Pub Date : 2025-12-22DOI: 10.1038/s41592-025-02973-z
Gabriel Studer, Xavier Robin, Stefan Bienert, Janani Durairaj, Peter Škrinjar, Gerardo Tauriello, Andrew Mark Waterhouse, Torsten Schwede
Protein structure prediction has a long history of benchmarking efforts such as critical assessment of structure prediction, continuous automated model evaluation and critical assessment of prediction of interactions. With the rise of artificial intelligence-based methods for prediction of macromolecular complexes, benchmarking with large datasets and robust, unsupervised scores to compare predictions against a reference has become essential. Also, the increasing size and complexity of experimentally determined reference structures by crystallography or cryogenic electron microscopy poses challenges for structure comparison methods. Here we review the current state of the art in scoring methodologies, identify existing limitations and present more suitable approaches for scoring of tertiary and quaternary structures, protein–protein interfaces and protein–ligand complexes. Our methods are designed to scale efficiently, enabling the assessment of large, complex systems. All developments are available in the structure benchmarking framework of OpenStructure. OpenStructure is open source software and available for free at https://openstructure.org/ . This study advances methods for benchmarking macromolecular complex predictions by introducing a scalable open-source framework used in recent community assessments to compare structures, interfaces and ligand interactions against experimental data.
{"title":"A fully automated benchmarking suite to compare macromolecular complexes","authors":"Gabriel Studer, Xavier Robin, Stefan Bienert, Janani Durairaj, Peter Škrinjar, Gerardo Tauriello, Andrew Mark Waterhouse, Torsten Schwede","doi":"10.1038/s41592-025-02973-z","DOIUrl":"10.1038/s41592-025-02973-z","url":null,"abstract":"Protein structure prediction has a long history of benchmarking efforts such as critical assessment of structure prediction, continuous automated model evaluation and critical assessment of prediction of interactions. With the rise of artificial intelligence-based methods for prediction of macromolecular complexes, benchmarking with large datasets and robust, unsupervised scores to compare predictions against a reference has become essential. Also, the increasing size and complexity of experimentally determined reference structures by crystallography or cryogenic electron microscopy poses challenges for structure comparison methods. Here we review the current state of the art in scoring methodologies, identify existing limitations and present more suitable approaches for scoring of tertiary and quaternary structures, protein–protein interfaces and protein–ligand complexes. Our methods are designed to scale efficiently, enabling the assessment of large, complex systems. All developments are available in the structure benchmarking framework of OpenStructure. OpenStructure is open source software and available for free at https://openstructure.org/ . This study advances methods for benchmarking macromolecular complex predictions by introducing a scalable open-source framework used in recent community assessments to compare structures, interfaces and ligand interactions against experimental data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"387-394"},"PeriodicalIF":32.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02973-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145810688","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 : 2025-12-19DOI: 10.1038/s41592-025-02960-4
Nitsan Elmalam, Assaf Zaritsky
The in silico labeling prediction of organelle fluorescence from label-free microscopy images has the potential to revolutionize our understanding of cells as integrated complex systems. However, out-of-distribution data caused by changes in the intracellular organization across cell types, cellular processes or perturbations can lead to altered label-free images and impaired in silico labeling. Here we demonstrate that incorporating biological meaningful cell contexts, via a context-dependent model that we call CELTIC, enhanced in silico labeling prediction and enabled the downstream analysis of out-of-distribution data such as cells undergoing mitosis and cells located at the edge of the colony. These results suggest a link between cell context and intracellular organization. Using CELTIC to generate single-cell images transitioning between different contexts enabled us to overcome intercell variability toward the integrated characterization of organelles’ alterations in cellular organization. The explicit inclusion of context has the potential to harmonize multiple datasets, paving the way for generalized in silico labeling foundation models. CELTIC is a context-dependent deep learning model for in silico organelle localization that uses biological priors to enable the generalized and harmonized prediction of organelle fluorescence from label-free images.
