Nature Protocols最新文献

The recombinant expression, isolation and characterization of pore-forming proteins is one of the most commonly used strategies for understanding the permeability properties of the biological membrane into which they are embedded. This protocol describes how to quantify the expression of your protein of interest and use this information to optimize its production using the Escherichia coli strain BL21Gold(de3)ΔABCF. It explains with a step-by-step approach how to separate the bacterial compartments according to their solubility and how to extract your protein of interest in its native conformation using detergent solutions. Finally, it describes how to improve its purity via ion-exchange chromatography and insert the purified porins into outer membrane vesicles, from which they can be copurified. The protocol is simpler and less empirical than those described for most channel-forming membrane proteins and also provides a solid foundation for the isolation of soluble proteins. Several parameters can be optimized on a case-by-case basis: expression time and temperature, concentration of the inducer, nature and concentration of the detergent, incubation time and temperature, pH and ionic strength of the purification buffers. This protocol is effective with prokaryotic channel-forming membrane proteins and can be employed for the production of pore-forming proteins from chloroplasts, mitochondria or eukaryotes in general. With minor optimization, this protocol can be adapted for the isolation of receptors, carrier, pumps or any other membrane-active proteins.

Oligodendrocyte precursor cells (OPCs) sculpt neural circuits through the phagocytic engulfment of synapses during development and adulthood. However, existing techniques for analyzing synapse engulfment by OPCs have limited accuracy. Here we describe the quantification of synapse engulfment by OPCs via a two-pronged cell biological approach that combines high-confidence and high-throughput methodologies. Firstly, an adeno-associated virus encoding a pH-sensitive, fluorescently tagged synaptic marker is expressed in neurons in vivo to differentially label presynaptic inputs, depending upon whether they are outside of or within acidic phagolysosomal compartments. When paired with immunostaining for OPC markers in lightly fixed tissue, this approach quantifies the engulfment of synapses by around 30-50 OPCs in each experiment. The second method uses OPCs isolated from dissociated brain tissue that are then fixed, incubated with fluorescent antibodies against presynaptic proteins, and analyzed by flow cytometry, enabling the quantification of presynaptic material within tens of thousands of OPCs in <1 week. The integration of both methods extends the current imaging-based assays, originally designed to quantify synaptic phagocytosis by other brain cells such as microglia and astrocytes, by enabling the quantification of synaptic engulfment by OPCs at individual and populational levels. With minor modifications, these approaches can be adapted to study synaptic phagocytosis by numerous glial cell types in the brain. The protocol is suitable for users with expertise in both confocal microscopy and flow cytometry. The imaging-based and flow cytometry-based protocols require 5 weeks and 2 d to complete, respectively.

ChIP-seq is a widely used technique for studying histone post-translational modifications and DNA-binding proteins. DNA fragments associated with a specific protein or histone modification epitope are captured by using antibodies, sequenced and mapped to a reference genome. Albeit versatile and popular, performing many parallel ChIP-seq experiments to compare different conditions, replicates and epitopes is laborious, is prone to experimental variation and does not allow quantitative comparisons unless adequate spike-in chromatin is included. We present a detailed protocol for performing and analyzing a multiplexed quantitative chromatin immunoprecipitation-sequencing experiment (MINUTE-ChIP), in which multiple samples are profiled against multiple epitopes in a single workflow. Multiplexing not only dramatically increases the throughput of ChIP-seq experiments (e.g., profiling 12 samples against multiple histone modifications or DNA-binding proteins in a single experiment), but also enables accurate quantitative comparisons. The protocol consists of four parts: sample preparation (i.e., lysis, chromatin fragmentation and barcoding of native or formaldehyde-fixed material), pooling and splitting of the barcoded chromatin into parallel immunoprecipitation reactions, preparation of next-generation sequencing libraries from input and immunoprecipitated DNA and data analysis using our dedicated analysis pipeline. This pipeline autonomously generates quantitatively scaled ChIP-seq tracks for downstream analysis and visualization, alongside necessary quality control indicators. The entire workflow requires basic knowledge in molecular biology and bioinformatics and can be completed in 1 week. MINUTE-ChIP empowers biologists to perform every ChIP-seq experiment with an appropriate number of replicates and control conditions, delivering more statistically robust, exquisitely quantitative and biologically meaningful results.

Haplotype phasing represents a pivotal procedure in genome analysis, entailing the identification of specific genetic variant combinations on each chromosome. Achieving chromosome-level genome phasing constitutes a considerable challenge, particularly in organisms with large and complex genomes. To address this challenge, we have developed a robust, gamete cell-based phasing pipeline, including wet-laboratory processes for plant sperm cell isolation, short-read sequencing and a bioinformatics workflow to generate chromosome-level phasing. The bioinformatics workflow is applicable for both plant and other sperm cells, for example, those of mammals. Our pipeline ensures high-quality single-nucleotide polymorphism (SNP) calling for each sperm cell and the subsequent construction of a high-density genetic map. The genetic map facilitates accurate chromosome-level genome phasing, enables crossover event detection and could be used to correct potential assembly errors. Our bioinformatics pipeline runs on a Linux system and most of its steps can be executed in parallel, expediting the analysis process. The entire workflow can be performed over the course of 1 d. We provide a practical example from our previous research using this protocol and provide the whole bioinformatics pipeline as a Docker image to ensure its easy adaptability to other studies.

