scholarly journals Applying CRISPR Screen in Diabetes Research

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.

scholarly journals Applying CRISPR Screen in Diabetes Research

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.

scholarly journals A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells

2019 ◽  
Author(s):  
Charlotte R. Feddersen ◽  
Lexy S. Wadsworth ◽  
Eliot Y. Zhu ◽  
Hayley R. Vaughn ◽  
Andrew P. Voigt ◽  
...  
Keyword(s):  
Cultured Cells ◽  
Sequence Data ◽  
Sleeping Beauty ◽  
Transposon Mutagenesis ◽  
Loss Of Function ◽  
Genetic Screens ◽  
Sleeping Beauty Transposon ◽  
Genome Wide ◽  
Forward Genetic Screens ◽  
The Impact

AbstractThe introduction of genome-wide shRNA and CRISPR libraries has facilitated cell-based screens to identify loss-of-function mutations associated with a phenotype of interest. Approaches to perform analogous gain-of-function screens are less common, although some reports have utilized arrayed viral expression libraries or the CRISPR activation system. However, a variety of technical and logistical challenges make these approaches difficult for many labs to execute. In addition, genome-wide shRNA or CRISPR libraries typically contain of hundreds of thousands of individual engineered elements, and the associated complexity creates issues with replication and reproducibility for these methods. Here we describe a simple, reproducible approach using the Sleeping Beauty transposon system to perform phenotypic cell-based genetic screens. This approach employs only three plasmids to perform unbiased, whole-genome transposon mutagenesis. We also describe a ligation-mediated PCR method that can be used in conjunction with the included software tools to map raw sequence data, identify candidate genes associated with phenotypes of interest, and predict the impact of recurrent transposon insertions on candidate gene function. Finally, we demonstrate the high reproducibility of our approach by having three individuals perform independent replicates of a mutagenesis screen to identify drivers of vemurafenib resistance in cultured melanoma cells. Collectively, our work establishes a facile, adaptable method that can be performed by labs of any size to perform robust, genome-wide screens to identify genes that influence phenotypes of interest.

scholarly journals Genome-wide CRISPR screen for PARKIN regulators reveals transcriptional repression as a determinant of mitophagy

2017 ◽  
Vol 115 (2) ◽  
pp. E180-E189 ◽  
Author(s):  
Christoph Potting ◽  
Christophe Crochemore ◽  
Francesca Moretti ◽  
Florian Nigsch ◽  
Isabel Schmidt ◽  
...  
Keyword(s):  
Transcriptional Repression ◽  
Critical Role ◽  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Physiological Regulation ◽  
Genome Wide ◽  
Crispr Screen ◽  
Multiple Cell ◽  
Induced Pluripotent ◽  
The Impact

PARKIN, an E3 ligase mutated in familial Parkinson’s disease, promotes mitophagy by ubiquitinating mitochondrial proteins for efficient engagement of the autophagy machinery. Specifically, PARKIN-synthesized ubiquitin chains represent targets for the PINK1 kinase generating phosphoS65-ubiquitin (pUb), which constitutes the mitophagy signal. Physiological regulation of PARKIN abundance, however, and the impact on pUb accumulation are poorly understood. Using cells designed to discover physiological regulators of PARKIN abundance, we performed a pooled genome-wide CRISPR/Cas9 knockout screen. Testing identified genes individually resulted in a list of 53 positive and negative regulators. A transcriptional repressor network including THAP11 was identified and negatively regulates endogenous PARKIN abundance. RNAseq analysis revealed the PARKIN-encoding locus as a prime THAP11 target, and THAP11 CRISPR knockout in multiple cell types enhanced pUb accumulation. Thus, our work demonstrates the critical role of PARKIN abundance, identifies regulating genes, and reveals a link between transcriptional repression and mitophagy, which is also apparent in human induced pluripotent stem cell-derived neurons, a disease-relevant cell type.

scholarly journals Engineering digitizer circuits for chemical and genetic screens in human cells

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicole M. Wong ◽  
Elizabeth Frias ◽  
Frederic D. Sigoillot ◽  
Justin H. Letendre ◽  
Marc Hild ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamic Range ◽  
Genetic Screens ◽  
Genetic Circuits ◽  
Transient Responses ◽  
Reporter Activity ◽  
Genome Wide ◽  
Pathway Activation ◽  
Crispr Screen ◽  
Or Genes

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.

scholarly journals The mini player with diverse functions: extracellular vesicles in cell biology, disease, and therapeutics

2021 ◽  
Author(s):  
Abhimanyu Thakur ◽  
Xiaoshan Ke ◽  
Ya-Wen Chen ◽  
Pedram Motallebnejad ◽  
Kui Zhang ◽  
...  
Keyword(s):  
Stem Cell ◽  
Extracellular Vesicles ◽  
Cancer Progression ◽  
Cell Biology ◽  
Cancer Biology ◽  
Cell Types ◽  
Research Progress ◽  
Drug Delivery Vehicles ◽  
Significant Research

AbstractExtracellular vesicles (EVs) are tiny biological nanovesicles ranging from approximately 30–1000 nm in diameter that are released into the extracellular matrix of most cell types and in biofluids. The classification of EVs includes exosomes, microvesicles, and apoptotic bodies, dependent on various factors such as size, markers, and biogenesis pathways. The transition of EV relevance from that of being assumed as a trash bag to be a key player in critical physiological and pathological conditions has been revolutionary in many ways. EVs have been recently revealed to play a crucial role in stem cell biology and cancer progression via intercellular communication, contributing to organ development and the progression of cancer. This review focuses on the significant research progress made so far in the role of the crosstalk between EVs and stem cells and their niche, and cellular communication among different germ layers in developmental biology. In addition, it discusses the role of EVs in cancer progression and their application as therapeutic agents or drug delivery vehicles. All such discoveries have been facilitated by tremendous technological advancements in EV-associated research, especially the microfluidics systems. Their pros and cons in the context of characterization of EVs are also extensively discussed in this review. This review also deliberates the role of EVs in normal cell processes and disease conditions, and their application as a diagnostic and therapeutic tool. Finally, we propose future perspectives for EV-related research in stem cell and cancer biology.

