Pub Date : 2024-09-20DOI: 10.1038/s41592-024-02411-6
Helen Farrants, Yichun Shuai, William C. Lemon, Christian Monroy Hernandez, Deng Zhang, Shang Yang, Ronak Patel, Guanda Qiao, Michelle S. Frei, Sarah E. Plutkis, Jonathan B. Grimm, Timothy L. Hanson, Filip Tomaska, Glenn C. Turner, Carsen Stringer, Philipp J. Keller, Abraham G. Beyene, Yao Chen, Yajie Liang, Luke D. Lavis, Eric R. Schreiter
Genetically encoded fluorescent calcium indicators allow cellular-resolution recording of physiology. However, bright, genetically targetable indicators that can be multiplexed with existing tools in vivo are needed for simultaneous imaging of multiple signals. Here we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several that efficiently label the brain in animals. When bound to a near-infrared dye-ligand, WHaloCaMP shows a 7× increase in fluorescence intensity and a 2.1-ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a to image Ca2+ responses in vivo in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae and to quantify Ca2+ concentration using fluorescence lifetime imaging microscopy (FLIM). WHaloCaMP is a chemigenetic calcium indicator that can be combined with different rhodamine dyes for multiplexed or FLIM imaging in vivo, as demonstrated for calcium imaging in neuronal cultures, brain slices, Drosophila, zebrafish larvae and the mouse brain.
{"title":"A modular chemigenetic calcium indicator for multiplexed in vivo functional imaging","authors":"Helen Farrants, Yichun Shuai, William C. Lemon, Christian Monroy Hernandez, Deng Zhang, Shang Yang, Ronak Patel, Guanda Qiao, Michelle S. Frei, Sarah E. Plutkis, Jonathan B. Grimm, Timothy L. Hanson, Filip Tomaska, Glenn C. Turner, Carsen Stringer, Philipp J. Keller, Abraham G. Beyene, Yao Chen, Yajie Liang, Luke D. Lavis, Eric R. Schreiter","doi":"10.1038/s41592-024-02411-6","DOIUrl":"10.1038/s41592-024-02411-6","url":null,"abstract":"Genetically encoded fluorescent calcium indicators allow cellular-resolution recording of physiology. However, bright, genetically targetable indicators that can be multiplexed with existing tools in vivo are needed for simultaneous imaging of multiple signals. Here we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several that efficiently label the brain in animals. When bound to a near-infrared dye-ligand, WHaloCaMP shows a 7× increase in fluorescence intensity and a 2.1-ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a to image Ca2+ responses in vivo in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae and to quantify Ca2+ concentration using fluorescence lifetime imaging microscopy (FLIM). WHaloCaMP is a chemigenetic calcium indicator that can be combined with different rhodamine dyes for multiplexed or FLIM imaging in vivo, as demonstrated for calcium imaging in neuronal cultures, brain slices, Drosophila, zebrafish larvae and the mouse brain.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 10","pages":"1916-1925"},"PeriodicalIF":36.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02411-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142291695","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 : 2024-09-19DOI: 10.1038/s41592-024-02378-4
Dinithi Sumanaweera, Chenqu Suo, Ana-Maria Cujba, Daniele Muraro, Emma Dann, Krzysztof Polanski, Alexander S. Steemers, Woochan Lee, Amanda J. Oliver, Jong-Eun Park, Kerstin B. Meyer, Bianca Dumitrascu, Sarah A. Teichmann
Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.
