Robust decomposition of cell type mixtures in spatial transcriptomics
A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational m...
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| Vydané v: | Nature biotechnology Ročník 40; číslo 4; s. 517 - 526 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Nature Publishing Group US
01.04.2022
Nature Publishing Group |
| Predmet: | |
| ISSN: | 1087-0156, 1546-1696, 1546-1696 |
| On-line prístup: | Získať plný text |
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| Abstract | A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD’s recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at
https://github.com/dmcable/RCTD
.
Cell type mapping in spatial transcriptomics is enabled by accounting for compositional mixtures and differences in sequencing technologies. |
|---|---|
| AbstractList | A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD . A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures, while correcting for differences across sequencing technologies. We demonstrate RCTD’s ability to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD’s recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables defining spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open source R package at https://github.com/dmcable/RCTD. Cell-type mapping in spatial transcriptomics is enabled by accounting for compositional mixtures and differences in sequencing technologies. A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD . A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD’s recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD.Cell type mapping in spatial transcriptomics is enabled by accounting for compositional mixtures and differences in sequencing technologies. A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD’s recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD . Cell type mapping in spatial transcriptomics is enabled by accounting for compositional mixtures and differences in sequencing technologies. |
| Author | Cable, Dylan M. Chen, Fei Goeva, Aleksandrina Macosko, Evan Z. Irizarry, Rafael A. Zou, Luli S. Murray, Evan |
| AuthorAffiliation | 2 Broad Institute of Harvard and MIT, Cambridge, MA, 02142 5 Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114 6 Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge MA 02138 1 Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, 02139 3 Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215 4 Department of Biostatistics, Harvard University, Boston, MA, 02115 |
| AuthorAffiliation_xml | – name: 4 Department of Biostatistics, Harvard University, Boston, MA, 02115 – name: 3 Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215 – name: 2 Broad Institute of Harvard and MIT, Cambridge, MA, 02142 – name: 6 Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge MA 02138 – name: 5 Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114 – name: 1 Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, 02139 |
| Author_xml | – sequence: 1 givenname: Dylan M. surname: Cable fullname: Cable, Dylan M. organization: Department of Electrical Engineering and Computer Science, MIT, Broad Institute of Harvard and MIT, Department of Data Science, Dana-Farber Cancer Institute – sequence: 2 givenname: Evan surname: Murray fullname: Murray, Evan organization: Broad Institute of Harvard and MIT – sequence: 3 givenname: Luli S. surname: Zou fullname: Zou, Luli S. organization: Broad Institute of Harvard and MIT, Department of Data Science, Dana-Farber Cancer Institute, Department of Biostatistics, Harvard University – sequence: 4 givenname: Aleksandrina surname: Goeva fullname: Goeva, Aleksandrina organization: Broad Institute of Harvard and MIT – sequence: 5 givenname: Evan Z. orcidid: 0000-0002-2794-5165 surname: Macosko fullname: Macosko, Evan Z. organization: Broad Institute of Harvard and MIT, Department of Psychiatry, Massachusetts General Hospital – sequence: 6 givenname: Fei orcidid: 0000-0003-2308-3649 surname: Chen fullname: Chen, Fei email: chenf@broadinstitute.org organization: Broad Institute of Harvard and MIT, Department of Stem Cell and Regenerative Biology, Harvard University – sequence: 7 givenname: Rafael A. orcidid: 0000-0002-3944-4309 surname: Irizarry fullname: Irizarry, Rafael A. email: rafa@ds.dfci.harvard.edu organization: Department of Data Science, Dana-Farber Cancer Institute, Department of Biostatistics, Harvard University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33603203$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2021 2021. The Author(s), under exclusive licence to Springer Nature America, Inc. The Author(s), under exclusive licence to Springer Nature America, Inc. 2021. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Author Contributions These authors contributed equally D.M.C., F.C., R.A.I, and E.Z.M. conceived the study; F.C., E.M., and E.Z.M. designed the Slide-seq experiment; E.M. generated the Slide-seq data; D.M.C., R.A.I., and F.C. developed the statistical methods; D.M.C., F.C., R.A.I, and E.Z.M. designed the analysis; D.M.C., R.A.I., F.C, A.G., and L.S.Z. analyzed the data; D.M.C., F.C., R.A.I., E.Z.M., and L.S.Z. wrote the manuscript; all authors read and approved the final manuscript. |
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| PublicationSubtitle | The Science and Business of Biotechnology |
| PublicationTitle | Nature biotechnology |
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| Snippet | A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of... |
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| SubjectTerms | 631/114/2415 631/61 631/61/212 631/61/212/2019 Agriculture Animals Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Computational neuroscience Datasets Decomposition Exome Sequencing Life Sciences Localization Mapping Mice Mixtures Sequence Analysis, RNA Single-Cell Analysis Software Tissues Transcriptome - genetics Transcriptomics |
| Title | Robust decomposition of cell type mixtures in spatial transcriptomics |
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