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Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram
Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial tra...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566243/ https://www.ncbi.nlm.nih.gov/pubmed/34711971 http://dx.doi.org/10.1038/s41592-021-01264-7 |
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author | Biancalani, Tommaso Scalia, Gabriele Buffoni, Lorenzo Avasthi, Raghav Lu, Ziqing Sanger, Aman Tokcan, Neriman Vanderburg, Charles R. Segerstolpe, Åsa Zhang, Meng Avraham-Davidi, Inbal Vickovic, Sanja Nitzan, Mor Ma, Sai Subramanian, Ayshwarya Lipinski, Michal Buenrostro, Jason Brown, Nik Bear Fanelli, Duccio Zhuang, Xiaowei Macosko, Evan Z. Regev, Aviv |
author_facet | Biancalani, Tommaso Scalia, Gabriele Buffoni, Lorenzo Avasthi, Raghav Lu, Ziqing Sanger, Aman Tokcan, Neriman Vanderburg, Charles R. Segerstolpe, Åsa Zhang, Meng Avraham-Davidi, Inbal Vickovic, Sanja Nitzan, Mor Ma, Sai Subramanian, Ayshwarya Lipinski, Michal Buenrostro, Jason Brown, Nik Bear Fanelli, Duccio Zhuang, Xiaowei Macosko, Evan Z. Regev, Aviv |
author_sort | Biancalani, Tommaso |
collection | PubMed |
description | Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas. |
format | Online Article Text |
id | pubmed-8566243 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-85662432021-11-16 Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram Biancalani, Tommaso Scalia, Gabriele Buffoni, Lorenzo Avasthi, Raghav Lu, Ziqing Sanger, Aman Tokcan, Neriman Vanderburg, Charles R. Segerstolpe, Åsa Zhang, Meng Avraham-Davidi, Inbal Vickovic, Sanja Nitzan, Mor Ma, Sai Subramanian, Ayshwarya Lipinski, Michal Buenrostro, Jason Brown, Nik Bear Fanelli, Duccio Zhuang, Xiaowei Macosko, Evan Z. Regev, Aviv Nat Methods Article Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas. Nature Publishing Group US 2021-10-28 2021 /pmc/articles/PMC8566243/ /pubmed/34711971 http://dx.doi.org/10.1038/s41592-021-01264-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Biancalani, Tommaso Scalia, Gabriele Buffoni, Lorenzo Avasthi, Raghav Lu, Ziqing Sanger, Aman Tokcan, Neriman Vanderburg, Charles R. Segerstolpe, Åsa Zhang, Meng Avraham-Davidi, Inbal Vickovic, Sanja Nitzan, Mor Ma, Sai Subramanian, Ayshwarya Lipinski, Michal Buenrostro, Jason Brown, Nik Bear Fanelli, Duccio Zhuang, Xiaowei Macosko, Evan Z. Regev, Aviv Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram |
title | Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram |
title_full | Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram |
title_fullStr | Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram |
title_full_unstemmed | Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram |
title_short | Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram |
title_sort | deep learning and alignment of spatially resolved single-cell transcriptomes with tangram |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8566243/ https://www.ncbi.nlm.nih.gov/pubmed/34711971 http://dx.doi.org/10.1038/s41592-021-01264-7 |
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