单细胞数据分析可以推断细胞群的动态变化，例如跨时间、跨空间或对扰动的反应，从而得出伪时间轨迹。目前比较轨迹的方法通常使用动态编程，但受到存在确定匹配等假设的限制。在此，我们介绍一种用于排列单细胞轨迹的贝叶斯信息论动态编程框架--Genes2Genes。它能捕捉参考轨迹和查询轨迹之间单个基因的连续匹配和不匹配，突出不同的配准模式群。在真实世界和模拟数据集上，它都能准确地推断出配准，并证明了它在疾病细胞状态轨迹分析中的实用性。在概念验证应用中，Genes2Genes 发现体外分化的 T 细胞与体内未成熟状态相匹配，但缺乏与 TNF 信号转导相关的基因表达。这表明，精确的轨迹比对可以精确定位与体内系统的差异，从而指导体外培养条件的优化。
{"title":"Gene-level alignment of single-cell trajectories","authors":"Dinithi Sumanaweera, Chenqu Suo, Ana-Maria Cujba, Daniele Muraro, Emma Dann, Krzysztof Polanski, Alexander S. Steemers, Woochan Lee, Amanda J. Oliver, Jong-Eun Park, Kerstin B. Meyer, Bianca Dumitrascu, Sarah A. Teichmann","doi":"10.1038/s41592-024-02378-4","DOIUrl":"https://doi.org/10.1038/s41592-024-02378-4","url":null,"abstract":"<p>Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"30 1","pages":""},"PeriodicalIF":48.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263107","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 : 2024-09-18DOI: 10.1038/s41592-024-02404-5
Johannes Roos, Stéphane Bancelin, Tom Delaire, Alexander Wilhelmi, Florian Levet, Maren Engelhardt, Virgile Viasnoff, Rémi Galland, U. Valentin Nägerl, Jean-Baptiste Sibarita
Quantitative microscopy workflows have evolved dramatically over the past years, progressively becoming more complex with the emergence of deep learning. Long-standing challenges such as three-dimensional segmentation of complex microscopy data can finally be addressed, and new imaging modalities are breaking records in both resolution and acquisition speed, generating gigabytes if not terabytes of data per day. With this shift in bioimage workflows comes an increasing need for efficient orchestration and data management, necessitating multitool interoperability and the ability to span dedicated computing resources. However, existing solutions are still limited in their flexibility and scalability and are usually restricted to offline analysis. Here we introduce Arkitekt, an open-source middleman between users and bioimage apps that enables complex quantitative microscopy workflows in real time. It allows the orchestration of popular bioimage software locally or remotely in a reliable and efficient manner. It includes visualization and analysis modules, but also mechanisms to execute source code and pilot acquisition software, making ‘smart microscopy’ a reality. Arkitekt is an open-source platform that facilitates the implementation of complex quantitative bioimaging workflows in real time, from acquisition to visualization and analysis.
{"title":"Arkitekt: streaming analysis and real-time workflows for microscopy","authors":"Johannes Roos, Stéphane Bancelin, Tom Delaire, Alexander Wilhelmi, Florian Levet, Maren Engelhardt, Virgile Viasnoff, Rémi Galland, U. Valentin Nägerl, Jean-Baptiste Sibarita","doi":"10.1038/s41592-024-02404-5","DOIUrl":"10.1038/s41592-024-02404-5","url":null,"abstract":"Quantitative microscopy workflows have evolved dramatically over the past years, progressively becoming more complex with the emergence of deep learning. Long-standing challenges such as three-dimensional segmentation of complex microscopy data can finally be addressed, and new imaging modalities are breaking records in both resolution and acquisition speed, generating gigabytes if not terabytes of data per day. With this shift in bioimage workflows comes an increasing need for efficient orchestration and data management, necessitating multitool interoperability and the ability to span dedicated computing resources. However, existing solutions are still limited in their flexibility and scalability and are usually restricted to offline analysis. Here we introduce Arkitekt, an open-source middleman between users and bioimage apps that enables complex quantitative microscopy workflows in real time. It allows the orchestration of popular bioimage software locally or remotely in a reliable and efficient manner. It includes visualization and analysis modules, but also mechanisms to execute source code and pilot acquisition software, making ‘smart microscopy’ a reality. Arkitekt is an open-source platform that facilitates the implementation of complex quantitative bioimaging workflows in real time, from acquisition to visualization and analysis.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 10","pages":"1884-1894"},"PeriodicalIF":36.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269621","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 : 2024-09-18DOI: 10.1038/s41592-024-02412-5
Camilla Bosone, Davide Castaldi, Thomas Rainer Burkard, Segundo Jose Guzman, Tom Wyatt, Cristina Cheroni, Nicolò Caporale, Sunanjay Bajaj, Joshua Adam Bagley, Chong Li, Benoit Sorre, Carlo Emanuele Villa, Giuseppe Testa, Veronica Krenn, Jürgen Arthur Knoblich
