PT - JOURNAL ARTICLE AU - Tommaso Biancalani AU - Gabriele Scalia AU - Lorenzo Buffoni AU - Raghav Avasthi AU - Ziqing Lu AU - Aman Sanger AU - Neriman Tokcan AU - Charles R. Vanderburg AU - Asa Segerstolpe AU - Meng Zhang AU - Inbal Avraham-Davidi AU - Sanja Vickovic AU - Mor Nitzan AU - Sai Ma AU - Jason Buenrostro AU - Nik Bear Brown AU - Duccio Fanelli AU - Xiaowei Zhuang AU - Evan Z. Macosko AU - Aviv Regev TI - Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram AID - 10.1101/2020.08.29.272831 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.08.29.272831 4099 - http://biorxiv.org/content/early/2020/09/24/2020.08.29.272831.short 4100 - http://biorxiv.org/content/early/2020/09/24/2020.08.29.272831.full AB - 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 StatementAR 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.