In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer.
TL;DR: In this paper, the authors developed an in-vivo T-cell CRISPR screening platform and identified a key mechanism restraining TEFF biology through the ETS family TF, Fli1.
About: This article is published in Cell.The article was published on 2021-03-04. It has received 74 citations till now.
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TL;DR: Bock et al. as mentioned in this paper described the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development, with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen.
Abstract: CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development — with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen. CRISPR screening is a high-throughput approach for identifying genes, pathways and mechanisms involved in a given phenotype or biological process. High-content read-outs of these screens, such as imaging and single-cell sequencing techniques, have further broadened its applicability. This Primer by Bock et al. describes the main concepts of CRISPR screening and gives examples of its application as a method for biological discovery, with a focus on the use of high-content read-outs.
64 citations
61 citations
TL;DR: In this paper , the authors identify positive regulators of T cell functions through overexpression of around 12,000 barcoded human open reading frames (ORFs), which increased the proliferation and activation of primary human CD4+ and CD8+ T cells and their secretion of key cytokines such as interleukin-2 and interferonγ.
Abstract: The engineering of autologous patient T cells for adoptive cell therapies has revolutionized the treatment of several types of cancer1. However, further improvements are needed to increase response and cure rates. CRISPR-based loss-of-function screens have been limited to negative regulators of T cell functions2–4 and raise safety concerns owing to the permanent modification of the genome. Here we identify positive regulators of T cell functions through overexpression of around 12,000 barcoded human open reading frames (ORFs). The top-ranked genes increased the proliferation and activation of primary human CD4+ and CD8+ T cells and their secretion of key cytokines such as interleukin-2 and interferon-γ. In addition, we developed the single-cell genomics method OverCITE-seq for high-throughput quantification of the transcriptome and surface antigens in ORF-engineered T cells. The top-ranked ORF—lymphotoxin-β receptor (LTBR)—is typically expressed in myeloid cells but absent in lymphocytes. When overexpressed in T cells, LTBR induced profound transcriptional and epigenomic remodelling, leading to increased T cell effector functions and resistance to exhaustion in chronic stimulation settings through constitutive activation of the canonical NF-κB pathway. LTBR and other highly ranked genes improved the antigen-specific responses of chimeric antigen receptor T cells and γδ T cells, highlighting their potential for future cancer-agnostic therapies5. Our results provide several strategies for improving next-generation T cell therapies by the induction of synthetic cell programmes. A genome-scale gain-of-function screen using overexpression of nearly 12,000 open reading frames (ORFs) identifies positive regulators of human T cell function and suggests that ORF-based screens could be applied clinically to improve T cell therapies.
53 citations
TL;DR: In this paper , the authors provided an atlas of the genetic regulators of T cell exhaustion and demonstrated that modulation of epigenetic state can improve T cell responses in cancer immunotherapy, which led to improved antitumor immunity.
53 citations
TL;DR: In this article , the authors identify positive regulators of T cell functions through overexpression of around 12,000 barcoded human open reading frames (ORFs), which increased the proliferation and activation of primary human CD4+ and CD8+ T cells and their secretion of key cytokines such as interleukin-2 and interferonγ.
Abstract: The engineering of autologous patient T cells for adoptive cell therapies has revolutionized the treatment of several types of cancer1. However, further improvements are needed to increase response and cure rates. CRISPR-based loss-of-function screens have been limited to negative regulators of T cell functions2–4 and raise safety concerns owing to the permanent modification of the genome. Here we identify positive regulators of T cell functions through overexpression of around 12,000 barcoded human open reading frames (ORFs). The top-ranked genes increased the proliferation and activation of primary human CD4+ and CD8+ T cells and their secretion of key cytokines such as interleukin-2 and interferon-γ. In addition, we developed the single-cell genomics method OverCITE-seq for high-throughput quantification of the transcriptome and surface antigens in ORF-engineered T cells. The top-ranked ORF—lymphotoxin-β receptor (LTBR)—is typically expressed in myeloid cells but absent in lymphocytes. When overexpressed in T cells, LTBR induced profound transcriptional and epigenomic remodelling, leading to increased T cell effector functions and resistance to exhaustion in chronic stimulation settings through constitutive activation of the canonical NF-κB pathway. LTBR and other highly ranked genes improved the antigen-specific responses of chimeric antigen receptor T cells and γδ T cells, highlighting their potential for future cancer-agnostic therapies5. Our results provide several strategies for improving next-generation T cell therapies by the induction of synthetic cell programmes. A genome-scale gain-of-function screen using overexpression of nearly 12,000 open reading frames (ORFs) identifies positive regulators of human T cell function and suggests that ORF-based screens could be applied clinically to improve T cell therapies.
49 citations
References
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TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
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47,038 citations
TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Abstract: Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Availability: http://samtools.sourceforge.net
Contact: [email protected]
45,957 citations
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
37,898 citations
TL;DR: A new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format, which allows the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks.
Abstract: Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing webbased methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools
18,858 citations
TL;DR: An R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters and can be easily extended to other species and ontologies is presented.
Abstract: Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters The analysis module and visualization module were combined into a reusable workflow Currently, clusterProfiler supports three species, including humans, mice, and yeast Methods provided in this package can be easily extended to other species and ontologies The clusterProfiler package is released under Artistic-20 License within Bioconductor project The source code and vignette are freely available at http://bioconductororg/packages/release/bioc/html/clusterProfilerhtml
16,644 citations