function to be systematically interrogated. Here, we evaluate numerous CRISPRko libraries and show that our recently-described CRISPRko library (Brunello) is more effective than previously published libraries at distinguishing essential and non-essential genes, providing approximately the same perturbation-level performance improvement over GeCKO libraries as GeCKO provided over RNAi. Additionally, we developed genome-wide libraries for CRISPRi (Dolcetto) and CRISPRa (Calabrese). Negative selection screens showed that Dolcetto substantially outperforms existing CRISPRi libraries with fewer sgRNAs per gene and achieves comparable performance to CRISPRko in the detection of gold-standard essential genes. We also conducted positive selection CRISPRa screens and show that Calabrese outperforms the SAM library approach at detecting vemurafenib resistance genes. We further compare CRISPRa to genome-scale libraries of open reading frames (ORFs). Together, these libraries represent a suite of genome-wide tools to efficiently interrogate gene function with multiple modalities.tracr

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Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier

Genome Medicine ◽

10.1186/s13073-020-00809-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Eiru Kim ◽

Traver Hart

Keyword(s):

Dynamic Range ◽

Quality Data ◽

Loss Of Function ◽

Genetic Screens ◽

Gene Essentiality ◽

Guide Rna ◽

Genome Wide ◽

And Performance ◽

Improved Model ◽

Sensitivity Specificity

Abstract Background Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome-wide genetic screens. Results We introduce an updated version, BAGEL2, which employs an improved model that offers a greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes; a multi-target correction that reduces false positives from off-target CRISPR guide RNA; and the implementation of a cross-validation strategy that improves performance ~ 10× over the prior bootstrap resampling approach. We also describe a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower quality data in substantially different ways. Conclusions BAGEL2 substantially improves the sensitivity, specificity, and performance over BAGEL and establishes the new state of the art in the analysis of CRISPR knockout fitness screens. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).

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Applying CRISPR Screen in Diabetes Research

10.2337/figshare.14806587 ◽

2021 ◽

Author(s):

Peng Yi ◽

Noelle Morrow

Keyword(s):

Cell Biology ◽

Cancer Biology ◽

Cell Types ◽

Diabetes Research ◽

Genetic Screens ◽

Multiple Organs ◽

Genome Wide ◽

Forward Genetic Screens ◽

Crispr Screen ◽

Single Cell Type

The CRISPR/Cas9 genome editing system has been one of the greatest scientific discoveries in the last decade. The highly efficient and precise editing ability of this technology is of great therapeutic value and benefits the basic sciences as an advantageous research tool. In recent years, forward genetic screens utilizing CRISPR technology have been widely adopted, with genome-wide or pathway-focused screens leading to important and novel discoveries. CRISPR screens have been used primarily in cancer biology, virology and basic cell biology; but they have rarely been applied to diabetes research. A potential reason for this is that diabetes related research can be more complicated, often involving cross-talk between multiple organs or cell types. Nevertheless, many questions can still be reduced to the study of a single cell type if assays are carefully designed. Here we review the application of CRISPR screen technology and provide perspective on how it can be used in diabetes research.

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Understanding How Genetic Mutations Collaborate with Genomic Instability in Cancer

Cells ◽

10.3390/cells10020342 ◽

2021 ◽

Vol 10 (2) ◽

pp. 342

Author(s):

Laura J. Jilderda ◽

Lin Zhou ◽

Floris Foijer

Keyword(s):

Cancer Cells ◽

Chromosomal Instability ◽

Large Scale ◽

Small Scale ◽

Genetic Screens ◽

Driver Genes ◽

Cancer Driver ◽

The Past ◽

Cancer Models ◽

Genome Wide

Chromosomal instability is the process of mis-segregation for ongoing chromosomes, which leads to cells with an abnormal number of chromosomes, also known as an aneuploid state. Induced aneuploidy is detrimental during development and in primary cells but aneuploidy is also a hallmark of cancer cells. It is therefore believed that premalignant cells need to overcome aneuploidy-imposed stresses to become tumorigenic. Over the past decade, some aneuploidy-tolerating pathways have been identified through small-scale screens, which suggest that aneuploidy tolerance pathways can potentially be therapeutically exploited. However, to better understand the processes that lead to aneuploidy tolerance in cancer cells, large-scale and unbiased genetic screens are needed, both in euploid and aneuploid cancer models. In this review, we describe some of the currently known aneuploidy-tolerating hits, how large-scale genome-wide screens can broaden our knowledge on aneuploidy specific cancer driver genes, and how we can exploit the outcomes of these screens to improve future cancer therapy.

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Minimal genome-wide human CRISPR-Cas9 library

Genome Biology ◽

10.1186/s13059-021-02268-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Emanuel Gonçalves ◽

Mark Thomas ◽

Fiona M. Behan ◽

Gabriele Picco ◽

Clare Pacini ◽

...

Keyword(s):

Large Scale ◽

Dynamic Range ◽

Loss Of Function ◽

Assay Sensitivity ◽

Guide Rna ◽

Minimal Genome ◽

Backward Compatibility ◽

Large Size ◽

Genome Wide ◽

Complex Models

AbstractCRISPR guide RNA libraries have been iteratively improved to provide increasingly efficient reagents, although their large size is a barrier for many applications. We design an optimised minimal genome-wide human CRISPR-Cas9 library (MinLibCas9) by mining existing large-scale gene loss-of-function datasets, resulting in a greater than 42% reduction in size compared to other CRISPR-Cas9 libraries while preserving assay sensitivity and specificity. MinLibCas9 provides backward compatibility with existing datasets, increases the dynamic range of CRISPR-Cas9 screens and extends their application to complex models and assays.

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