scholarly journals Conserved epigenetic regulatory logic infers genes governing cell identity

2019 ◽  
Author(s):  
Woo Jun Shim ◽  
Enakshi Sinniah ◽  
Jun Xu ◽  
Burcu Vitrinel ◽  
Michael Alexanian ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Cell Biology ◽  
Peer Review Process ◽  
Cell Types ◽  
Computational Method ◽  
Cell Identity ◽  
Genome Wide ◽  
Fundamental Goal ◽  
A Genome ◽  
Wide Range

SUMMARYDetermining genes orchestrating cell differentiation in development and disease remains a fundamental goal of cell biology. This study establishes a genome-wide metric based on the gene-repressive tri-methylation of histone 3 lysine 27 (H3K27me3) across hundreds of diverse cell types to identify genetic regulators of cell differentiation. We introduce a computational method, TRIAGE, that uses discordance between gene-repressive tendency and expression to identify genetic drivers of cell identity. We apply TRIAGE to millions of genome-wide single-cell transcriptomes, diverse omics platforms, and eukaryotic cells and tissue types. Using a wide range of data, we validate TRIAGE’s performance for identifying cell-type specific regulatory factors across diverse species including human, mouse, boar, bird, fish, and tunicate. Using CRISPR gene editing, we use TRIAGE to experimentally validate RNF220 as a regulator of Ciona cardiopharyngeal development and SIX3 as required for differentiation of endoderm in human pluripotent stem cells. A record of this paper’s Transparent Peer Review process is included in the Supplemental Information.

scholarly journals Choosing an appropriate modelling framework for analysing multispecies co-culture cell biology experiments

2014 ◽  
Author(s):  
Deborah C Markham ◽  
Matthew J Simpson ◽  
Ruth E Baker
Keyword(s):  
Spatial Statistics ◽  
Cell Biology ◽  
Cell Types ◽  
Culture Cell ◽  
Parameter Estimates ◽  
Culture Systems ◽  
Modelling Framework ◽  
Direct Model ◽  
Single Cell Type

In vitro cell biology assays play a crucial role in informing our understanding of the migratory, proliferative and invasive properties of many cell types in different biological contexts. While mono-culture assays involve the study of a population of cells composed of a single cell type, co-culture assays study a population of cells composed of multiple cell types (or subpopulations of cells). Such co-culture assays can provide more realistic insights into many biological processes including tissue repair, tissue regeneration and malignant spreading. Typically, system parameters, such as motility and proliferation rates, are estimated by calibrating a mathematical or computational model to the observed experimental data. However, parameter estimates can be highly sensitive to the choice of model and modelling framework. This observation motivates us to consider the fundamental question of how we can best choose a model to facilitate accurate parameter estimation for a particular assay. In this work we describe three mathematical models of mono-culture and co-culture assays that include different levels of spatial detail. We study various spatial summary statistics to explore if they can be used to distinguish between the suitability of each model over a range of parameter space. Our results for mono-culture experiments are promising, in that we suggest two spatial statistics that can be used to direct model choice. However, co-culture experiments are far more challenging: we show that these same spatial statistics which provide useful insight into mono-culture systems are insufficient for co-culture systems. Therefore, we conclude that great care ought to be exercised when estimating the parameters of co-culture assays.

scholarly journals CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons

2019 ◽  
Author(s):  
Ruilin Tian ◽  
Mariam A. Gachechiladze ◽  
Connor H. Ludwig ◽  
Matthew T. Laurie ◽  
Jason Y. Hong ◽  
...  
Keyword(s):  
Cell Biology ◽  
Cell Types ◽  
Neuronal Morphology ◽  
Genetic Screens ◽  
Crispr Interference ◽  
Disease States ◽  
Differentiated Cells ◽  
Human Neurons ◽  
Longitudinal Imaging ◽  
Induced Pluripotent

SUMMARYCRISPR/Cas9-based functional genomics have transformed our ability to elucidate mammalian cell biology. However, most previous CRISPR-based screens were conducted in cancer cell lines, rather than healthy, differentiated cells. Here, we describe a CRISPR interference (CRISPRi)-based platform for genetic screens in human neurons derived from induced pluripotent stem cells (iPSCs). We demonstrate robust and durable knockdown of endogenous genes in such neurons, and present results from three complementary genetic screens. First, a survival-based screen revealed neuron-specific essential genes and genes that improved neuronal survival upon knockdown. Second, a screen with a single-cell transcriptomic readout uncovered several examples of genes whose knockdown had strikingly cell-type specific consequences. Third, a longitudinal imaging screen detected distinct consequences of gene knockdown on neuronal morphology. Our results highlight the power of unbiased genetic screens in iPSC-derived differentiated cell types and provide a platform for systematic interrogation of normal and disease states of neurons.

scholarly journals Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural Networks

2016 ◽  
Author(s):  
Shashank Singh ◽  
Yang Yang ◽  
Barnabás Póczos ◽  
Jian Ma
Keyword(s):  
Deep Learning ◽  
Target Genes ◽  
Genomic Sequence ◽  
Cell Types ◽  
Computational Method ◽  
Learning Models ◽  
Cell Type ◽  
Genome Wide ◽  
Level Information ◽  
Single Cell Type

AbstractIn the human genome, distal enhancers are involved in regulating target genes through proxi-mal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Here we report a new computational method (named “SPEID”) using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes. This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.

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