Pub Date : 2025-02-06DOI: 10.1038/s41592-024-02590-2
Antonio Fiore, Guoqiang Yu, Jason J Northey, Ronak Patel, Thomas A Ravenscroft, Richard Ikegami, Wiert Kolkman, Pratik Kumar, Tanya L Dilan, Virginia M S Ruetten, Misha B Ahrens, Hari Shroff, Shaohe Wang, Valerie M Weaver, Kayvon Pedram
All multicellular systems produce and dynamically regulate extracellular matrices (ECMs) that play essential roles in both biochemical and mechanical signaling. Though the spatial arrangement of these extracellular assemblies is critical to their biological functions, visualization of ECM structure is challenging, in part because the biomolecules that compose the ECM are difficult to fluorescently label individually and collectively. Here, we present a cell-impermeable small-molecule fluorophore, termed Rhobo6, that turns on and red shifts upon reversible binding to glycans. Given that most ECM components are densely glycosylated, the dye enables wash-free visualization of ECM, in systems ranging from in vitro substrates to in vivo mouse mammary tumors. Relative to existing techniques, Rhobo6 provides a broad substrate profile, superior tissue penetration, non-perturbative labeling, and negligible photobleaching. This work establishes a straightforward method for imaging the distribution of ECM in live tissues and organisms, lowering barriers for investigation of extracellular biology.
{"title":"Live imaging of the extracellular matrix with a glycan-binding fluorophore.","authors":"Antonio Fiore, Guoqiang Yu, Jason J Northey, Ronak Patel, Thomas A Ravenscroft, Richard Ikegami, Wiert Kolkman, Pratik Kumar, Tanya L Dilan, Virginia M S Ruetten, Misha B Ahrens, Hari Shroff, Shaohe Wang, Valerie M Weaver, Kayvon Pedram","doi":"10.1038/s41592-024-02590-2","DOIUrl":"10.1038/s41592-024-02590-2","url":null,"abstract":"<p><p>All multicellular systems produce and dynamically regulate extracellular matrices (ECMs) that play essential roles in both biochemical and mechanical signaling. Though the spatial arrangement of these extracellular assemblies is critical to their biological functions, visualization of ECM structure is challenging, in part because the biomolecules that compose the ECM are difficult to fluorescently label individually and collectively. Here, we present a cell-impermeable small-molecule fluorophore, termed Rhobo6, that turns on and red shifts upon reversible binding to glycans. Given that most ECM components are densely glycosylated, the dye enables wash-free visualization of ECM, in systems ranging from in vitro substrates to in vivo mouse mammary tumors. Relative to existing techniques, Rhobo6 provides a broad substrate profile, superior tissue penetration, non-perturbative labeling, and negligible photobleaching. This work establishes a straightforward method for imaging the distribution of ECM in live tissues and organisms, lowering barriers for investigation of extracellular biology.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":" ","pages":""},"PeriodicalIF":36.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143365265","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 : 2025-02-05DOI: 10.1038/s41592-025-02593-7
Woosub Kim, Milot Mirdita, Eli Levy Karin, Cameron L. M. Gilchrist, Hugo Schweke, Johannes Söding, Emmanuel D. Levy, Martin Steinegger
Advances in computational structure prediction will vastly augment the hundreds of thousands of currently available protein complex structures. Translating these into discoveries requires aligning them, which is computationally prohibitive. Foldseek-Multimer computes complex alignments from compatible chain-to-chain alignments, identified by efficiently clustering their superposition vectors. Foldseek-Multimer is 3–4 orders of magnitudes faster than the gold standard, while producing comparable alignments; this allows it to compare billions of complex pairs in 11 h. Foldseek-Multimer is open-source software available at GitHub via https://github.com/steineggerlab/foldseek/ , https://search.foldseek.com/search/ and the BFMD database. Foldseek-Multimer offers a fast strategy for complex-to-complex alignment to quickly identify compatible sets of chain-to-chain alignments by their superpositions. It can compare billions of complex pairs in 11 h.
