Natural killer (NK) cells are an important component of the cancer immune surveillance system. They are regulated by germline-encoded receptors that activate and inhibit their effector function, such as secretion of c...
详细信息
Natural killer (NK) cells are an important component of the cancer immune surveillance system. They are regulated by germline-encoded receptors that activate and inhibit their effector function, such as secretion of cytokines and direct lysis of tumor cells and virus-infected cells. Without the need to be primed by prior exposure to tumor antigen, NK cells can detect ligands expressed on tumor cells and selectively kill these cells. NK cells are under strict control by inhibitory receptors that bind to HLA class I on target cells and block early activation signals, thus preventing lysis of target cells. The sensitivity to lysis by NK cells is therefore determined to a large extent by the expression of HLA class I molecules on tumor cells. In addition to receptor–ligand interactions that occur at NK–target cell synapses, many other factors determine the sensitivity of tumor cells to lysis by NK. Intrinsic properties of tumor cells, such as their metabolism and signaling networks establish a threshold above which they will succumb to the death pathways triggered by NK cell attack. Here we provide a protocol for a genome-wide CRISPR screen in tumor cells to identify factors that regulate their sensitivity to primary human NK cells. Tumor cells first transduced for expression of Cas9 are then transduced with a guide RNA (gRNA) library and co-cultured with NK cells. Deep sequencing of the library generated from the genome of tumor cells that survived the selection by NK cells and analysis of the distribution of guide RNAs is performed to identify genes that promote either sensitivity or resistance to NK-mediated killing. The contribution of individual genes to tumor sensitivity can be validated by knockouts using individual gRNAs. The techniques and workflow described here could be applied to primary tumors from cancer patients and reveal tumor-specific points of vulnerability that could be exploited for cancer immunotherapy, such as checkpoint blockade or expression of
Using High-Throughput DNA Sequencing (HTS) to examine gene expression is rapidly becoming a viable choice and is typically referred to as RNA-seq. Often the depth and breadth of coverage of RNA-seq data can exce...
详细信息
Using High-Throughput DNA Sequencing (HTS) to examine gene expression is rapidly becoming a viable choice and is typically referred to as RNA-seq. Often the depth and breadth of coverage of RNA-seq data can exceed what is achievable using microarrays. However, the strengths of RNA-seq are often its greatest weaknesses. Accurately and comprehensively mapping millions of relatively short reads to a reference genome sequence can require not only specialized software, but also more structured and automated procedures to manage, analyze, and visualize the data. Additionally, the computational hardware required to efficiently process and store the data can be a necessary and often-overlooked component of a research plan. We discuss several aspects of the computational analysis of RNA-seq, including file management and data quality control, analysis, and visualization. We provide a framework for a standard nomenclature system that can facilitate automation and the ability to track data provenance. Finally, we provide a general workflow of the computational analysis of RNA-seq and a downloadable package of scripts to automate the *** High-Throughput DNA Sequencing (HTS) to examine gene expression is rapidly becoming a viable choice and is typically referred to as RNA-seq. Often the depth and breadth of coverage of RNA-seq data can exceed what is achievable using microarrays. However, the strengths of RNA-seq are often its greatest weaknesses. Accurately and comprehensively mapping millions of relatively short reads to a reference genome sequence can require not only specialized software, but also more structured and automated procedures to manage, analyze, and visualize the data. Additionally, the computational hardware required to efficiently process and store the data can be a necessary and often-overlooked component of a research plan. We discuss several aspects of the computational analysis of RNA-seq, including file management and data quality control,
Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic in...
详细信息
Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology. less
Users can adapt contemporary natural language interfaces (NLIs) by teaching the NLIs how to handle new natural language (NL) inputs. One promising approach is interactive task learning (ITL), which enables users to te...
详细信息
Users can adapt contemporary natural language interfaces (NLIs) by teaching the NLIs how to handle new natural language (NL) inputs. One promising approach is interactive task learning (ITL), which enables users to teach new NL inputs for multi-modal systems. While recent advances enable users to teach the syntactic and semantic level of the NL inputs through ITL, NLIs are still not able to learn how to consider the context, such as the current state of the graphical user interface (GUI). To address this challenge, we designed MALACHITE through three formative studies. MALACHITE enables users to successfully teach NL inputs on a semantic and syntactic level leveraging the GUI screen of a datavisualization tool. With two evaluative studies, we provide evidence that with MALACHITE's suggestions users significantly improve their accuracy by a factor of 2.3 in teaching GUI-dependent NL inputs in contrast to those without MALACHITE's suggestions.
暂无评论