{"title":"Cell context-dependent in silico organelle localization in label-free microscopy images","authors":"Nitsan Elmalam, Assaf Zaritsky","doi":"10.1038/s41592-025-02960-4","DOIUrl":"10.1038/s41592-025-02960-4","url":null,"abstract":"The in silico labeling prediction of organelle fluorescence from label-free microscopy images has the potential to revolutionize our understanding of cells as integrated complex systems. However, out-of-distribution data caused by changes in the intracellular organization across cell types, cellular processes or perturbations can lead to altered label-free images and impaired in silico labeling. Here we demonstrate that incorporating biological meaningful cell contexts, via a context-dependent model that we call CELTIC, enhanced in silico labeling prediction and enabled the downstream analysis of out-of-distribution data such as cells undergoing mitosis and cells located at the edge of the colony. These results suggest a link between cell context and intracellular organization. Using CELTIC to generate single-cell images transitioning between different contexts enabled us to overcome intercell variability toward the integrated characterization of organelles’ alterations in cellular organization. The explicit inclusion of context has the potential to harmonize multiple datasets, paving the way for generalized in silico labeling foundation models. CELTIC is a context-dependent deep learning model for in silico organelle localization that uses biological priors to enable the generalized and harmonized prediction of organelle fluorescence from label-free images.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"405-416"},"PeriodicalIF":32.1,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41592-025-02960-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145794209","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 : 2025-12-18DOI: 10.1038/s41592-025-02935-5
Pei-Hsun Wu, Jude M. Phillip, Wenxuan Du, Andre Forjaz, Praful R. Nair, Denis Wirtz
Cell migration assays provide invaluable insights into fundamental biological processes. In a companion Review, we describe commercial and custom in vitro and in vivo assays to measure cell migration and provide guidelines on how to select the most appropriate assay for a given biological question. Here, we describe the fundamental principles of how to compute—from the raw data generated by these assays—quantitative cell migration parameters that help determine the biophysical nature of the cell migration, such as cell speed, mean-squared displacement, diffusivity, persistence, speed and anisotropy, and how to quantify cell heterogeneity, with practical guidance. We also describe new imaging and computational technologies, including AI-based methods, which have helped establish fast, robust and accurate tracking of cells and quantification of cell migration. Taken together, these Reviews offer practical guidance for cell migration assays from conception to analysis. This Review describes the principles of data analysis for extracting quantitative data from cell migration assays. It also highlights advanced image analysis tools and offers practical guidance for interested users.
{"title":"Methods to analyze cell migration data: fundamentals and practical guidelines","authors":"Pei-Hsun Wu, Jude M. Phillip, Wenxuan Du, Andre Forjaz, Praful R. Nair, Denis Wirtz","doi":"10.1038/s41592-025-02935-5","DOIUrl":"10.1038/s41592-025-02935-5","url":null,"abstract":"Cell migration assays provide invaluable insights into fundamental biological processes. In a companion Review, we describe commercial and custom in vitro and in vivo assays to measure cell migration and provide guidelines on how to select the most appropriate assay for a given biological question. Here, we describe the fundamental principles of how to compute—from the raw data generated by these assays—quantitative cell migration parameters that help determine the biophysical nature of the cell migration, such as cell speed, mean-squared displacement, diffusivity, persistence, speed and anisotropy, and how to quantify cell heterogeneity, with practical guidance. We also describe new imaging and computational technologies, including AI-based methods, which have helped establish fast, robust and accurate tracking of cells and quantification of cell migration. Taken together, these Reviews offer practical guidance for cell migration assays from conception to analysis. This Review describes the principles of data analysis for extracting quantitative data from cell migration assays. It also highlights advanced image analysis tools and offers practical guidance for interested users.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"43-55"},"PeriodicalIF":32.1,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145781473","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 : 2025-12-18DOI: 10.1038/s41592-025-02968-w
Grant Kinsler, Caitlin Fagan, Haiyin Li, Jessica Kaster, Maggie Dunne, Robert J. Vander Velde, Ryan H. Boe, Sydney Shaffer, Meenhard Herlyn, Arjun Raj, Yael Heyman
Imaging-based spatial transcriptomics methods allow for the measurement of spatial determinants of cellular phenotypes but are incompatible with random barcode-based clone-tracing methods, preventing the simultaneous detection of clonal and spatial drivers. Here we report SpaceBar, which enables simultaneous clone tracing and spatial gene expression profiling with standard imaging-based spatial transcriptomics pipelines. Our approach uses a library of 96 synthetic barcode sequences that combinatorially labels each cell. Thus, SpaceBar can distinguish between clonal dynamics and environmentally driven transcriptional regulation in complex tissue contexts. SpaceBar is a cellular barcoding strategy for simultaneous analysis of cell clonal and spatial identities.
