Pub Date : 2025-03-17DOI: 10.1038/s41592-025-02618-1
Adrien Hallou, Ruiyang He, Benjamin D Simons, Bianca Dumitrascu
Advances in spatial profiling technologies are providing insights into how molecular programs are influenced by local signaling and environmental cues. However, cell fate specification and tissue patterning involve the interplay of biochemical and mechanical feedback. Here we develop a computational framework that enables the joint statistical analysis of transcriptional and mechanical signals in the context of spatial transcriptomics. To illustrate the application and utility of the approach, we use spatial transcriptomics data from the developing mouse embryo to infer the forces acting on individual cells, and use these results to identify mechanical, morphometric and gene expression signatures that are predictive of tissue compartment boundaries. In addition, we use geoadditive structural equation modeling to identify gene modules that predict the mechanical behavior of cells in an unbiased manner. This computational framework is easily generalized to other spatial profiling contexts, providing a generic scheme for exploring the interplay of biomolecular and mechanical cues in tissues.
{"title":"A computational pipeline for spatial mechano-transcriptomics.","authors":"Adrien Hallou, Ruiyang He, Benjamin D Simons, Bianca Dumitrascu","doi":"10.1038/s41592-025-02618-1","DOIUrl":"https://doi.org/10.1038/s41592-025-02618-1","url":null,"abstract":"<p><p>Advances in spatial profiling technologies are providing insights into how molecular programs are influenced by local signaling and environmental cues. However, cell fate specification and tissue patterning involve the interplay of biochemical and mechanical feedback. Here we develop a computational framework that enables the joint statistical analysis of transcriptional and mechanical signals in the context of spatial transcriptomics. To illustrate the application and utility of the approach, we use spatial transcriptomics data from the developing mouse embryo to infer the forces acting on individual cells, and use these results to identify mechanical, morphometric and gene expression signatures that are predictive of tissue compartment boundaries. In addition, we use geoadditive structural equation modeling to identify gene modules that predict the mechanical behavior of cells in an unbiased manner. This computational framework is easily generalized to other spatial profiling contexts, providing a generic scheme for exploring the interplay of biomolecular and mechanical cues in tissues.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649847","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 : 2025-03-17DOI: 10.1038/s41592-025-02627-0
Ron Sheinin, Roded Sharan, Asaf Madi
Recent advances in single-cell RNA sequencing (scRNA-seq) techniques have provided unprecedented insights into the heterogeneity of various tissues. However, gene expression data alone often fails to capture and identify changes in cellular pathways and complexes, as they are more discernible at the protein level. Moreover, analyzing scRNA-seq data presents further challenges due to inherent characteristics such as high noise levels and zero inflation. In this study, we propose an approach to address these limitations by integrating scRNA-seq datasets with a protein-protein interaction network. Our method utilizes a unique dual-view architecture based on graph neural networks, enabling joint representation of gene expression and protein-protein interaction network data. This approach models gene-to-gene relationships under specific biological contexts and refines cell-cell relations using an attention mechanism. Next, through comprehensive evaluations, we demonstrate that scNET better captures gene annotation, pathway characterization and gene-gene relationship identification, while improving cell clustering and pathway analysis across diverse cell types and biological conditions.
