Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

Journal: Nature methods
Published Date: Nov 1, 2021

Abstract

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.

Authors

  • Tommaso Biancalani
    Broad Institute of MIT and Harvard, Cambridge, MA, USA. tommaso.biancalani@gmail.com.
  • Gabriele Scalia
    Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
  • Lorenzo Buffoni
    Department of Physics and Astrophysics, University of Florence, Florence, Italy.
  • Raghav Avasthi
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Ziqing Lu
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Aman Sanger
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Neriman Tokcan
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Charles R Vanderburg
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Åsa Segerstolpe
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Meng Zhang
    College of Software, Beihang University, Beijing, China.
  • Inbal Avraham-Davidi
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Sanja Vickovic
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Mor Nitzan
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Sai Ma
    Department of Cardiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
  • Ayshwarya Subramanian
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Michal Lipinski
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Jason Buenrostro
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Nik Bear Brown
    Northeastern University, Boston, MA, USA.
  • Duccio Fanelli
    Department of Physics and Astrophysics, University of Florence, Florence, Italy.
  • Xiaowei Zhuang
    Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA.
  • Evan Z Macosko
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Aviv Regev
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Keywords

External Resources

Popular Topics
Recent Journals