Pub Date : 2024-05-28DOI: 10.1038/s41596-024-00987-z
Andreas Dannhorn, Emine Kazanc, Lucy Flint, Fei Guo, Alfie Carter, Andrew R. Hall, Stewart A. Jones, George Poulogiannis, Simon T. Barry, Owen J. Sansom, Josephine Bunch, Zoltan Takats, Richard J. A. Goodwin
The landscape of tissue-based imaging modalities is constantly and rapidly evolving. While formalin-fixed, paraffin-embedded material is still useful for histological imaging, the fixation process irreversibly changes the molecular composition of the sample. Therefore, many imaging approaches require fresh-frozen material to get meaningful results. This is particularly true for molecular imaging techniques such as mass spectrometry imaging, which are widely used to probe the spatial arrangement of the tissue metabolome. As high-quality fresh-frozen tissues are limited in their availability, any sample preparation workflow they are subjected to needs to ensure morphological and molecular preservation of the tissues and be compatible with as many of the established and emerging imaging techniques as possible to obtain the maximum possible insights from the tissues. Here we describe a universal sample preparation workflow, from the initial step of freezing the tissues to the cold embedding in a new hydroxypropyl methylcellulose/polyvinylpyrrolidone-enriched hydrogel and the generation of thin tissue sections for analysis. Moreover, we highlight the optimized storage conditions that limit molecular and morphological degradation of the sections. The protocol is compatible with human and plant tissues and can be easily adapted for the preparation of alternative sample formats (e.g., three-dimensional cell cultures). The integrated workflow is universally compatible with histological tissue analysis, mass spectrometry imaging and imaging mass cytometry, as well as spatial proteomic, genomic and transcriptomic tissue analysis. The protocol can be completed within 4 h and requires minimal prior experience in the preparation of tissue samples for multimodal imaging experiments. The morphological and molecular preservation of fresh-frozen tissues is difficult. Embedding with an hydroxypropyl methylcellulose/polyvinylpyrrolidone-rich hydrogel results in a material compatible with spatial biochemical analysis (e.g., mass spectrometry imaging), enabling multimodal data integration.
{"title":"Morphological and molecular preservation through universal preparation of fresh-frozen tissue samples for multimodal imaging workflows","authors":"Andreas Dannhorn, Emine Kazanc, Lucy Flint, Fei Guo, Alfie Carter, Andrew R. Hall, Stewart A. Jones, George Poulogiannis, Simon T. Barry, Owen J. Sansom, Josephine Bunch, Zoltan Takats, Richard J. A. Goodwin","doi":"10.1038/s41596-024-00987-z","DOIUrl":"10.1038/s41596-024-00987-z","url":null,"abstract":"The landscape of tissue-based imaging modalities is constantly and rapidly evolving. While formalin-fixed, paraffin-embedded material is still useful for histological imaging, the fixation process irreversibly changes the molecular composition of the sample. Therefore, many imaging approaches require fresh-frozen material to get meaningful results. This is particularly true for molecular imaging techniques such as mass spectrometry imaging, which are widely used to probe the spatial arrangement of the tissue metabolome. As high-quality fresh-frozen tissues are limited in their availability, any sample preparation workflow they are subjected to needs to ensure morphological and molecular preservation of the tissues and be compatible with as many of the established and emerging imaging techniques as possible to obtain the maximum possible insights from the tissues. Here we describe a universal sample preparation workflow, from the initial step of freezing the tissues to the cold embedding in a new hydroxypropyl methylcellulose/polyvinylpyrrolidone-enriched hydrogel and the generation of thin tissue sections for analysis. Moreover, we highlight the optimized storage conditions that limit molecular and morphological degradation of the sections. The protocol is compatible with human and plant tissues and can be easily adapted for the preparation of alternative sample formats (e.g., three-dimensional cell cultures). The integrated workflow is universally compatible with histological tissue analysis, mass spectrometry imaging and imaging mass cytometry, as well as spatial proteomic, genomic and transcriptomic tissue analysis. The protocol can be completed within 4 h and requires minimal prior experience in the preparation of tissue samples for multimodal imaging experiments. The morphological and molecular preservation of fresh-frozen tissues is difficult. Embedding with an hydroxypropyl methylcellulose/polyvinylpyrrolidone-rich hydrogel results in a material compatible with spatial biochemical analysis (e.g., mass spectrometry imaging), enabling multimodal data integration.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":13.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141162099","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}
Pub Date : 2024-05-20DOI: 10.1038/s41596-024-01000-3
Devon Kohler, Mateusz Staniak, Fengchao Yu, Alexey I Nesvizhskii, Olga Vitek
Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.
