Nature Protocols最新文献

{"title":"Characterizing the structure of mouse behavior using Motion Sequencing","authors":"Sherry Lin, Winthrop F. Gillis, Caleb Weinreb, Ayman Zeine, Samuel C. Jones, Emma M. Robinson, Jeffrey Markowitz, Sandeep Robert Datta","doi":"10.1038/s41596-024-01015-w","DOIUrl":"10.1038/s41596-024-01015-w","url":null,"abstract":"Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called ‘syllables’. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior. Motion Sequencing uses three-dimensional machine vision and unsupervised machine learning on depth videos to decompose spontaneous mouse behavior into a series of elemental modules called ‘syllables’, revealing how often syllables are used and how they transition over time.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 11","pages":"3242-3291"},"PeriodicalIF":13.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141458192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A versatile tissue-rolling technique for spatial-omics analyses of the entire murine gastrointestinal tract","authors":"Gustavo Monasterio, Rodrigo A. Morales, David A. Bejarano, Xesús M. Abalo, Jennifer Fransson, Ludvig Larsson, Andreas Schlitzer, Joakim Lundeberg, Srustidhar Das, Eduardo J. Villablanca","doi":"10.1038/s41596-024-01001-2","DOIUrl":"10.1038/s41596-024-01001-2","url":null,"abstract":"Tissues are dynamic and complex biological systems composed of specialized cell types that interact with each other for proper biological function. To comprehensively characterize and understand the cell circuitry underlying biological processes within tissues, it is crucial to preserve their spatial information. Here we report a simple mounting technique to maximize the area of the tissue to be analyzed, encompassing the whole length of the murine gastrointestinal (GI) tract, from mouth to rectum. Using this method, analysis of the whole murine GI tract can be performed in a single slide not only by means of histological staining, immunohistochemistry and in situ hybridization but also by multiplexed antibody staining and spatial transcriptomic approaches. We demonstrate the utility of our method in generating a comprehensive gene and protein expression profile of the whole GI tract by combining the versatile tissue-rolling technique with a cutting-edge transcriptomics method (Visium) and two cutting-edge proteomics methods (ChipCytometry and CODEX-PhenoCycler) in a systematic and easy-to-follow step-by-step procedure. The entire process, including tissue rolling, processing and sectioning, can be achieved within 2–3 d for all three methods. For Visium spatial transcriptomics, an additional 2 d are needed, whereas for spatial proteomics assays (ChipCytometry and CODEX-PhenoCycler), another 3–4 d might be considered. The whole process can be accomplished by researchers with skills in performing murine surgery, and standard histological and molecular biology methods. This protocol presents a versatile tissue-rolling technique for spatially profiling the transcriptome and proteome of the whole murine gastrointestinal tract with high spatial resolution.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 10","pages":"3085-3137"},"PeriodicalIF":13.1,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141437254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electro-elution-based purification of covalent DNA–protein cross-links","authors":"Pedro Weickert, Sophie Dürauer, Maximilian J. Götz, Hao-Yi Li, Julian Stingele","doi":"10.1038/s41596-024-01004-z","DOIUrl":"10.1038/s41596-024-01004-z","url":null,"abstract":"Covalent DNA–protein cross-links (DPCs) are pervasive DNA lesions that challenge genome stability and can be induced by metabolic or chemotherapeutic cross-linking agents including reactive aldehydes, topoisomerase poisons and DNMT1 inhibitors. The purification of x-linked proteins (PxP), where DNA–cross-linked proteins are separated from soluble proteins via electro-elution, can be used to identify DPCs. Here we describe a versatile and sensitive strategy for PxP. Mammalian cells are collected following exposure to a DPC-inducing agent, embedded in low-melt agarose plugs and lysed under denaturing conditions. Following lysis, the soluble proteins are extracted from the agarose plug by electro-elution, while genomic DNA and cross-linked proteins are retained in the plug. The cross-linked proteins can then be analyzed by standard analytical techniques such as sodium dodecyl-sulfate–polyacrylamide gel electrophoresis followed by western blotting or fluorescent staining. Alternatively, quantitative mass spectrometry-based proteomics can be used for the unbiased identification of DPCs. The isolation and analysis of DPCs by PxP overcomes the limitations of alternative methods to analyze DPCs that rely on precipitation as the separating principle and can be performed by users trained in molecular or cell biology within 2–3 d. The protocol has been optimized to study DPC induction and repair in mammalian cells but may also be adapted to other sample types including bacteria, yeast and tissue samples. An assay based on the electrophoresis of whole-cell lysates embedded in agarose plugs separates soluble from immobilized proteins, enabling the purification and the subsequent identification of DNA–protein cross-links.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 10","pages":"2891-2914"},"PeriodicalIF":13.1,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding","authors":"Karl D. Gaisser, Sophie N. Skloss, Leandra M. Brettner, Luana Paleologu, Charles M. Roco, Alexander B. Rosenberg, Matthew Hirano, R. William DePaolo, Georg Seelig, Anna Kuchina","doi":"10.1038/s41596-024-01007-w","DOIUrl":"10.1038/s41596-024-01007-w","url":null,"abstract":"Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. The standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R. Single-cell transcriptomics of bacteria is challenging. microSPLiT is a high-throughput method for single-cell RNA sequencing of both Gram-positive and Gram-negative bacteria using combinatorial barcoding without the need for specialized equipment.