scholarly journals Minimized double guide RNA libraries enable scale-limited CRISPR/Cas9 screens

2019 ◽  
Author(s):  
Elin Madli Peets ◽  
Luca Crepaldi ◽  
Yan Zhou ◽  
Felicity Allen ◽  
Rasa Elmentaite ◽  
...  
Keyword(s):  
High Performance ◽  
Genetic Screens ◽  
Screen Design ◽  
Guide Rna ◽  
Genome Wide ◽  
Reagent Cost ◽  
Induced Pluripotent ◽  
Cellular Phenotypes ◽  
Per Gene ◽  
Do So

Genetic screens based on CRISPR/Cas technology are a powerful tool for understanding cellular phenotypes. However, the coverage and replicate requirements result in large experiment sizes, which are limiting when samples are scarce, or the protocols are expensive and laborious. Here, we present an approach to reduce the scale of genome-wide perturbation screens up to fivefold without sacrificing performance. To do so, we deliver two randomly paired gRNAs into each cell, and rely on recent advances in gRNA design, as well as availability of gRNA effect measurements, to reduce the number of gRNAs per gene. We designed a human genome-wide library that has effective size of 30,000 constructs, yet targets each gene with three gRNAs. Our minimized double guide RNA library gives similar results to a standard single gRNA one, but using substantially fewer cells. We demonstrate that genome-wide screens can be optimized in a demanding model of induced pluripotent stem cells, reducing reagent cost 70% per replicate compared to conventional approach, while retaining high performance. The screen design and the reduction in scale it provides will enable functional genomics experiments across many possible combinations of environments and genetic backgrounds, as well as in hard to obtain and culture primary cells.

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

2020 ◽  
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).

scholarly journals Image-based pooled whole genome CRISPR screening for Parkin and TFEB subcellular localization

2020 ◽  
Author(s):  
Gil Kanfer ◽  
Shireen A. Sarraf ◽  
Yaakov Maman ◽  
Heather Baldwin ◽  
Kory R. Johnson ◽  
...  
Keyword(s):  
Neural Network ◽  
Subcellular Localization ◽  
Fluorescent Protein ◽  
Network Models ◽  
Neural Network Models ◽  
Guide Rna ◽  
Genome Wide ◽  
A Genome ◽  
Screening Platform ◽  
Cellular Phenotypes

AbstractGenome-wide CRISPR screens have transformed our ability to systematically interrogate human gene function, but are currently limited to a subset of cellular phenotypes. We report a novel pooled screening approach for a wider range of cellular and subtle subcellular phenotypes. Machine learning and convolutional neural network models are trained on the subcellular phenotype to be queried. Genome-wide screening then utilizes cells stably expressing dCas9 (CRISPRi), photoactivatable fluorescent protein (PA-mCherry), and a lentiviral guide RNA (gRNA) pool. Cells are screened by microscopy and classified by artificial intelligence (AI) algorithms, which precisely identify the genetically altered phenotype. Cells with the phenotype of interest are photoactivated, isolated via flow cytometry, and the gRNAs are identified by sequencing. A proof-of-concept screen accurately identified PINK1 as essential for Parkin recruitment to mitochondria. A genome-wide screen identified factors mediating TFEB relocation from the nucleus to the cytosol upon prolonged starvation. Twenty of the sixty-four hits called by the neural network model were independently validated, revealing new effectors of TFEB subcellular localization. This approach, AI-Photoswitchable Screening (AI-PS) offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.