{"title":"Rapid and sensitive protein complex alignment with Foldseek-Multimer","authors":"Woosub Kim, Milot Mirdita, Eli Levy Karin, Cameron L. M. Gilchrist, Hugo Schweke, Johannes Söding, Emmanuel D. Levy, Martin Steinegger","doi":"10.1038/s41592-025-02593-7","DOIUrl":"10.1038/s41592-025-02593-7","url":null,"abstract":"Advances in computational structure prediction will vastly augment the hundreds of thousands of currently available protein complex structures. Translating these into discoveries requires aligning them, which is computationally prohibitive. Foldseek-Multimer computes complex alignments from compatible chain-to-chain alignments, identified by efficiently clustering their superposition vectors. Foldseek-Multimer is 3–4 orders of magnitudes faster than the gold standard, while producing comparable alignments; this allows it to compare billions of complex pairs in 11 h. Foldseek-Multimer is open-source software available at GitHub via https://github.com/steineggerlab/foldseek/ , https://search.foldseek.com/search/ and the BFMD database. Foldseek-Multimer offers a fast strategy for complex-to-complex alignment to quickly identify compatible sets of chain-to-chain alignments by their superpositions. It can compare billions of complex pairs in 11 h.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"469-472"},"PeriodicalIF":36.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-025-02593-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143255839","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 : 2025-02-04DOI: 10.1038/s41592-025-02596-4
Vivien Marx
As they study the emerging roles of RNA in disease and homeostasis, some scientists use RNA editing to direct precise RNA changes that shape cellular events.
{"title":"Taking control with RNA","authors":"Vivien Marx","doi":"10.1038/s41592-025-02596-4","DOIUrl":"10.1038/s41592-025-02596-4","url":null,"abstract":"As they study the emerging roles of RNA in disease and homeostasis, some scientists use RNA editing to direct precise RNA changes that shape cellular events.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"226-230"},"PeriodicalIF":36.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-025-02596-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189857","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 : 2025-01-27DOI: 10.1038/s41592-024-02583-1
Ahmadreza Attarpour, Jonas Osmann, Anthony Rinaldi, Tianbo Qi, Neeraj Lal, Shruti Patel, Matthew Rozak, Fengqing Yu, Newton Cho, Jordan Squair, JoAnne McLaurin, Misha Raffiee, Karl Deisseroth, Gregoire Courtine, Li Ye, Bojana Stefanovic, Maged Goubran
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity. Validation against state-of-the-art segmentation and detection methods on unseen datasets demonstrated ACE’s high generalizability and performance. Applying ACE in two distinct neurobiological contexts, we discovered subregional effects missed by existing atlas-based analyses and showcase ACE’s ability to reveal localized or laminar neuronal activity brain-wide. Our open-source pipeline enables whole-brain mapping of neuronal ensembles at a high level of precision across a wide range of neuroscientific applications. The ACE pipeline utilized deep learning and advanced statistics for mapping neural activity at a granular level that is independent of atlas-defined regions.
{"title":"A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy","authors":"Ahmadreza Attarpour, Jonas Osmann, Anthony Rinaldi, Tianbo Qi, Neeraj Lal, Shruti Patel, Matthew Rozak, Fengqing Yu, Newton Cho, Jordan Squair, JoAnne McLaurin, Misha Raffiee, Karl Deisseroth, Gregoire Courtine, Li Ye, Bojana Stefanovic, Maged Goubran","doi":"10.1038/s41592-024-02583-1","DOIUrl":"10.1038/s41592-024-02583-1","url":null,"abstract":"Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity. Validation against state-of-the-art segmentation and detection methods on unseen datasets demonstrated ACE’s high generalizability and performance. Applying ACE in two distinct neurobiological contexts, we discovered subregional effects missed by existing atlas-based analyses and showcase ACE’s ability to reveal localized or laminar neuronal activity brain-wide. Our open-source pipeline enables whole-brain mapping of neuronal ensembles at a high level of precision across a wide range of neuroscientific applications. The ACE pipeline utilized deep learning and advanced statistics for mapping neural activity at a granular level that is independent of atlas-defined regions.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"600-611"},"PeriodicalIF":36.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02583-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052493","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 : 2025-01-27DOI: 10.1038/s41592-024-02537-7
Meraj Ramezani, Erin Weisbart, Julia Bauman, Avtar Singh, John Yong, Maria Lozada, Gregory P. Way, Sanam L. Kavari, Celeste Diaz, Eddy Leardini, Gunjan Jetley, Jenlu Pagnotta, Marzieh Haghighi, Thiago M. Batista, Joaquín Pérez-Schindler, Melina Claussnitzer, Shantanu Singh, Beth A. Cimini, Paul C. Blainey, Anne E. Carpenter, Calvin H. Jan, James T. Neal
A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. Here we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-wide genotype–phenotype maps comprising CRISPR–Cas9-based knockouts of >20,000 genes in >30 million cells. Our optical pooled cell profiling platform (PERISCOPE) combines a destainable high-dimensional phenotyping panel (based on Cell Painting) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This perturbation atlas comprises high-dimensional phenotypic profiles of individual cells with sufficient resolution to cluster thousands of human genes, reconstruct known pathways and protein–protein interaction networks, interrogate subcellular processes and identify culture media-specific responses. Using this atlas, we identify the poorly characterized disease-associated TMEM251/LYSET as a Golgi-resident transmembrane protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes. In sum, this perturbation atlas and screening platform represents a rich and accessible resource for connecting genes to cellular functions at scale. An optical pooled cell profiling platform (PERISCOPE) based on Cell Painting and optical sequencing of molecular barcodes was used to develop the first unbiased genome-wide morphology-based perturbation atlas in human cells.