质谱仪和蛋白质组学技术的进步使得进行更大规模、更复杂的实验成为可能。由此产生的数据量和复杂性给下游分析带来了重大挑战。特别是与传统的靶向方法相比，下一代数据独立获取（DIA）实验能够实现更广泛的蛋白质组覆盖，但需要能够管理大得多的数据集并从复杂和重叠的光谱特征中识别肽序列的计算工作流。数据处理工具（如 FragPipe、DIA-NN 和 Spectronaut）在合理时间内处理光谱特征方面有了很大改进。需要使用统计分析工具对实验样本进行有意义的比较，但这些工具最初也是针对较小的数据集设计的。本协议描述了 MSstats 的更新版本，该版本已进行了调整，以兼容大规模 DIA 实验。以一个用 FragPipe 处理的超大型 DIA 实验为例，演示了不同的 MSstats 工作流程。工作流程的选择取决于用户的计算资源。如果数据集过大，标准计算机内存无法容纳，我们将演示 MSstatsBig（MSstats 的配套 R 软件包）的使用。该协议还强调了对分析结果和处理时间有重大影响的关键决策。根据 MSstatsBig 的使用情况，MSstats 处理预计需要 1-3 小时。该协议可在点选式图形用户界面 MSstatsShiny 中运行，也可在 R 语言中以最低限度的专业编码技术实现。
{"title":"An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing.","authors":"Devon Kohler, Mateusz Staniak, Fengchao Yu, Alexey I Nesvizhskii, Olga Vitek","doi":"10.1038/s41596-024-01000-3","DOIUrl":"https://doi.org/10.1038/s41596-024-01000-3","url":null,"abstract":"<p><p>Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.</p>","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":14.8,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071601","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}
Pub Date : 2024-05-20DOI: 10.1038/s41596-024-00985-1
Shashi Gujar, Jonathan G. Pol, Vishnupriyan Kumar, Manuela Lizarralde-Guerrero, Prathyusha Konda, Guido Kroemer, John C. Bell
Oncolytic viruses (OVs) represent a novel class of cancer immunotherapy agents that preferentially infect and kill cancer cells and promote protective antitumor immunity. Furthermore, OVs can be used in combination with established or upcoming immunotherapeutic agents, especially immune checkpoint inhibitors, to efficiently target a wide range of malignancies. The development of OV-based therapy involves three major steps before clinical evaluation: design, production and preclinical testing. OVs can be designed as natural or engineered strains and subsequently selected for their ability to kill a broad spectrum of cancer cells rather than normal, healthy cells. OV selection is further influenced by multiple factors, such as the availability of a specific viral platform, cancer cell permissivity, the need for genetic engineering to render the virus non-pathogenic and/or more effective and logistical considerations around the use of OVs within the laboratory or clinical setting. Selected OVs are then produced and tested for their anticancer potential by using syngeneic, xenograft or humanized preclinical models wherein immunocompromised and immunocompetent setups are used to elucidate their direct oncolytic ability as well as indirect immunotherapeutic potential in vivo. Finally, OVs demonstrating the desired anticancer potential progress toward translation in patients with cancer. This tutorial provides guidelines for the design, production and preclinical testing of OVs, emphasizing considerations specific to OV technology that determine their clinical utility as cancer immunotherapy agents. This tutorial provides guidelines on oncolytic virus design, production and testing in cancer immunotherapy. Best practice recommendations for preclinical and clinical use of oncolytic viruses as an immunotherapy tool and related future challenges are also considered.
