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

Tommaso Biancalani1,2, Gabriele Scalia3,4, Lorenzo Buffoni5

  • 1Broad Institute of MIT and Harvard, Cambridge, MA, USA. tommaso.biancalani@gmail.com.

Nature Methods
|October 29, 2021
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Summary

Tangram integrates single-cell RNA sequencing data with spatial transcriptomics, creating detailed brain atlases. This method reconstructs genome-wide spatial maps at single-cell resolution, advancing anatomical and cellular understanding.

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Area of Science:

  • Genomics
  • Neuroscience
  • Computational Biology

Background:

  • Charting organ atlases requires spatial resolution of single-cell transcriptomes.
  • Current methods like sc/snRNA-seq lack spatial information, while spatial transcriptomics has lower resolution and sensitivity.
  • Targeted in situ technologies have limited gene throughput.

Purpose of the Study:

  • To overcome limitations in spatial transcriptomics and provide a method for aligning sc/snRNA-seq data with spatial information.
  • To enable the reconstruction of genome-wide, anatomically integrated spatial maps at single-cell resolution.

Main Methods:

  • Developed Tangram, a computational method to align single-cell/nucleus RNA sequencing (sc/snRNA-seq) data with various spatial data types (MERFISH, STARmap, smFISH, Visium, histology).
  • Applied Tangram to multimodal data (SHARE-seq) to map chromatin accessibility.
  • Demonstrated Tangram on healthy mouse brain tissue.

Main Results:

  • Successfully aligned sc/snRNA-seq data to spatial datasets, including histological images.
  • Revealed spatial patterns of chromatin accessibility using multimodal data.
  • Reconstructed a high-resolution, genome-wide spatial map of mouse brain visual and somatomotor areas.

Conclusions:

  • Tangram effectively integrates diverse transcriptomic and spatial data, overcoming previous technological limitations.
  • The method enables the creation of comprehensive spatial atlases at single-cell resolution.
  • This approach advances the understanding of anatomical organization and cellular function within tissues.