Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.
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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. |
Year of Publication | 2021
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Journal | Nat Methods
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Volume | 18
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Issue | 11
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Pages | 1352-1362
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Date Published | 2021 11
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ISSN | 1548-7105
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DOI | 10.1038/s41592-021-01264-7
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PubMed ID | 34711971
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PubMed Central ID | PMC8566243
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Grant list | OT2 OD026673 / OD / NIH HHS / United States
U19 MH114821 / MH / NIMH NIH HHS / United States
U19 MH114830 / MH / NIMH NIH HHS / United States
HHMI / Howard Hughes Medical Institute / United States
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