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 10","pages":"3048-3084"},"PeriodicalIF":13.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The HADDOCK2.4 web server for integrative modeling of biomolecular complexes","authors":"Rodrigo V. Honorato, Mikael E. Trellet, Brian Jiménez-García, Jörg J. Schaarschmidt, Marco Giulini, Victor Reys, Panagiotis I. Koukos, João P. G. L. M. Rodrigues, Ezgi Karaca, Gydo C. P. van Zundert, Jorge Roel-Touris, Charlotte W. van Noort, Zuzana Jandová, Adrien S. J. Melquiond, Alexandre M. J. J. Bonvin","doi":"10.1038/s41596-024-01011-0","DOIUrl":"10.1038/s41596-024-01011-0","url":null,"abstract":"Interactions between macromolecules, such as proteins and nucleic acids, are essential for cellular functions. Experimental methods can fail to provide all the information required to fully model biomolecular complexes at atomic resolution, particularly for large and heterogeneous assemblies. Integrative computational approaches have, therefore, gained popularity, complementing traditional experimental methods in structural biology. Here, we introduce HADDOCK2.4, an integrative modeling platform, and its updated web interface ( https://wenmr.science.uu.nl/haddock2.4 ). The platform seamlessly integrates diverse experimental and theoretical data to generate high-quality models of macromolecular complexes. The user-friendly web server offers automated parameter settings, access to distributed computing resources, and pre- and post-processing steps that enhance the user experience. To present the web server’s various interfaces and features, we demonstrate two different applications: (i) we predict the structure of an antibody–antigen complex by using NMR data for the antigen and knowledge of the hypervariable loops for the antibody, and (ii) we perform coarse-grained modeling of PRC1 with a nucleosome particle guided by mutagenesis and functional data. The described protocols require some basic familiarity with molecular modeling and the Linux command shell. This new version of our widely used HADDOCK web server allows structural biologists and non-experts to explore intricate macromolecular assemblies encompassing various molecule types. The HADDOCK2.4 web server is a modeling platform that can integrate experimental and theoretical data for guiding 3D prediction of biomolecular complexes.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 11","pages":"3219-3241"},"PeriodicalIF":13.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing acyl–enzyme intermediates with genetically encoded 2,3-diaminopropionic acid for hydrolase substrate identification","authors":"Juan Luo, Yao Yu, Ke Wang, Sizhe He, Longjie Wang, Fangfang Liang, Jason W. Chin, Shan Tang","doi":"10.1038/s41596-024-01006-x","DOIUrl":"10.1038/s41596-024-01006-x","url":null,"abstract":"Catalytic mechanism-based, light-activated traps have recently been developed to identify the substrates of cysteine or serine hydrolases. These traps are hydrolase mutants whose catalytic cysteine or serine are replaced with genetically encoded 2,3-diaminopropionic acid (DAP). DAP-containing hydrolases specifically capture the transient thioester- or ester-linked acyl–enzyme intermediates resulting from the first step of the proteolytic reaction as their stable amide analogs. The trapped substrate fragments allow the downstream identification of hydrolase substrates by mass spectrometry and immunoblotting. In this protocol, we provide a detailed step-by-step guide for substrate capture and identification of the peptidase domain of the large tegument protein deneddylase (UL36USP) from human herpesvirus 1, both in mammalian cell lysate and live mammalian cells. Four procedures are included: Procedure 1, DAP-mediated substrate trapping in mammalian cell lysate (~8 d); Procedure 2, DAP-mediated substrate trapping in adherent mammalian cells (~6 d); Procedure 3, DAP-mediated substrate trapping in suspension mammalian cells (~5 d); and Procedure 4, substrate identification and validation (~12–13 d). Basic skills to perform protein expression in bacteria or mammalian cells, affinity enrichment and proteomic analysis are required to implement the protocol. This protocol will be a practical guide for identifying substrates of serine or cysteine hydrolases either in a complex mixture, where genetic manipulation is challenging, or in live cells such as bacteria, yeasts and mammalian cells. Light-activated, 2,3-diaminopropionic acid-containing hydrolases trap substrate fragments, facilitating the discovery of new substrates and activities of enzymes in complex mixtures and live cells by mass spectrometry.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 10","pages":"2967-2999"},"PeriodicalIF":13.