{"title":"A genome-wide atlas of human cell morphology","authors":"Meraj Ramezani, Erin Weisbart, Julia Bauman, Avtar Singh, John Yong, Maria Lozada, Gregory P. Way, Sanam L. Kavari, Celeste Diaz, Eddy Leardini, Gunjan Jetley, Jenlu Pagnotta, Marzieh Haghighi, Thiago M. Batista, Joaquín Pérez-Schindler, Melina Claussnitzer, Shantanu Singh, Beth A. Cimini, Paul C. Blainey, Anne E. Carpenter, Calvin H. Jan, James T. Neal","doi":"10.1038/s41592-024-02537-7","DOIUrl":"10.1038/s41592-024-02537-7","url":null,"abstract":"A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. Here we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-wide genotype–phenotype maps comprising CRISPR–Cas9-based knockouts of >20,000 genes in >30 million cells. Our optical pooled cell profiling platform (PERISCOPE) combines a destainable high-dimensional phenotyping panel (based on Cell Painting) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This perturbation atlas comprises high-dimensional phenotypic profiles of individual cells with sufficient resolution to cluster thousands of human genes, reconstruct known pathways and protein–protein interaction networks, interrogate subcellular processes and identify culture media-specific responses. Using this atlas, we identify the poorly characterized disease-associated TMEM251/LYSET as a Golgi-resident transmembrane protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes. In sum, this perturbation atlas and screening platform represents a rich and accessible resource for connecting genes to cellular functions at scale. An optical pooled cell profiling platform (PERISCOPE) based on Cell Painting and optical sequencing of molecular barcodes was used to develop the first unbiased genome-wide morphology-based perturbation atlas in human cells.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"621-633"},"PeriodicalIF":36.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02537-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052499","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 : 2025-01-27DOI: 10.1038/s41592-024-02576-0
Pengfei Guo, Liran Mao, Yufan Chen, Chin Nien Lee, Angelysia Cardilla, Mingyao Li, Marek Bartosovic, Yanxiang Deng
The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner. We applied this technology to mouse embryos and mouse brains, generating detailed multimodal tissue maps that identified more cell types and states compared to unimodal data. This analysis uncovered spatiotemporal relationships among histone modifications, chromatin accessibility, gene expression and protein levels during neuron differentiation, and revealed a radial glia niche with spatially dynamic epigenetic signals. Collectively, the spatial multi-omics approach heralds a new era for characterizing tissue and cellular heterogeneity that single-modality studies alone could not reveal. Spatial-Mux-seq offers a multimodal spatial platform capable of profiling multiple molecular modalities, including the transcriptome, chromatin accessibility, histone modifications and targeted proteins.
{"title":"Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues","authors":"Pengfei Guo, Liran Mao, Yufan Chen, Chin Nien Lee, Angelysia Cardilla, Mingyao Li, Marek Bartosovic, Yanxiang Deng","doi":"10.1038/s41592-024-02576-0","DOIUrl":"10.1038/s41592-024-02576-0","url":null,"abstract":"The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner. We applied this technology to mouse embryos and mouse brains, generating detailed multimodal tissue maps that identified more cell types and states compared to unimodal data. This analysis uncovered spatiotemporal relationships among histone modifications, chromatin accessibility, gene expression and protein levels during neuron differentiation, and revealed a radial glia niche with spatially dynamic epigenetic signals. Collectively, the spatial multi-omics approach heralds a new era for characterizing tissue and cellular heterogeneity that single-modality studies alone could not reveal. Spatial-Mux-seq offers a multimodal spatial platform capable of profiling multiple molecular modalities, including the transcriptome, chromatin accessibility, histone modifications and targeted proteins.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"520-529"},"PeriodicalIF":36.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052616","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 : 2025-01-27DOI: 10.1038/s41592-024-02571-5
Maren Salla, Klara Penkert, Leif S. Ludwig
Spatial-Mux-seq adds to a growing portfolio of innovative spatial omics technologies and enables simultaneous profiling of up to five omic modalities in situ.