肿瘤溶解病毒（OV）是一类新型的癌症免疫治疗药物，它能优先感染和杀死癌细胞，并促进保护性抗肿瘤免疫。此外，OVs 还可与现有或即将推出的免疫治疗药物（尤其是免疫检查点抑制剂）联合使用，有效地针对各种恶性肿瘤。开发基于 OV 的疗法涉及临床评估前的三个主要步骤：设计、生产和临床前测试。OV 可以设计为天然菌株或工程菌株，然后根据其杀死广谱癌细胞而非正常健康细胞的能力进行筛选。OV 的选择还受到多种因素的影响，如特定病毒平台的可用性、癌细胞允许性、是否需要通过基因工程使病毒不致病和/或更有效，以及在实验室或临床环境中使用 OV 的后勤考虑因素。然后，利用免疫功能低下和免疫功能正常的临床前模型，生产和测试选定的 OVs 的抗癌潜力，以阐明它们在体内的直接溶瘤能力和间接免疫治疗潜力。最后，具有理想抗癌潜力的 OV 将在癌症患者身上得到应用。本教程为 OV 的设计、生产和临床前测试提供指导，强调了 OV 技术的具体注意事项，这些注意事项决定了 OV 作为癌症免疫疗法药物的临床用途。
{"title":"Tutorial: design, production and testing of oncolytic viruses for cancer immunotherapy","authors":"Shashi Gujar, Jonathan G. Pol, Vishnupriyan Kumar, Manuela Lizarralde-Guerrero, Prathyusha Konda, Guido Kroemer, John C. Bell","doi":"10.1038/s41596-024-00985-1","DOIUrl":"10.1038/s41596-024-00985-1","url":null,"abstract":"Oncolytic viruses (OVs) represent a novel class of cancer immunotherapy agents that preferentially infect and kill cancer cells and promote protective antitumor immunity. Furthermore, OVs can be used in combination with established or upcoming immunotherapeutic agents, especially immune checkpoint inhibitors, to efficiently target a wide range of malignancies. The development of OV-based therapy involves three major steps before clinical evaluation: design, production and preclinical testing. OVs can be designed as natural or engineered strains and subsequently selected for their ability to kill a broad spectrum of cancer cells rather than normal, healthy cells. OV selection is further influenced by multiple factors, such as the availability of a specific viral platform, cancer cell permissivity, the need for genetic engineering to render the virus non-pathogenic and/or more effective and logistical considerations around the use of OVs within the laboratory or clinical setting. Selected OVs are then produced and tested for their anticancer potential by using syngeneic, xenograft or humanized preclinical models wherein immunocompromised and immunocompetent setups are used to elucidate their direct oncolytic ability as well as indirect immunotherapeutic potential in vivo. Finally, OVs demonstrating the desired anticancer potential progress toward translation in patients with cancer. This tutorial provides guidelines for the design, production and preclinical testing of OVs, emphasizing considerations specific to OV technology that determine their clinical utility as cancer immunotherapy agents. This tutorial provides guidelines on oncolytic virus design, production and testing in cancer immunotherapy. Best practice recommendations for preclinical and clinical use of oncolytic viruses as an immunotherapy tool and related future challenges are also considered.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":13.1,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071553","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}
Pub Date : 2024-05-20DOI: 10.1038/s41596-024-01002-1
Prosenjit Samanta, Samuel F Cooke, Ryan McNulty, Sahand Hormoz, Adam Rosenthal
Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.
{"title":"ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets.","authors":"Prosenjit Samanta, Samuel F Cooke, Ryan McNulty, Sahand Hormoz, Adam Rosenthal","doi":"10.1038/s41596-024-01002-1","DOIUrl":"https://doi.org/10.1038/s41596-024-01002-1","url":null,"abstract":"<p><p>Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.</p>","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":14.8,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071605","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}
Pub Date : 2024-05-20DOI: 10.1038/s41596-024-00996-y
Steffen Heuckeroth, Tito Damiani, Aleksandr Smirnov, Olena Mokshyna, Corinna Brungs, Ansgar Korf, Joshua David Smith, Paolo Stincone, Nicola Dreolin, Louis-Félix Nothias, Tuulia Hyötyläinen, Matej Orešič, Uwe Karst, Pieter C. Dorrestein, Daniel Petras, Xiuxia Du, Justin J. J. van der Hooft, Robin Schmid, Tomáš Pluskal
Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography–MS, gas chromatography–MS and MS–imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography–(IMS–)MS, gas chromatography–MS and (IMS–)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis. Untargeted mass spectrometry (MS) produces complex, multidimensional data. The MZmine open-source project enables processing of spectral data from various MS platforms, e.g., liquid chromatography–MS, gas chromatography–MS, MS–imaging and ion mobility spectrometry–MS, and is specialized for metabolomics.