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

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).

scholarly journals Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes

2021 ◽  
Vol 220 (2) ◽  
Author(s):  
Gil Kanfer ◽  
Shireen A. Sarraf ◽  
Yaakov Maman ◽  
Heather Baldwin ◽  
Eunice Dominguez-Martin ◽  
...  
Keyword(s):  
Neural Network ◽  
Fluorescent Protein ◽  
Network Models ◽  
Neural Network Models ◽  
Guide Rna ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale ◽  
Screening Platform ◽  
Cellular Phenotypes

Genome-wide CRISPR screens have transformed our ability to systematically interrogate human gene function, but are currently limited to a subset of cellular phenotypes. We report a novel pooled screening approach for a wider range of cellular and subtle subcellular phenotypes. Machine learning and convolutional neural network models are trained on the subcellular phenotype to be queried. Genome-wide screening then utilizes cells stably expressing dCas9-KRAB (CRISPRi), photoactivatable fluorescent protein (PA-mCherry), and a lentiviral guide RNA (gRNA) pool. Cells are screened by using microscopy and classified by artificial intelligence (AI) algorithms, which precisely identify the genetically altered phenotype. Cells with the phenotype of interest are photoactivated and isolated via flow cytometry, and the gRNAs are identified by sequencing. A proof-of-concept screen accurately identified PINK1 as essential for Parkin recruitment to mitochondria. A genome-wide screen identified factors mediating TFEB relocation from the nucleus to the cytosol upon prolonged starvation. Twenty-one of the 64 hits called by the neural network model were independently validated, revealing new effectors of TFEB subcellular localization. This approach, AI-photoswitchable screening (AI-PS), offers a novel screening platform capable of classifying a broad range of mammalian subcellular morphologies, an approach largely unattainable with current methodologies at genome-wide scale.

scholarly journals Generation and characterization of iPSC-derived renal proximal tubule-like cells with extended stability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vidya Chandrasekaran ◽  
Giada Carta ◽  
Daniel da Costa Pereira ◽  
Rajinder Gupta ◽  
Cormac Murphy ◽  
...  
Keyword(s):  
Proximal Tubule ◽  
Retinoic Acid Receptor ◽  
Renal Proximal Tubule ◽  
Whole Body ◽  
Proper Function ◽  
Genome Wide ◽  
Glomerular Filtrate ◽  
Proximal Tubular ◽  
Induced Pluripotent

AbstractThe renal proximal tubule is responsible for re-absorption of the majority of the glomerular filtrate and its proper function is necessary for whole-body homeostasis. Aging, certain diseases and chemical-induced toxicity are factors that contribute to proximal tubule injury and chronic kidney disease progression. To better understand these processes, it would be advantageous to generate renal tissues from human induced pluripotent stem cells (iPSC). Here, we report the differentiation and characterization of iPSC lines into proximal tubular-like cells (PTL). The protocol is a step wise exposure of small molecules and growth factors, including the GSK3 inhibitor (CHIR99021), the retinoic acid receptor activator (TTNPB), FGF9 and EGF, to drive iPSC to PTL via cell stages representing characteristics of early stages of renal development. Genome-wide RNA sequencing showed that PTL clustered within a kidney phenotype. PTL expressed proximal tubular-specific markers, including megalin (LRP2), showed a polarized phenotype, and were responsive to parathyroid hormone. PTL could take up albumin and exhibited ABCB1 transport activity. The phenotype was stable for up to 7 days and was maintained after passaging. This protocol will form the basis of an optimized strategy for molecular investigations using iPSC derived PTL.

scholarly journals GWASpro: a high-performance genome-wide association analysis server

2018 ◽  
Vol 35 (14) ◽  
pp. 2512-2514 ◽  
Author(s):  
Bongsong Kim ◽  
Xinbin Dai ◽  
Wenchao Zhang ◽  
Zhaohong Zhuang ◽  
Darlene L Sanchez ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Learning Curves ◽  
Experimental Designs ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome-Wide Epigenetic and Transcriptomic Characterization of Human-Induced Pluripotent Stem Cell–Derived Intestinal Epithelial Organoids