{"title":"The next generation of in situ multi-omics","authors":"Maren Salla, Klara Penkert, Leif S. Ludwig","doi":"10.1038/s41592-024-02571-5","DOIUrl":"10.1038/s41592-024-02571-5","url":null,"abstract":"Spatial-Mux-seq adds to a growing portfolio of innovative spatial omics technologies and enables simultaneous profiling of up to five omic modalities in situ.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"461-462"},"PeriodicalIF":36.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143052607","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 : 2025-01-24DOI: 10.1038/s41592-025-02601-w
Cheng Zhao, Alvaro Plaza Reyes, John Paul Schell, Jere Weltner, Nicolás M. Ortega, Yi Zheng, Åsa K. Björklund, Laura Baqué-Vidal, Joonas Sokka, Ras Trokovic, Brian Cox, Janet Rossant, Jianping Fu, Sophie Petropoulos, Fredrik Lanner
{"title":"Author Correction: A comprehensive human embryo reference tool using single-cell RNA-sequencing data","authors":"Cheng Zhao, Alvaro Plaza Reyes, John Paul Schell, Jere Weltner, Nicolás M. Ortega, Yi Zheng, Åsa K. Björklund, Laura Baqué-Vidal, Joonas Sokka, Ras Trokovic, Brian Cox, Janet Rossant, Jianping Fu, Sophie Petropoulos, Fredrik Lanner","doi":"10.1038/s41592-025-02601-w","DOIUrl":"10.1038/s41592-025-02601-w","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"636-636"},"PeriodicalIF":36.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-025-02601-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039460","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 : 2025-01-24DOI: 10.1038/s41592-025-02600-x
Kyle Coleman, Amelia Schroeder, Melanie Loth, Daiwei Zhang, Jeong Hwan Park, Ji-Youn Sung, Niklas Blank, Alexis J. Cowan, Xuyu Qian, Jianfeng Chen, Jiahui Jiang, Hanying Yan, Laith Z. Samarah, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Joshua D. Rabinowitz, Yanxiang Deng, Edward B. Lee, Alexander Lazar, Jianjun Gao, Emma E. Furth, Tae Hyun Hwang, Linghua Wang, Christoph A. Thaiss, Jian Hu, Mingyao Li
{"title":"Author Correction: Resolving tissue complexity by multimodal spatial omics modeling with MISO","authors":"Kyle Coleman, Amelia Schroeder, Melanie Loth, Daiwei Zhang, Jeong Hwan Park, Ji-Youn Sung, Niklas Blank, Alexis J. Cowan, Xuyu Qian, Jianfeng Chen, Jiahui Jiang, Hanying Yan, Laith Z. Samarah, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Joshua D. Rabinowitz, Yanxiang Deng, Edward B. Lee, Alexander Lazar, Jianjun Gao, Emma E. Furth, Tae Hyun Hwang, Linghua Wang, Christoph A. Thaiss, Jian Hu, Mingyao Li","doi":"10.1038/s41592-025-02600-x","DOIUrl":"10.1038/s41592-025-02600-x","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 3","pages":"635-635"},"PeriodicalIF":36.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-025-02600-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039462","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 : 2025-01-23DOI: 10.1038/s41592-024-02503-3
Uthsav Chitra, Brian J. Arnold, Hirak Sarkar, Kohei Sanno, Cong Ma, Sereno Lopez-Darwin, Benjamin J. Raphael
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice—analogous to a map of elevation in a landscape—using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functions that model both continuous gradients and discontinuous variation in gene expression. We show that GASTON accurately identifies spatial domains and marker genes across several tissues, gradients of neuronal differentiation and firing in the brain, and gradients of metabolism and immune activity in the tumor microenvironment. Gene expression topography analysis by GASTON portrays domain organization and spatial gradients of gene expression and cell type composition using spatially resolved transcriptomics data.
{"title":"Mapping the topography of spatial gene expression with interpretable deep learning","authors":"Uthsav Chitra, Brian J. Arnold, Hirak Sarkar, Kohei Sanno, Cong Ma, Sereno Lopez-Darwin, Benjamin J. Raphael","doi":"10.1038/s41592-024-02503-3","DOIUrl":"10.1038/s41592-024-02503-3","url":null,"abstract":"Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice—analogous to a map of elevation in a landscape—using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functions that model both continuous gradients and discontinuous variation in gene expression. We show that GASTON accurately identifies spatial domains and marker genes across several tissues, gradients of neuronal differentiation and firing in the brain, and gradients of metabolism and immune activity in the tumor microenvironment. Gene expression topography analysis by GASTON portrays domain organization and spatial gradients of gene expression and cell type composition using spatially resolved transcriptomics data.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"298-309"},"PeriodicalIF":36.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029207","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}
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