Organoids generating major cortical cell types in distinct compartments are used to study cortical development, evolution and disorders. However, the lack of morphogen gradients imparting cortical positional information and topography in current systems hinders the investigation of complex phenotypes. Here, we engineer human cortical assembloids by fusing an organizer-like structure expressing fibroblast growth factor 8 (FGF8) with an elongated organoid to enable the controlled modulation of FGF8 signaling along the longitudinal organoid axis. These polarized cortical assembloids mount a position-dependent transcriptional program that in part matches the in vivo rostrocaudal gene expression patterns and that is lost upon mutation in the FGFR3 gene associated with temporal lobe malformations and intellectual disability. By producing spatially oriented cell populations with signatures related to frontal and temporal area identity within individual assembloids, this model recapitulates in part the early transcriptional divergence embedded in the protomap and enables the study of cortical area-relevant alterations underlying human disorders. Cortical development is influenced by morphogen gradients. To mimic patterning events during brain development, polarized cortical assembloids are generated with the help of a localized FGF8 source.
{"title":"A polarized FGF8 source specifies frontotemporal signatures in spatially oriented cell populations of cortical assembloids","authors":"Camilla Bosone, Davide Castaldi, Thomas Rainer Burkard, Segundo Jose Guzman, Tom Wyatt, Cristina Cheroni, Nicolò Caporale, Sunanjay Bajaj, Joshua Adam Bagley, Chong Li, Benoit Sorre, Carlo Emanuele Villa, Giuseppe Testa, Veronica Krenn, Jürgen Arthur Knoblich","doi":"10.1038/s41592-024-02412-5","DOIUrl":"10.1038/s41592-024-02412-5","url":null,"abstract":"Organoids generating major cortical cell types in distinct compartments are used to study cortical development, evolution and disorders. However, the lack of morphogen gradients imparting cortical positional information and topography in current systems hinders the investigation of complex phenotypes. Here, we engineer human cortical assembloids by fusing an organizer-like structure expressing fibroblast growth factor 8 (FGF8) with an elongated organoid to enable the controlled modulation of FGF8 signaling along the longitudinal organoid axis. These polarized cortical assembloids mount a position-dependent transcriptional program that in part matches the in vivo rostrocaudal gene expression patterns and that is lost upon mutation in the FGFR3 gene associated with temporal lobe malformations and intellectual disability. By producing spatially oriented cell populations with signatures related to frontal and temporal area identity within individual assembloids, this model recapitulates in part the early transcriptional divergence embedded in the protomap and enables the study of cortical area-relevant alterations underlying human disorders. Cortical development is influenced by morphogen gradients. To mimic patterning events during brain development, polarized cortical assembloids are generated with the help of a localized FGF8 source.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 11","pages":"2147-2159"},"PeriodicalIF":36.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02412-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269623","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 : 2024-09-18DOI: 10.1038/s41592-024-02409-0
Seulki Kwon, Jordan Safer, Duyen T. Nguyen, David Hoksza, Patrick May, Jeremy A. Arbesfeld, Alan F. Rubin, Arthur J. Campbell, Alex Burgin, Sumaiya Iqbal
Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics have generated genetic variants at an unprecedented scale. However, efficient tools and resources are needed to link disparate data types—to ‘map’ variants onto protein structures, to better understand how the variation causes disease, and thereby design therapeutics. Here we present the Genomics 2 Proteins portal ( https://g2p.broadinstitute.org/ ): a human proteome-wide resource that maps 20,076,998 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the Genomics 2 Proteins portal allows users to interactively upload protein residue-wise annotations (for example, variants and scores) as well as the protein structure beyond databases to establish the connection between genomics to proteins. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure–function relationship between natural or synthetic variations and their molecular phenotypes. The Genomics 2 Proteins portal is an open-source tool for proteome-wide linking of human genetic variants to protein sequences and structures. The portal serves as a discovery tool to hypothesize the structure–function relationship between natural or synthetic variations and their molecular phenotypes.