2019 ◽  
Vol 7 (2) ◽  
pp. 285-288 ◽  
Author(s):  
Judith Kraiczy ◽  
Alexander D.B. Ross ◽  
Jessica L. Forbester ◽  
Gordon Dougan ◽  
Ludovic Vallier ◽  
...  
Keyword(s):  
Stem Cell ◽  
Pluripotent Stem Cell ◽  
Induced Pluripotent Stem Cell ◽  
Intestinal Epithelial ◽  
Genome Wide ◽  
Induced Pluripotent

scholarly journals Discovering functional sequences with RELICS, an analysis method for tiling CRISPR screens

2019 ◽  
Author(s):  
Patrick C. Fiaux ◽  
Hsiuyi V. Chen ◽  
Aaron R. Chen ◽  
Poshen B. Chen ◽  
Graham McVicker
Keyword(s):  
New Technology ◽  
Regulatory Sequences ◽  
Analysis Method ◽  
Genome Sequences ◽  
Bayesian Hierarchical ◽  
Guide Rna ◽  
Target Sites ◽  
Cellular Phenotypes ◽  
Genomic Regions ◽  
Crispr Activation

AbstractCRISPR screens are a powerful new technology for the identification of genome sequences that affect cellular phenotypes such as gene expression, survival, and proliferation. By tiling single-guide RNA (sgRNA) target sites across large genomic regions, CRISPR screens have the potential to systematically discovery novel functional sequences, however, a lack of purpose-built analysis tools limits the effectiveness of this approach. Here we describe RELICS, a Bayesian hierarchical model for the discovery of functional sequences from tiling CRISPR screens. RELICS considers the overlapping effects of multiple nearby functional sequences, accounts for the ‘area of effect’ surrounding sgRNA target sites, models overdispersion in sgRNA counts, combines information across multiple pools, and estimates the number of functional sequences supported by the data. In simulations, RELICS outperforms existing methods and provides higher resolution predictions. We apply RELICS to published CRISPR interference and CRISPR activation screens and predict novel regulatory sequences, several of which we experimentally validate. In summary, RELICS is a powerful new analysis method for tiling CRISPR screens that enables the discovery of functional sequences with unprecedented resolution and accuracy.

scholarly journals Numerical analysis of stiffened plates subjected to transverse uniform load through the constructal design method

2022 ◽  
Vol 10 (1) ◽  
pp. 99-108 ◽  
Author(s):  
Vinícius Torres Pinto ◽  
Luiz Alberto Oliveira Rocha ◽  
Elizaldo Domingues dos Santos ◽  
Liércio André Isoldi
Keyword(s):  
High Performance ◽  
Design Method ◽  
Material Constant ◽  
Stiffened Plates ◽  
The Finite Element Method ◽  
Uniform Load ◽  
Constructal Design ◽  
Geometric Configurations ◽  
Out Of Plane ◽  
Do So

When it comes to engineering, high performance is always a desired goal. In this context, regarding stiffened plates, the search for better geometric configurations able to minimize the out-of-plane displacements become interesting. So, this study aimed to analyze several stiffened plates defined by the Constructal Design Method (CDM) and solved through the Finite Element Method (FEM) using the ANSYS® software. After that, these plates are compared among each other through the Exhaustive Search (ES) technique. To do so, a non-stiffened rectangular plate was adopted as reference. Then, a portion of its steel volume was converted into stiffeners through the ϕ parameter, which represents the ratio between the volume of the stiffeners and the total volume of the reference plate. Taking into consideration the value of ϕ = 0.3, 75 different stiffened plates arrangements were proposed: 25 with rectangular stiffeners oriented at 0°; 25 with rectangular stiffeners oriented at 45° and 25 with trapezoidal stiffeners oriented at 0°. Maintaining the total volume of material constant, it was investigated the geometry influence on the maximum deflection of these stiffened plates. The results have shown trapezoidal stiffeners oriented at 0° are more effective to reduce the maximum deflections than rectangular stiffeners also oriented at 0°. It was also observed that rectangular stiffeners oriented at 45° presented the smallest maximum deflections for the majority of the analyzed cases, when compared to the trapezoidal and rectangular stiffeners oriented at 0°.

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