非靶向质谱（MS）实验会产生复杂的多维数据，而这些数据实际上无法通过人工方式进行研究。因此，需要使用计算管道从原始光谱数据中提取相关信息，并将其转换为更易于理解的格式。根据样品类型和/或研究目标的不同，可使用各种 MS 平台进行此类分析。MZmine 是一款开源软件，用于处理不同质谱平台生成的原始光谱数据。例如液相色谱-质谱、气相色谱-质谱和质谱成像。这些数据通常与代谢组学和脂质组学等各种应用有关。此外，本文介绍的第三版软件还支持离子迁移谱（IMS）数据的处理。本协议提供了三种不同的程序，用于对不同仪器设置产生的非目标质谱数据进行特征检测和注释：液相色谱-（IMS-）质谱、气相色谱-质谱和（IMS-）质谱成像。为便于培训，我们提供了示例数据集和配置批处理文件（即处理步骤和参数列表），以便新用户轻松复制所述工作流程。根据数据文件的数量和可用的计算资源，我们预计新的 MZmine 用户和非专业人员需要 2 到 24 小时才能完成这项工作。在每个程序中，我们都对所有处理参数进行了详细说明，并提供了优化说明/建议。生成的主要输出结果由对齐的特征表和碎片光谱列表表示，可用于其他第三方工具的进一步下游分析。
{"title":"Reproducible mass spectrometry data processing and compound annotation in MZmine 3","authors":"Steffen Heuckeroth, Tito Damiani, Aleksandr Smirnov, Olena Mokshyna, Corinna Brungs, Ansgar Korf, Joshua David Smith, Paolo Stincone, Nicola Dreolin, Louis-Félix Nothias, Tuulia Hyötyläinen, Matej Orešič, Uwe Karst, Pieter C. Dorrestein, Daniel Petras, Xiuxia Du, Justin J. J. van der Hooft, Robin Schmid, Tomáš Pluskal","doi":"10.1038/s41596-024-00996-y","DOIUrl":"10.1038/s41596-024-00996-y","url":null,"abstract":"Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography–MS, gas chromatography–MS and MS–imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography–(IMS–)MS, gas chromatography–MS and (IMS–)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis. Untargeted mass spectrometry (MS) produces complex, multidimensional data. The MZmine open-source project enables processing of spectral data from various MS platforms, e.g., liquid chromatography–MS, gas chromatography–MS, MS–imaging and ion mobility spectrometry–MS, and is specialized for metabolomics.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":13.1,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071608","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}
Pub Date : 2024-05-17DOI: 10.1038/s41596-024-01012-z
Springer Nature recently acquired protocols.io, an open-access platform for developing and sharing protocols, which will replace the Protocol Exchange from June 2024.
{"title":"Welcoming protocols.io","authors":"","doi":"10.1038/s41596-024-01012-z","DOIUrl":"10.1038/s41596-024-01012-z","url":null,"abstract":"Springer Nature recently acquired protocols.io, an open-access platform for developing and sharing protocols, which will replace the Protocol Exchange from June 2024.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":13.1,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41596-024-01012-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-16DOI: 10.1038/s41596-024-01010-1
Sierra N Henson, Evan A Elko, Piotr M Swiderski, Yong Liang, Anna L Engelbrektson, Alejandra Piña, Annalee S Boyle, Zane Fink, Salvatore J Facista, Vidal Martinez, Fatima Rahee, Annabelle Brown, Erin J Kelley, Georgia A Nelson, Isaiah Raspet, Heather L Mead, John A Altin, Jason T Ladner
{"title":"Author Correction: PepSeq: a fully in vitro platform for highly multiplexed serology using customizable DNA-barcoded peptide libraries.","authors":"Sierra N Henson, Evan A Elko, Piotr M Swiderski, Yong Liang, Anna L Engelbrektson, Alejandra Piña, Annalee S Boyle, Zane Fink, Salvatore J Facista, Vidal Martinez, Fatima Rahee, Annabelle Brown, Erin J Kelley, Georgia A Nelson, Isaiah Raspet, Heather L Mead, John A Altin, Jason T Ladner","doi":"10.1038/s41596-024-01010-1","DOIUrl":"https://doi.org/10.1038/s41596-024-01010-1","url":null,"abstract":"","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":14.8,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958365","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}
Pub Date : 2024-05-16DOI: 10.1038/s41596-024-00993-1
Nina Jeliazkova, Eleonora Longhin, Naouale El Yamani, Elise Rundén-Pran, Elisa Moschini, Tommaso Serchi, Ivana Vinković Vrček, Michael J. Burgum, Shareen H. Doak, Mihaela Roxana Cimpan, Ivan Rios-Mondragon, Emil Cimpan, Chiara L. Battistelli, Cecilia Bossa, Rositsa Tsekovska, Damjana Drobne, Sara Novak, Neža Repar, Ammar Ammar, Penny Nymark, Veronica Di Battista, Anita Sosnowska, Tomasz Puzyn, Nikolay Kochev, Luchesar Iliev, Vedrin Jeliazkov, Katie Reilly, Iseult Lynch, Martine Bakker, Camila Delpivo, Araceli Sánchez Jiménez, Ana Sofia Fonseca, Nicolas Manier, María Luisa Fernandez-Cruz, Shahzad Rashid, Egon Willighagen, Margarita D Apostolova, Maria Dusinska
