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Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

View ORCID ProfileTommaso Biancalani, View ORCID ProfileGabriele Scalia, View ORCID ProfileLorenzo Buffoni, View ORCID ProfileRaghav Avasthi, View ORCID ProfileZiqing Lu, View ORCID ProfileAman Sanger, View ORCID ProfileNeriman Tokcan, View ORCID ProfileCharles R. Vanderburg, View ORCID ProfileAsa Segerstolpe, View ORCID ProfileMeng Zhang, View ORCID ProfileInbal Avraham-Davidi, View ORCID ProfileSanja Vickovic, View ORCID ProfileMor Nitzan, Sai Ma, View ORCID ProfileJason Buenrostro, View ORCID ProfileNik Bear Brown, View ORCID ProfileDuccio Fanelli, View ORCID ProfileXiaowei Zhuang, View ORCID ProfileEvan Z. Macosko, View ORCID ProfileAviv Regev
doi: https://doi.org/10.1101/2020.08.29.272831
Tommaso Biancalani
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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  • For correspondence: tbiancal@broadinstitute.org aregev@broadinstitute.org
Gabriele Scalia
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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  • ORCID record for Gabriele Scalia
Lorenzo Buffoni
2Department of Physics and Astrophysics, University of Florence, Florence, Italy
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  • ORCID record for Lorenzo Buffoni
Raghav Avasthi
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
3Northeastern University, Boston, MA 02115
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Ziqing Lu
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
3Northeastern University, Boston, MA 02115
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Aman Sanger
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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Neriman Tokcan
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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Charles R. Vanderburg
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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Asa Segerstolpe
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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  • ORCID record for Asa Segerstolpe
Meng Zhang
4Department of Chemistry and Chemical Biology, Department of Physics, Harvard University
6Howard Hughes Medical Institute
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  • ORCID record for Meng Zhang
Inbal Avraham-Davidi
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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Sanja Vickovic
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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Mor Nitzan
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
5School of Engineering and Applied Sciences, Harvard University
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Sai Ma
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
7Department of Biology, MIT, Cambridge, MA 02140
8Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138 USA
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Jason Buenrostro
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
8Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138 USA
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Nik Bear Brown
3Northeastern University, Boston, MA 02115
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Duccio Fanelli
2Department of Physics and Astrophysics, University of Florence, Florence, Italy
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Xiaowei Zhuang
4Department of Chemistry and Chemical Biology, Department of Physics, Harvard University
6Howard Hughes Medical Institute
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Evan Z. Macosko
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
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Aviv Regev
1Broad Institute of MIT and Harvard, Cambridge, MA 02142
6Howard Hughes Medical Institute
7Department of Biology, MIT, Cambridge, MA 02140
9Genentech, 1 DNA Way, South San Francisco, CA, 94080
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  • ORCID record for Aviv Regev
  • For correspondence: tbiancal@broadinstitute.org aregev@broadinstitute.org
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Abstract

Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively5,6, but relating those to their histological and anatomical positions in the context of an organ’s common coordinate framework remains a major challenge and barrier to the construction of a cell atlas7–10. Conversely, Spatial Transcriptomics allows for in-situ measurements11–13 at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies1–3 solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH1, STARmap2, smFISH3, and Spatial Transcriptomics4 (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with ∼30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.

Competing Interest Statement

AR is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and was an SAB member of ThermoFisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov. From August 1, 2020, AR is an employee of Genentech. XZ is a co-founder and consultant of Vizgen.

Footnotes

  • Manuscript is now part of the Human Cell Atlas.

  • https://github.com/broadinstitute/Tangram

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 24, 2020.
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Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram
Tommaso Biancalani, Gabriele Scalia, Lorenzo Buffoni, Raghav Avasthi, Ziqing Lu, Aman Sanger, Neriman Tokcan, Charles R. Vanderburg, Asa Segerstolpe, Meng Zhang, Inbal Avraham-Davidi, Sanja Vickovic, Mor Nitzan, Sai Ma, Jason Buenrostro, Nik Bear Brown, Duccio Fanelli, Xiaowei Zhuang, Evan Z. Macosko, Aviv Regev
bioRxiv 2020.08.29.272831; doi: https://doi.org/10.1101/2020.08.29.272831
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Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram
Tommaso Biancalani, Gabriele Scalia, Lorenzo Buffoni, Raghav Avasthi, Ziqing Lu, Aman Sanger, Neriman Tokcan, Charles R. Vanderburg, Asa Segerstolpe, Meng Zhang, Inbal Avraham-Davidi, Sanja Vickovic, Mor Nitzan, Sai Ma, Jason Buenrostro, Nik Bear Brown, Duccio Fanelli, Xiaowei Zhuang, Evan Z. Macosko, Aviv Regev
bioRxiv 2020.08.29.272831; doi: https://doi.org/10.1101/2020.08.29.272831

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