{"title":"scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein-protein interactions.","authors":"Ron Sheinin, Roded Sharan, Asaf Madi","doi":"10.1038/s41592-025-02627-0","DOIUrl":"https://doi.org/10.1038/s41592-025-02627-0","url":null,"abstract":"<p><p>Recent advances in single-cell RNA sequencing (scRNA-seq) techniques have provided unprecedented insights into the heterogeneity of various tissues. However, gene expression data alone often fails to capture and identify changes in cellular pathways and complexes, as they are more discernible at the protein level. Moreover, analyzing scRNA-seq data presents further challenges due to inherent characteristics such as high noise levels and zero inflation. In this study, we propose an approach to address these limitations by integrating scRNA-seq datasets with a protein-protein interaction network. Our method utilizes a unique dual-view architecture based on graph neural networks, enabling joint representation of gene expression and protein-protein interaction network data. This approach models gene-to-gene relationships under specific biological contexts and refines cell-cell relations using an attention mechanism. Next, through comprehensive evaluations, we demonstrate that scNET better captures gene annotation, pathway characterization and gene-gene relationship identification, while improving cell clustering and pathway analysis across diverse cell types and biological conditions.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649850","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 : 2025-03-13DOI: 10.1038/s41592-025-02623-4
Ying Chen, Nadia M Davidson, Yuk Kei Wan, Fei Yao, Yan Su, Hasindu Gamaarachchi, Andre Sim, Harshil Patel, Hwee Meng Low, Christopher Hendra, Laura Wratten, Christopher Hakkaart, Chelsea Sawyer, Viktoriia Iakovleva, Puay Leng Lee, Lixia Xin, Hui En Vanessa Ng, Jia Min Loo, Xuewen Ong, Hui Qi Amanda Ng, Jiaxu Wang, Wei Qian Casslynn Koh, Suk Yeah Polly Poon, Dominik Stanojevic, Hoang-Dai Tran, Kok Hao Edwin Lim, Shen Yon Toh, Philip Andrew Ewels, Huck-Hui Ng, N Gopalakrishna Iyer, Alexandre Thiery, Wee Joo Chng, Leilei Chen, Ramanuj DasGupta, Mile Sikic, Yun-Shen Chan, Boon Ooi Patrick Tan, Yue Wan, Wai Leong Tam, Qiang Yu, Chiea Chuan Khor, Torsten Wüstefeld, Alexander Lezhava, Ploy N Pratanwanich, Michael I Love, Wee Siong Sho Goh, Sarah B Ng, Alicia Oshlack, Jonathan Göke
The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.
{"title":"A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines.","authors":"Ying Chen, Nadia M Davidson, Yuk Kei Wan, Fei Yao, Yan Su, Hasindu Gamaarachchi, Andre Sim, Harshil Patel, Hwee Meng Low, Christopher Hendra, Laura Wratten, Christopher Hakkaart, Chelsea Sawyer, Viktoriia Iakovleva, Puay Leng Lee, Lixia Xin, Hui En Vanessa Ng, Jia Min Loo, Xuewen Ong, Hui Qi Amanda Ng, Jiaxu Wang, Wei Qian Casslynn Koh, Suk Yeah Polly Poon, Dominik Stanojevic, Hoang-Dai Tran, Kok Hao Edwin Lim, Shen Yon Toh, Philip Andrew Ewels, Huck-Hui Ng, N Gopalakrishna Iyer, Alexandre Thiery, Wee Joo Chng, Leilei Chen, Ramanuj DasGupta, Mile Sikic, Yun-Shen Chan, Boon Ooi Patrick Tan, Yue Wan, Wai Leong Tam, Qiang Yu, Chiea Chuan Khor, Torsten Wüstefeld, Alexander Lezhava, Ploy N Pratanwanich, Michael I Love, Wee Siong Sho Goh, Sarah B Ng, Alicia Oshlack, Jonathan Göke","doi":"10.1038/s41592-025-02623-4","DOIUrl":"https://doi.org/10.1038/s41592-025-02623-4","url":null,"abstract":"<p><p>The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N<sup>6</sup>-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N<sup>6</sup>-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625346","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 : 2025-03-13DOI: 10.1038/s41592-025-02617-2
Sergio Marco Salas, Louis B Kuemmerle, Christoffer Mattsson-Langseth, Sebastian Tismeyer, Christophe Avenel, Taobo Hu, Habib Rehman, Marco Grillo, Paulo Czarnewski, Saga Helgadottir, Katarina Tiklova, Axel Andersson, Nima Rafati, Maria Chatzinikolaou, Fabian J Theis, Malte D Luecken, Carolina Wählby, Naveed Ishaque, Mats Nilsson
The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.