{"title":"SpaceBar enables single-cell-resolution clone tracing with imaging-based spatial transcriptomics","authors":"Grant Kinsler, Caitlin Fagan, Haiyin Li, Jessica Kaster, Maggie Dunne, Robert J. Vander Velde, Ryan H. Boe, Sydney Shaffer, Meenhard Herlyn, Arjun Raj, Yael Heyman","doi":"10.1038/s41592-025-02968-w","DOIUrl":"10.1038/s41592-025-02968-w","url":null,"abstract":"Imaging-based spatial transcriptomics methods allow for the measurement of spatial determinants of cellular phenotypes but are incompatible with random barcode-based clone-tracing methods, preventing the simultaneous detection of clonal and spatial drivers. Here we report SpaceBar, which enables simultaneous clone tracing and spatial gene expression profiling with standard imaging-based spatial transcriptomics pipelines. Our approach uses a library of 96 synthetic barcode sequences that combinatorially labels each cell. Thus, SpaceBar can distinguish between clonal dynamics and environmentally driven transcriptional regulation in complex tissue contexts. SpaceBar is a cellular barcoding strategy for simultaneous analysis of cell clonal and spatial identities.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"328-333"},"PeriodicalIF":32.1,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145781497","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 : 2025-12-18DOI: 10.1038/s41592-025-02890-1
Wenxuan Du, Praful R. Nair, Andre Forjaz, Jude M. Phillip, Pei-Hsun Wu, Denis Wirtz
Cell migration is a key cellular process that drives major developmental programs. To mimic and mechanistically understand cell migration in these different contexts, different assays have been developed. However, owing to the lack of practical guidelines, these different cell migration assays are often used interchangeably. This and the inherent dynamic nature of cell migration, which often requires sophisticated live-cell microscopy, may have caused cell migration to be notably less well understood than equally important cell functions, such as cell differentiation or proliferation. In this Review, we describe commonly used custom and commercial in vitro and in vivo cell migration assays and provide a comprehensive practical guide and decision tree outlining how to choose and implement an assay that best suits the biological question at hand. We hope this guidance spurs biological insights into this complex process and encourages future methods development. This Review introduces ten cell migration assays, offers practical guidance toward selecting the best assay for a specific biological question and describes how future advances can reveal important insights into dynamic cellular behaviors.
{"title":"Selecting the optimal cell migration assay: fundamentals and practical guidelines","authors":"Wenxuan Du, Praful R. Nair, Andre Forjaz, Jude M. Phillip, Pei-Hsun Wu, Denis Wirtz","doi":"10.1038/s41592-025-02890-1","DOIUrl":"10.1038/s41592-025-02890-1","url":null,"abstract":"Cell migration is a key cellular process that drives major developmental programs. To mimic and mechanistically understand cell migration in these different contexts, different assays have been developed. However, owing to the lack of practical guidelines, these different cell migration assays are often used interchangeably. This and the inherent dynamic nature of cell migration, which often requires sophisticated live-cell microscopy, may have caused cell migration to be notably less well understood than equally important cell functions, such as cell differentiation or proliferation. In this Review, we describe commonly used custom and commercial in vitro and in vivo cell migration assays and provide a comprehensive practical guide and decision tree outlining how to choose and implement an assay that best suits the biological question at hand. We hope this guidance spurs biological insights into this complex process and encourages future methods development. This Review introduces ten cell migration assays, offers practical guidance toward selecting the best assay for a specific biological question and describes how future advances can reveal important insights into dynamic cellular behaviors.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"30-42"},"PeriodicalIF":32.1,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145781537","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 : 2025-12-17DOI: 10.1038/s41592-025-02905-x
Tien G. Pham, Omoyemi Ajayi, Jiaze He, Irina Sagarbarria, Jeanne A. Hardy, Jiahui Wu
RNA is one of the key molecules responsible for controlling gene expression regulation, and visualizing individual RNA molecules in living cells offers unique insights into the dynamics of this process. Recently, the RNA-regulated destabilization domain was developed for live-cell imaging of single RNA. However, this method is limited to single-color RNA imaging and its long RNA tag induces destabilization of the tagged RNA. Here we describe two orthogonal RNA-regulated destabilization domains (mDeg and pDeg) that enable three-color messenger RNA (mRNA) imaging in living cells. We show that these destabilization domains can image mRNA tethered to the endoplasmic reticulum membrane, the inner surface of the plasma membrane and in the cytosol. In addition, we show that mDeg can detect mRNA more effectively than the previously reported tDeg system. Moreover, mDeg can be combined with a short RNA tag (9XMS2) for single-molecule RNA imaging without perturbation of RNA stability. This work presents two distinct RNA-regulated destabilization domains that support three-color live-cell imaging of single mRNA molecules, one of which shows minimal impact on RNA stability.