{"title":"Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures","authors":"Seulki Kwon, Jordan Safer, Duyen T. Nguyen, David Hoksza, Patrick May, Jeremy A. Arbesfeld, Alan F. Rubin, Arthur J. Campbell, Alex Burgin, Sumaiya Iqbal","doi":"10.1038/s41592-024-02409-0","DOIUrl":"10.1038/s41592-024-02409-0","url":null,"abstract":"Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics have generated genetic variants at an unprecedented scale. However, efficient tools and resources are needed to link disparate data types—to ‘map’ variants onto protein structures, to better understand how the variation causes disease, and thereby design therapeutics. Here we present the Genomics 2 Proteins portal ( https://g2p.broadinstitute.org/ ): a human proteome-wide resource that maps 20,076,998 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the Genomics 2 Proteins portal allows users to interactively upload protein residue-wise annotations (for example, variants and scores) as well as the protein structure beyond databases to establish the connection between genomics to proteins. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure–function relationship between natural or synthetic variations and their molecular phenotypes. The Genomics 2 Proteins portal is an open-source tool for proteome-wide linking of human genetic variants to protein sequences and structures. The portal serves as a discovery tool to hypothesize the structure–function relationship between natural or synthetic variations and their molecular phenotypes.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 10","pages":"1947-1957"},"PeriodicalIF":36.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02409-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263109","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 : 2024-09-18DOI: 10.1038/s41592-024-02410-7
Zefang Tang, Shuchen Luo, Hu Zeng, Jiahao Huang, Xin Sui, Morgan Wu, Xiao Wang
Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (cross-sample alignment of spatial omics), a deep graph neural network-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST further allows spatially resolved differential analysis (∆Analysis) to pinpoint and visualize disease-associated molecular pathways and cell–cell interactions and single-cell relative translational efficiency profiling to reveal variations in translational control across cell types and regions. CAST serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities and sample conditions. CAST is a deep learning-based method that enables across-sample searching and matching based on spatial molecular features and reconstructing spatially resolved single-cell multi-omic profiles, as well as supports downstream differential analysis.
{"title":"Search and match across spatial omics samples at single-cell resolution","authors":"Zefang Tang, Shuchen Luo, Hu Zeng, Jiahao Huang, Xin Sui, Morgan Wu, Xiao Wang","doi":"10.1038/s41592-024-02410-7","DOIUrl":"10.1038/s41592-024-02410-7","url":null,"abstract":"Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (cross-sample alignment of spatial omics), a deep graph neural network-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST further allows spatially resolved differential analysis (∆Analysis) to pinpoint and visualize disease-associated molecular pathways and cell–cell interactions and single-cell relative translational efficiency profiling to reveal variations in translational control across cell types and regions. CAST serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities and sample conditions. CAST is a deep learning-based method that enables across-sample searching and matching based on spatial molecular features and reconstructing spatially resolved single-cell multi-omic profiles, as well as supports downstream differential analysis.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 10","pages":"1818-1829"},"PeriodicalIF":36.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269619","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 : 2024-09-18DOI: 10.1038/s41592-024-02432-1
Vivien Marx
Developing new methods takes passion and a penchant for risk-taking.
开发新方法需要激情和冒险精神。
{"title":"Why they take risks","authors":"Vivien Marx","doi":"10.1038/s41592-024-02432-1","DOIUrl":"10.1038/s41592-024-02432-1","url":null,"abstract":"Developing new methods takes passion and a penchant for risk-taking.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 10","pages":"1766-1766"},"PeriodicalIF":36.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263108","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 : 2024-09-16DOI: 10.1038/s41592-024-02405-4
Christoph F. Kurz, Martin Krzywinski, Naomi Altman
I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player.