{"title":"Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.","authors":"Sergio Marco Salas, Louis B Kuemmerle, Christoffer Mattsson-Langseth, Sebastian Tismeyer, Christophe Avenel, Taobo Hu, Habib Rehman, Marco Grillo, Paulo Czarnewski, Saga Helgadottir, Katarina Tiklova, Axel Andersson, Nima Rafati, Maria Chatzinikolaou, Fabian J Theis, Malte D Luecken, Carolina Wählby, Naveed Ishaque, Mats Nilsson","doi":"10.1038/s41592-025-02617-2","DOIUrl":"https://doi.org/10.1038/s41592-025-02617-2","url":null,"abstract":"<p><p>The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625363","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 : 2025-03-13DOI: 10.1038/s41592-025-02624-3
Luke Zappia, Sabrina Richter, Ciro Ramírez-Suástegui, Raphael Kfuri-Rubens, Larsen Vornholz, Weixu Wang, Oliver Dietrich, Amit Frishberg, Malte D Luecken, Fabian J Theis
The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions.
{"title":"Feature selection methods affect the performance of scRNA-seq data integration and querying.","authors":"Luke Zappia, Sabrina Richter, Ciro Ramírez-Suástegui, Raphael Kfuri-Rubens, Larsen Vornholz, Weixu Wang, Oliver Dietrich, Amit Frishberg, Malte D Luecken, Fabian J Theis","doi":"10.1038/s41592-025-02624-3","DOIUrl":"https://doi.org/10.1038/s41592-025-02624-3","url":null,"abstract":"<p><p>The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625352","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 : 2025-03-13DOI: 10.1038/s41592-024-02563-5
Katy Börner, Philip D Blood, Jonathan C Silverstein, Matthew Ruffalo, Rahul Satija, Sarah A Teichmann, Gloria J Pryhuber, Ravi S Misra, Jeffrey M Purkerson, Jean Fan, John W Hickey, Gesmira Molla, Chuan Xu, Yun Zhang, Griffin M Weber, Yashvardhan Jain, Danial Qaurooni, Yongxin Kong, Andreas Bueckle, Bruce W Herr
The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a Common Coordinate Framework (CCF), knowledge graphs and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes and biomarkers) and to use the HRA to characterize changes that occur with aging, disease and other perturbations. HRA v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types and 2,089 biomarkers (such as genes, proteins and lipids) from 33 ASCT+B tables and 65 3D Reference Objects linked to ontologies. New experimental data can be mapped into the HRA using (1) cell type annotation tools (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue data spatially. This paper describes HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interfaces, flexible hybrid cloud infrastructure and previews atlas usage applications.
{"title":"Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage.","authors":"Katy Börner, Philip D Blood, Jonathan C Silverstein, Matthew Ruffalo, Rahul Satija, Sarah A Teichmann, Gloria J Pryhuber, Ravi S Misra, Jeffrey M Purkerson, Jean Fan, John W Hickey, Gesmira Molla, Chuan Xu, Yun Zhang, Griffin M Weber, Yashvardhan Jain, Danial Qaurooni, Yongxin Kong, Andreas Bueckle, Bruce W Herr","doi":"10.1038/s41592-024-02563-5","DOIUrl":"10.1038/s41592-024-02563-5","url":null,"abstract":"<p><p>The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a Common Coordinate Framework (CCF), knowledge graphs and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes and biomarkers) and to use the HRA to characterize changes that occur with aging, disease and other perturbations. HRA v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types and 2,089 biomarkers (such as genes, proteins and lipids) from 33 ASCT+B tables and 65 3D Reference Objects linked to ontologies. New experimental data can be mapped into the HRA using (1) cell type annotation tools (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue data spatially. This paper describes HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interfaces, flexible hybrid cloud infrastructure and previews atlas usage applications.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625376","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 : 2025-03-12DOI: 10.1038/s41592-025-02639-w
Lin Tang
{"title":"Optimal transport for single-cell genomics","authors":"Lin Tang","doi":"10.1038/s41592-025-02639-w","DOIUrl":"10.1038/s41592-025-02639-w","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"452-452"},"PeriodicalIF":36.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602872","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}