Making research data findable, accessible, interoperable and reusable (FAIR) is typically hampered by a lack of skills in technical aspects of data management by data generators and a lack of resources. We developed a Template Wizard for researchers to easily create templates suitable for consistently capturing data and metadata from their experiments. The templates are easy to use and enable the compilation of machine-readable metadata to accompany data generation and align them to existing community standards and databases, such as eNanoMapper, streamlining the adoption of the FAIR principles. These templates are citable objects and are available as online tools. The Template Wizard is designed to be user friendly and facilitates using and reusing existing templates for new projects or project extensions. The wizard is accompanied by an online template validator, which allows self-evaluation of the template (to ensure mapping to the data schema and machine readability of the captured data) and transformation by an open-source parser into machine-readable formats, compliant with the FAIR principles. The templates are based on extensive collective experience in nanosafety data collection and include over 60 harmonized data entry templates for physicochemical characterization and hazard assessment (cell viability, genotoxicity, environmental organism dose-response tests, omics), as well as exposure and release studies. The templates are generalizable across fields and have already been extended and adapted for microplastics and advanced materials research. The harmonized templates improve the reliability of interlaboratory comparisons, data reuse and meta-analyses and can facilitate the safety evaluation and regulation process for (nano) materials. Community-generated online templates for harmonized data reporting ensure that data and metadata associated with experiments are findable, accessible, interoperable, reusable and compiled for consistency in experimental design and test performance.
{"title":"A template wizard for the cocreation of machine-readable data-reporting to harmonize the evaluation of (nano)materials","authors":"Nina Jeliazkova, Eleonora Longhin, Naouale El Yamani, Elise Rundén-Pran, Elisa Moschini, Tommaso Serchi, Ivana Vinković Vrček, Michael J. Burgum, Shareen H. Doak, Mihaela Roxana Cimpan, Ivan Rios-Mondragon, Emil Cimpan, Chiara L. Battistelli, Cecilia Bossa, Rositsa Tsekovska, Damjana Drobne, Sara Novak, Neža Repar, Ammar Ammar, Penny Nymark, Veronica Di Battista, Anita Sosnowska, Tomasz Puzyn, Nikolay Kochev, Luchesar Iliev, Vedrin Jeliazkov, Katie Reilly, Iseult Lynch, Martine Bakker, Camila Delpivo, Araceli Sánchez Jiménez, Ana Sofia Fonseca, Nicolas Manier, María Luisa Fernandez-Cruz, Shahzad Rashid, Egon Willighagen, Margarita D Apostolova, Maria Dusinska","doi":"10.1038/s41596-024-00993-1","DOIUrl":"10.1038/s41596-024-00993-1","url":null,"abstract":"Making research data findable, accessible, interoperable and reusable (FAIR) is typically hampered by a lack of skills in technical aspects of data management by data generators and a lack of resources. We developed a Template Wizard for researchers to easily create templates suitable for consistently capturing data and metadata from their experiments. The templates are easy to use and enable the compilation of machine-readable metadata to accompany data generation and align them to existing community standards and databases, such as eNanoMapper, streamlining the adoption of the FAIR principles. These templates are citable objects and are available as online tools. The Template Wizard is designed to be user friendly and facilitates using and reusing existing templates for new projects or project extensions. The wizard is accompanied by an online template validator, which allows self-evaluation of the template (to ensure mapping to the data schema and machine readability of the captured data) and transformation by an open-source parser into machine-readable formats, compliant with the FAIR principles. The templates are based on extensive collective experience in nanosafety data collection and include over 60 harmonized data entry templates for physicochemical characterization and hazard assessment (cell viability, genotoxicity, environmental organism dose-response tests, omics), as well as exposure and release studies. The templates are generalizable across fields and have already been extended and adapted for microplastics and advanced materials research. The harmonized templates improve the reliability of interlaboratory comparisons, data reuse and meta-analyses and can facilitate the safety evaluation and regulation process for (nano) materials. Community-generated online templates for harmonized data reporting ensure that data and metadata associated with experiments are findable, accessible, interoperable, reusable and compiled for consistency in experimental design and test performance.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":13.1,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958364","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}
Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder’s adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ). This protocol is for using xMarkerFinder, a four-stage computational framework, to enable the identification and validation of reproducible microbial biomarkers from cross-cohort studies, and establish potential microbiome-induced mechanisms.
