DestVI identifies continuums of cell types in spatial transcriptomics data

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of...

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Published inNature biotechnology Vol. 40; no. 9; pp. 1360 - 1369
Main Authors Lopez, Romain, Li, Baoguo, Keren-Shaul, Hadas, Boyeau, Pierre, Kedmi, Merav, Pilzer, David, Jelinski, Adam, Yofe, Ido, David, Eyal, Wagner, Allon, Ergen, Can, Addadi, Yoseph, Golani, Ofra, Ronchese, Franca, Jordan, Michael I., Amit, Ido, Yosef, Nir
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.09.2022
Nature Publishing Group
Subjects
631/114/2397
631/208/199
Agriculture
Animals
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Cell culture
Continuums
Exome Sequencing
Gene expression
Gene Expression Profiling - methods
Gene sequencing
Life Sciences
Lymph nodes
Mice
Neoplasms - genetics
Open source software
Single-Cell Analysis - methods
Software
Spatial data
Transcriptome - genetics
Transcriptomes
Transcriptomics
Tumors
Online AccessGet full text
ISSN1087-0156
1546-1696
1546-1696
DOI10.1038/s41587-022-01272-8

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Abstract Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ). DestVI models continuous cell states in spatial transcriptomics data.
AbstractList Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ). DestVI models continuous cell states in spatial transcriptomics data.
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools (https://scvi-tools.org).DestVI models continuous cell states in spatial transcriptomics data.
Author Addadi, Yoseph
Jelinski, Adam
Lopez, Romain
Pilzer, David
David, Eyal
Li, Baoguo
Jordan, Michael I.
Wagner, Allon
Amit, Ido
Yofe, Ido
Ronchese, Franca
Golani, Ofra
Keren-Shaul, Hadas
Boyeau, Pierre
Kedmi, Merav
Ergen, Can
Yosef, Nir
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  organization: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Center for Computational Biology, University of California, Berkeley, Chan Zuckerberg Biohub, Ragon Institute of MGH, MIT and Harvard
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ContentType Journal Article
Copyright The Author(s), under exclusive licence to Springer Nature America, Inc. 2022
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631/208/199
Agriculture
Animals
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Cell culture
Continuums
Exome Sequencing
Gene expression
Gene Expression Profiling - methods
Gene sequencing
Life Sciences
Lymph nodes
Mice
Neoplasms - genetics
Open source software
Single-Cell Analysis - methods
Software
Spatial data
Transcriptome - genetics
Transcriptomes
Transcriptomics
Tumors
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Title DestVI identifies continuums of cell types in spatial transcriptomics data
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