SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data

Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably captu...

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Published inNature communications Vol. 16; no. 1; pp. 2990 - 17
Main Authors Dong, Mingze, Su, David G., Kluger, Harriet, Fan, Rong, Kluger, Yuval
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 27.03.2025
Nature Publishing Group
Nature Portfolio
Subjects
631/114/1305
631/114/2397
631/114/2415
631/114/794
Annotations
B-Lymphocytes - metabolism
Biological activity
Cell Communication
Cellular communication
Computational Biology - methods
Deep Learning
Epigenetics
Gene expression
Germinal Center - cytology
Germinal Center - metabolism
Germinal centers
Humanities and Social Sciences
Humans
Interaction models
Lymphocytes B
Macrophages
Macrophages - metabolism
Melanoma
Melanoma - genetics
Melanoma - pathology
Microenvironments
multidisciplinary
Palatine Tonsil - cytology
Science
Science (multidisciplinary)
Single-Cell Analysis - methods
Spatial analysis
Spatial data
Structure-function relationships
Tumor microenvironment
Tumor Microenvironment - genetics
Tumors
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Abstract Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments. Dissecting cellular intrinsic properties and spatial interactions is crucial for understanding biological processes. Here, authors develop a theoretically grounded deep learning framework SIMVI, that disentangles the two factors from spatial omics data.
AbstractList Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.
Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.
Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.Dissecting cellular intrinsic properties and spatial interactions is crucial for understanding biological processes. Here, authors develop a theoretically grounded deep learning framework SIMVI, that disentangles the two factors from spatial omics data.
Abstract Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.
Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments. Dissecting cellular intrinsic properties and spatial interactions is crucial for understanding biological processes. Here, authors develop a theoretically grounded deep learning framework SIMVI, that disentangles the two factors from spatial omics data.
ArticleNumber 2990
Author Su, David G.
Fan, Rong
Dong, Mingze
Kluger, Harriet
Kluger, Yuval
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Snippet Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing...
Abstract Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing...
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SubjectTerms 631/114/1305
631/114/2397
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Annotations
B-Lymphocytes - metabolism
Biological activity
Cell Communication
Cellular communication
Computational Biology - methods
Deep Learning
Epigenetics
Gene expression
Germinal Center - cytology
Germinal Center - metabolism
Germinal centers
Humanities and Social Sciences
Humans
Interaction models
Lymphocytes B
Macrophages
Macrophages - metabolism
Melanoma
Melanoma - genetics
Melanoma - pathology
Microenvironments
multidisciplinary
Palatine Tonsil - cytology
Science
Science (multidisciplinary)
Single-Cell Analysis - methods
Spatial analysis
Spatial data
Structure-function relationships
Tumor microenvironment
Tumor Microenvironment - genetics
Tumors
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Title SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data
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