我在赛点上运气不好。拉斐尔-纳达尔，西班牙网球运动员。
{"title":"Propensity score matching","authors":"Christoph F. Kurz, Martin Krzywinski, Naomi Altman","doi":"10.1038/s41592-024-02405-4","DOIUrl":"10.1038/s41592-024-02405-4","url":null,"abstract":"I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 10","pages":"1770-1772"},"PeriodicalIF":36.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269622","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 : 2024-09-13DOI: 10.1038/s41592-024-02384-6
Ho Thuy Dung Nguyen, Gaia Perone, Nikolai Klena, Roberta Vazzana, Flaminia Kaluthantrige Don, Malan Silva, Simona Sorrentino, Paolo Swuec, Frederic Leroux, Nereo Kalebic, Francesca Coscia, Philipp S. Erdmann
Cryo-focused ion beam milling has substantially advanced our understanding of molecular processes by opening windows into cells. However, applying this technique to complex samples, such as tissues, has presented considerable technical challenges. Here we introduce an innovative adaptation of the cryo-lift-out technique, serialized on-grid lift-in sectioning for tomography (SOLIST), addressing these limitations. SOLIST enhances throughput, minimizes ice contamination and improves sample stability for cryo-electron tomography. It thereby facilitates the high-resolution imaging of a wide range of specimens. We illustrate these advantages on reconstituted liquid–liquid phase-separated droplets, brain organoids and native tissues from the mouse brain, liver and heart. With SOLIST, cellular processes can now be investigated at molecular resolution directly in native tissue. Furthermore, our method has a throughput high enough to render cryo-lift-out a competitive tool for structural biology. This opens new avenues for unprecedented insights into cellular function and structure in health and disease, a ‘biopsy at the nanoscale’. Serialized on-grid lift-in sectioning for tomography (SOLIST) improves the throughput of the serial lift-out technique for creating lamellas, addressing a major bottleneck in the use of cryo-electron tomography for in situ structural biology.
{"title":"Serialized on-grid lift-in sectioning for tomography (SOLIST) enables a biopsy at the nanoscale","authors":"Ho Thuy Dung Nguyen, Gaia Perone, Nikolai Klena, Roberta Vazzana, Flaminia Kaluthantrige Don, Malan Silva, Simona Sorrentino, Paolo Swuec, Frederic Leroux, Nereo Kalebic, Francesca Coscia, Philipp S. Erdmann","doi":"10.1038/s41592-024-02384-6","DOIUrl":"10.1038/s41592-024-02384-6","url":null,"abstract":"Cryo-focused ion beam milling has substantially advanced our understanding of molecular processes by opening windows into cells. However, applying this technique to complex samples, such as tissues, has presented considerable technical challenges. Here we introduce an innovative adaptation of the cryo-lift-out technique, serialized on-grid lift-in sectioning for tomography (SOLIST), addressing these limitations. SOLIST enhances throughput, minimizes ice contamination and improves sample stability for cryo-electron tomography. It thereby facilitates the high-resolution imaging of a wide range of specimens. We illustrate these advantages on reconstituted liquid–liquid phase-separated droplets, brain organoids and native tissues from the mouse brain, liver and heart. With SOLIST, cellular processes can now be investigated at molecular resolution directly in native tissue. Furthermore, our method has a throughput high enough to render cryo-lift-out a competitive tool for structural biology. This opens new avenues for unprecedented insights into cellular function and structure in health and disease, a ‘biopsy at the nanoscale’. Serialized on-grid lift-in sectioning for tomography (SOLIST) improves the throughput of the serial lift-out technique for creating lamellas, addressing a major bottleneck in the use of cryo-electron tomography for in situ structural biology.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 9","pages":"1693-1701"},"PeriodicalIF":36.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02384-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231138","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}
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