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141311211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular recording using DNA Typewriter","authors":"Hanna Liao, Junhong Choi, Jay Shendure","doi":"10.1038/s41596-024-01003-0","DOIUrl":"10.1038/s41596-024-01003-0","url":null,"abstract":"Recording molecular information to genomic DNA is a powerful means of investigating topics ranging from multicellular development to cancer evolution. With molecular recording based on genome editing, events such as cell divisions and signaling pathway activity drive specific alterations in a cell’s DNA, marking the genome with information about a cell’s history that can be read out after the fact. Although genome editing has been used for molecular recording, capturing the temporal relationships among recorded events in mammalian cells remains challenging. The DNA Typewriter system overcomes this limitation by leveraging prime editing to facilitate sequential insertions to an engineered genomic region. DNA Typewriter includes three distinct components: DNA Tape as the ‘substrate’ to which edits accrue in an ordered manner, the prime editor enzyme, and prime editing guide RNAs, which program insertional edits to DNA Tape. In this protocol, we describe general design considerations for DNA Typewriter, step-by-step instructions on how to perform recording experiments by using DNA Typewriter in HEK293T cells, and example scripts for analyzing DNA Typewriter data ( https://doi.org/10.6084/m9.figshare.22728758 ). This protocol covers two main applications of DNA Typewriter: recording sequential transfection events with programmed barcode insertions by using prime editing and recording lineage information during the expansion of a single cell to many. Compared with other methods that are compatible with mammalian cells, DNA Typewriter enables the recording of temporal information with higher recording capacities and can be completed within 4–6 weeks with basic expertise in molecular cloning, mammalian cell culturing and DNA sequencing data analysis. This protocol describes a CRISPR prime editing-based method for the sequential and unidirectional tracing of insertional events in mammalian cells, generating a dynamic recording of such information within living cells.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 10","pages":"2833-2862"},"PeriodicalIF":13.1,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141284272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scanorama: integrating large and diverse single-cell transcriptomic datasets","authors":"Brian L. Hie, Soochi Kim, Thomas A. Rando, Bryan Bryson, Bonnie Berger","doi":"10.1038/s41596-024-00991-3","DOIUrl":"10.1038/s41596-024-00991-3","url":null,"abstract":"Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5–3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama–Colaboratory resource should make scRNA-seq integration more widely accessible to researchers. Scanorama is an effective tool for combining multiple single-cell RNA sequencing datasets, addressing technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of diverse datasets.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 8","pages":"2283-2297"},"PeriodicalIF":13.1,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141284273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All-optical voltage imaging-guided postsynaptic single-cell transcriptome profiling with Voltage-Seq","authors":"Veronika Csillag, J. C. Noble, Daniela Calvigioni, Björn Reinius, János Fuzik","doi":"10.1038/s41596-024-01005-y","DOIUrl":"10.1038/s41596-024-01005-y","url":null,"abstract":"Neuronal pathways recruit large postsynaptic populations and maintain connections via distinct postsynaptic response types (PRTs). Until recently, PRTs were accessible as a selection criterion for single-cell RNA sequencing only through probing by low-throughput whole-cell electrophysiology. To overcome these limitations and target neurons on the basis of specific PRTs for soma collection and subsequent single-cell RNA sequencing, we developed Voltage-Seq using the genetically encoded voltage indicator Voltron in acute brain slices from mice. We also created an onsite analysis tool, VoltView, to guide soma collection of specific PRTs using a classifier based on a previously acquired database of connectomes from multiple animals. Here we present our procedure for preparing the optical path, the imaging setup and detailing the imaging and analysis steps, as well as a complete procedure for sequencing library preparation. This enables researchers to conduct our high-throughput all-optical synaptic assay and to obtain single-cell transcriptomic data from selected postsynaptic neurons. This also allows researchers to resolve the connectivity ratio of a specific pathway and explore the diversity of PRTs within that connectome. Furthermore, combining high throughput with quick analysis gives unique access to find specific connections within a large postsynaptic connectome. Voltage-Seq also allows the investigation of correlations between connectivity and gene expression changes in a postsynaptic cell-type-specific manner for both excitatory and inhibitory connections. The Voltage-Seq workflow can be completed in ~6 weeks, including 4–5 weeks for viral expression of the Voltron sensor. The technique requires knowledge of basic laboratory techniques, micromanipulator handling skills and experience in molecular biology and bioinformatics. Voltage-Seq is a method for all-optical voltage imaging-guided postsynaptic single-cell transcriptomics. It combines the use of the Voltron voltage indicator with the analysis tool VoltView to select specific neuronal somas to collect for single-cell RNA sequencing.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 10","pages":"2863-2890"},"PeriodicalIF":13.1,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141248028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Author Correction: CRISPR-Cas9-based genome-wide screening of Toxoplasma gondii.","authors":"Saima M Sidik, Diego Huet, Sebastian Lourido","doi":"10.1038/s41596-024-01018-7","DOIUrl":"https://doi.org/10.1038/s41596-024-01018-7","url":null,"abstract":"","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":" ","pages":""},"PeriodicalIF":14.8,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141248029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}