{"title":"Orthogonal RNA-regulated destabilization domains for three-color RNA imaging with minimal RNA perturbation","authors":"Tien G. Pham, Omoyemi Ajayi, Jiaze He, Irina Sagarbarria, Jeanne A. Hardy, Jiahui Wu","doi":"10.1038/s41592-025-02905-x","DOIUrl":"10.1038/s41592-025-02905-x","url":null,"abstract":"RNA is one of the key molecules responsible for controlling gene expression regulation, and visualizing individual RNA molecules in living cells offers unique insights into the dynamics of this process. Recently, the RNA-regulated destabilization domain was developed for live-cell imaging of single RNA. However, this method is limited to single-color RNA imaging and its long RNA tag induces destabilization of the tagged RNA. Here we describe two orthogonal RNA-regulated destabilization domains (mDeg and pDeg) that enable three-color messenger RNA (mRNA) imaging in living cells. We show that these destabilization domains can image mRNA tethered to the endoplasmic reticulum membrane, the inner surface of the plasma membrane and in the cytosol. In addition, we show that mDeg can detect mRNA more effectively than the previously reported tDeg system. Moreover, mDeg can be combined with a short RNA tag (9XMS2) for single-molecule RNA imaging without perturbation of RNA stability. This work presents two distinct RNA-regulated destabilization domains that support three-color live-cell imaging of single mRNA molecules, one of which shows minimal impact on RNA stability.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"165-174"},"PeriodicalIF":32.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775011","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 : 2025-12-17DOI: 10.1038/s41592-025-02869-y
Gal Haimovich
The gold standard system for mRNA imaging in live cells just got upgraded by being degraded. This development improves signal-to-noise ratios and may lead to a better understanding of mRNA transport and localization.
{"title":"Targeted RNA-binding protein degradation to improve mRNA live imaging in animal cells","authors":"Gal Haimovich","doi":"10.1038/s41592-025-02869-y","DOIUrl":"10.1038/s41592-025-02869-y","url":null,"abstract":"The gold standard system for mRNA imaging in live cells just got upgraded by being degraded. This development improves signal-to-noise ratios and may lead to a better understanding of mRNA transport and localization.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 1","pages":"18-19"},"PeriodicalIF":32.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145774967","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 : 2025-12-17DOI: 10.1038/s41592-025-02924-8
Divya Koyyalagunta, Karuna Ganesh, Quaid Morris
Cancers differ in how they spread. These routes of metastatic dissemination can be reconstructed from tumor sequencing data, but current reconstruction methods scale poorly or rely on assumptions that do not reflect known biology. Metient overcomes these limitations using gradient-based, multiobjective optimization to generate multiple hypotheses of metastatic spread that are rescored using independent genetic distance and organotropism data. Unlike current methods, Metient can be used with both clinical sequencing data and barcode-based lineage tracing in preclinical models. Here, applied to data from 167 patients and 479 tumors, Metient identifies distinct trends of metastatic dissemination in melanoma, high-risk neuroblastoma and non-small cell lung cancer. Its reconstructions usually match expert analyses but Metient often finds other plausible migration histories, ultimately positing more polyclonal and metastasis-to-metastasis seeding than previously reported. Metient's reconstructions thus challenge existing assumptions about metastatic dissemination and offer insights into cancer type-specific patterns of metastatic spread.