High-throughput RNA sequencing enables the quantification of transcript abundance and the identification of novel transcripts in biological samples. These include circular RNAs (circRNAs), a family of alternatively spliced RNA molecules that form a continuous loop. However, quantification and comparison of circRNAs between RNA sequencing libraries remain challenging due to confounding errors introduced during exonuclease digestion, library preparation and RNA sequencing itself. Here we describe a set of synthetic circRNA spike-ins-termed 'SynCRS'-that can be added directly to purified RNA samples before exonuclease digestion and library preparation. SynCRS, introduced either individually or in combinations of varying size and abundance, can be integrated into all next-generation sequencing workflows and, critically, facilitate the quantitative calibration of circRNA transcript abundance between samples, tissue types, species and laboratories. Our step-by-step protocol details the generation of SynCRS and guides users on the stoichiometry of SynCRS spike-in to RNA samples, followed by the bioinformatic steps required to facilitate quantitative comparisons of circRNAs between libraries. The laboratory steps to produce the SynCRS require an additional 3 d on top of the high throughput circRNA sequencing and bioinformatics. The protocol is suitable for users with basic experience in molecular biology and bioinformatics.

Cell-cell interactions are essential for the function and contextual regulation of biological tissues. We present a platform for high-throughput microfluidics-supported genetic screening of functional regulators of cell-cell interactions. Systematic perturbation of encapsulated associated cells followed by sequencing (SPEAC-seq) combines genome-wide CRISPR libraries, cell coculture in droplets and microfluidic droplet sorting based on functional read-outs determined by fluorescent reporter circuits to enable the unbiased discovery of interaction regulators. This technique overcomes limitations of traditional methods for characterization of cell-cell communication, which require a priori knowledge of cellular interactions, are highly engineered and lack functional read-outs. As an example of this technique, we describe the investigation of neuroinflammatory intercellular communication between microglia and astrocytes, using genome-wide CRISPR-Cas9 inactivation libraries and fluorescent reporters of NF-κB activation. This approach enabled the discovery of thousands of microglial regulators of astrocyte NF-κB activation important for the control of central nervous system inflammation. Importantly, SPEAC-seq can be adapted to different cell types, screening modalities, cell functions and physiological contexts, only limited by the ability to fluorescently report cell functions and by droplet cultivation conditions. Performing genome-wide screening takes less than 2 weeks and requires microfluidics capabilities. Thus, SPEAC-seq enables the large-scale investigation of cell-cell interactions.

O-GalNAc glycans, also known as mucin-type O-glycans, are primary constituents of mucins on various mucosal sites of the body and also ubiquitously expressed on cell surface and secreted proteins. They have crucial roles in a wide range of physiological and pathological processes, including tumor growth and progression. In addition, altered expression of O-GalNAc glycans is frequently observed during different disease states. Research dedicated to unraveling the structure-function relationships of O-GalNAc glycans has led to the discovery of disease biomarkers and diagnostic tools and the development of O-glycopeptide-based cancer vaccines. Many of these efforts require amino acid-linked O-GalNAc core structures as building blocks to assemble complex O-glycans and glycopeptides. There are eight core structures (cores one to eight), from which all mucin-type O-glycans are derived. In this protocol, we describe the first divergent synthesis of all eight cores from a versatile precursor in practical scales. The protocol involves (i) chemical synthesis of the orthogonally protected precursor (3 days) from commercially available materials, (ii) chemical synthesis of five unique glycosyl donors (1-2 days for each donor) and (iii) selective deprotection of the precursor and assembly of the eight cores (2-4 days for each core). The procedure can be adopted to prepare O-GalNAc cores linked to serine, threonine and tyrosine, which can then be utilized directly for solid-phase glycopeptide synthesis or chemoenzymatic synthesis of complex O-glycans. The procedure empowers researchers with fundamental organic chemistry skills to prepare gram scales of any desired O-GalNAc core(s) or all eight cores concurrently.

We previously described a protocol for genome engineering of mammalian cultured cells with clustered regularly interspaced short palindromic repeats and associated protein 9 (CRISPR-Cas9) to generate homozygous knock-ins of fluorescent tags into endogenous genes. Here we are updating this former protocol to reflect major improvements in the workflow regarding efficiency and throughput. In brief, we have improved our method by combining high-efficiency electroporation of optimized CRISPR-Cas9 reagents, screening of single cell-derived clones by automated bright-field and fluorescence imaging, rapidly assessing the number of tagged alleles and potential off-targets using digital polymerase chain reaction (PCR) and automated data analysis. Compared with the original protocol, our current procedure (1) substantially increases the efficiency of tag integration, (2) automates the identification of clones derived from single cells with correct subcellular localization of the tagged protein and (3) provides a quantitative and high throughput assay to measure the number of on- and off-target integrations with digital PCR. The increased efficiency of the new procedure reduces the number of clones that need to be analyzed in-depth by more than tenfold and yields to more than 26% of homozygous clones in polyploid cancer cell lines in a single genome engineering round. Overall, we were able to dramatically reduce the hands-on time from 30 d to 10 d during the overall ~10 week procedure, allowing a single person to process up to five genes in parallel, assuming that validated reagents-for example, PCR primers, digital PCR assays and western blot antibodies-are available.

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.