{"title":"Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder","authors":"Wenxing Gao, Weili Lin, Qiang Li, Wanning Chen, Wenjing Yin, Xinyue Zhu, Sheng Gao, Lei Liu, Wenjie Li, Dingfeng Wu, Guoqing Zhang, Ruixin Zhu, Na Jiao","doi":"10.1038/s41596-024-00999-9","DOIUrl":"10.1038/s41596-024-00999-9","url":null,"abstract":"Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder’s adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ). This protocol is for using xMarkerFinder, a four-stage computational framework, to enable the identification and validation of reproducible microbial biomarkers from cross-cohort studies, and establish potential microbiome-induced mechanisms.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":13.1,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140922695","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}
Pub Date : 2024-05-13DOI: 10.1038/s41596-024-00980-6
Shuo Han, Emma R. Guiberson, Yuxin Li, Justin L. Sonnenburg
A significant hurdle that has limited progress in microbiome science has been identifying and studying the diverse set of metabolites produced by gut microbes. Gut microbial metabolism produces thousands of difficult-to-identify metabolites, which present a challenge to study their roles in host biology. In recent years, mass spectrometry-based metabolomics has become one of the core technologies for identifying small metabolites. However, metabolomics expertise, ranging from sample preparation to instrument use and data analysis, is often lacking in academic labs. Most targeted metabolomics methods provide high levels of sensitivity and quantification, while they are limited to a panel of predefined molecules that may not be informative to microbiome-focused studies. Here we have developed a gut microbe-focused and wide-spectrum metabolomic protocol using liquid chromatography–mass spectrometry and bioinformatic analysis. This protocol enables users to carry out experiments from sample collection to data analysis, only requiring access to a liquid chromatography–mass spectrometry instrument, which is often available at local core facilities. By applying this protocol to samples containing human gut microbial metabolites, spanning from culture supernatant to human biospecimens, our approach enables high-confidence identification of >800 metabolites that can serve as candidate mediators of microbe–host interactions. We expect this protocol will lower the barrier to tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling hypothesis-driven mechanistic studies and accelerating our understanding of the gut microbiome at the chemical level. This protocol presents a metabolomics method tailored for detecting and measuring gut-microbe-derived metabolites using a broad reference library of metabolite standards.
{"title":"High-throughput identification of gut microbiome-dependent metabolites","authors":"Shuo Han, Emma R. Guiberson, Yuxin Li, Justin L. Sonnenburg","doi":"10.1038/s41596-024-00980-6","DOIUrl":"10.1038/s41596-024-00980-6","url":null,"abstract":"A significant hurdle that has limited progress in microbiome science has been identifying and studying the diverse set of metabolites produced by gut microbes. Gut microbial metabolism produces thousands of difficult-to-identify metabolites, which present a challenge to study their roles in host biology. In recent years, mass spectrometry-based metabolomics has become one of the core technologies for identifying small metabolites. However, metabolomics expertise, ranging from sample preparation to instrument use and data analysis, is often lacking in academic labs. Most targeted metabolomics methods provide high levels of sensitivity and quantification, while they are limited to a panel of predefined molecules that may not be informative to microbiome-focused studies. Here we have developed a gut microbe-focused and wide-spectrum metabolomic protocol using liquid chromatography–mass spectrometry and bioinformatic analysis. This protocol enables users to carry out experiments from sample collection to data analysis, only requiring access to a liquid chromatography–mass spectrometry instrument, which is often available at local core facilities. By applying this protocol to samples containing human gut microbial metabolites, spanning from culture supernatant to human biospecimens, our approach enables high-confidence identification of >800 metabolites that can serve as candidate mediators of microbe–host interactions. We expect this protocol will lower the barrier to tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling hypothesis-driven mechanistic studies and accelerating our understanding of the gut microbiome at the chemical level. This protocol presents a metabolomics method tailored for detecting and measuring gut-microbe-derived metabolites using a broad reference library of metabolite standards.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":null,"pages":null},"PeriodicalIF":13.1,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140916405","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：