{"title":"Inferring cancer type-specific patterns of metastatic spread using Metient.","authors":"Divya Koyyalagunta, Karuna Ganesh, Quaid Morris","doi":"10.1038/s41592-025-02924-8","DOIUrl":"10.1038/s41592-025-02924-8","url":null,"abstract":"<p><p>Cancers differ in how they spread. These routes of metastatic dissemination can be reconstructed from tumor sequencing data, but current reconstruction methods scale poorly or rely on assumptions that do not reflect known biology. Metient overcomes these limitations using gradient-based, multiobjective optimization to generate multiple hypotheses of metastatic spread that are rescored using independent genetic distance and organotropism data. Unlike current methods, Metient can be used with both clinical sequencing data and barcode-based lineage tracing in preclinical models. Here, applied to data from 167 patients and 479 tumors, Metient identifies distinct trends of metastatic dissemination in melanoma, high-risk neuroblastoma and non-small cell lung cancer. Its reconstructions usually match expert analyses but Metient often finds other plausible migration histories, ultimately positing more polyclonal and metastasis-to-metastasis seeding than previously reported. Metient's reconstructions thus challenge existing assumptions about metastatic dissemination and offer insights into cancer type-specific patterns of metastatic spread.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":32.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775052","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}
Spatial omics face challenges in achieving high-parameter, multi-omics coprofiling. Serial-section profiling of complementary panels mitigates technical trade-offs but introduces the spatial diagonal integration problem. To address this, here we present SpatialEx and its extension SpatialEx+, computational frameworks leveraging histology as a universal anchor to integrate spatial molecular data across tissue sections. SpatialEx combines a pretrained hematoxylin and eosin foundation model with hypergraph and contrastive learning to predict single-cell omics from histology, encoding multi-neighborhood spatial dependencies and global tissue context. SpatialEx+ further introduces an omics cycle module that encourages cross-omics consistency via slice-invariant mappings, enabling seamless integration without comeasured training data. Extensive validations show superior hematoxylin and eosin-to-omics prediction, panel diagonal integration and omics diagonal integration across various biological scenarios. The frameworks scale to datasets exceeding 1 million cells, maintain robustness with nonoverlapping or heterogeneous sections and support unlimited omics layers in principle. Our work makes multimodal spatial profiling broadly accessible. The SpatialEx(+) framework uses histology to generate and integrate complementary spatial multi-omics data across samples.
{"title":"High-parameter spatial multi-omics through histology-anchored integration","authors":"Yonghao Liu, Chuyao Wang, Zhikang Wang, Liang Chen, Zhi Li, Jiangning Song, Qi Zou, Rui Gao, Bin-Zhi Qian, Xiaoyue Feng, Renchu Guan, Zhiyuan Yuan","doi":"10.1038/s41592-025-02926-6","DOIUrl":"10.1038/s41592-025-02926-6","url":null,"abstract":"Spatial omics face challenges in achieving high-parameter, multi-omics coprofiling. Serial-section profiling of complementary panels mitigates technical trade-offs but introduces the spatial diagonal integration problem. To address this, here we present SpatialEx and its extension SpatialEx+, computational frameworks leveraging histology as a universal anchor to integrate spatial molecular data across tissue sections. SpatialEx combines a pretrained hematoxylin and eosin foundation model with hypergraph and contrastive learning to predict single-cell omics from histology, encoding multi-neighborhood spatial dependencies and global tissue context. SpatialEx+ further introduces an omics cycle module that encourages cross-omics consistency via slice-invariant mappings, enabling seamless integration without comeasured training data. Extensive validations show superior hematoxylin and eosin-to-omics prediction, panel diagonal integration and omics diagonal integration across various biological scenarios. The frameworks scale to datasets exceeding 1 million cells, maintain robustness with nonoverlapping or heterogeneous sections and support unlimited omics layers in principle. Our work makes multimodal spatial profiling broadly accessible. The SpatialEx(+) framework uses histology to generate and integrate complementary spatial multi-omics data across samples.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"373-386"},"PeriodicalIF":32.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145774981","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 : 2025-12-16DOI: 10.1038/s41592-025-02959-x
We systematically mapped how morphogen timing and dosage shape early cell fate specification in human neural organoids to unify our understanding of neural regionalization and to accelerate development of organoid models. This defined competence windows for key signaling pathways and benchmarked variability across human pluripotent stem cell lines and protocols.
{"title":"Dissecting how morphogens shape cell fates in human neural organoids","authors":"","doi":"10.1038/s41592-025-02959-x","DOIUrl":"10.1038/s41592-025-02959-x","url":null,"abstract":"We systematically mapped how morphogen timing and dosage shape early cell fate specification in human neural organoids to unify our understanding of neural regionalization and to accelerate development of organoid models. This defined competence windows for key signaling pathways and benchmarked variability across human pluripotent stem cell lines and protocols.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"23 2","pages":"297-298"},"PeriodicalIF":32.1,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145768664","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：