转录组
电池类型
生物
计算生物学
人口
细胞
计算机科学
遗传学
基因
基因表达
医学
环境卫生
作者
Vitalii Kleshchevnikov,Artem Shmatko,Emma Dann,Alexander Aivazidis,Hamish W. King,Tong Li,Rasa Elmentaite,Artem Lomakin,Veronika R. Kedlian,Adam Gayoso,Mika Sarkin Jain,Jun Sung Park,Lauma Ramona,Elizabeth Tuck,Anna Arutyunyan,Roser Vento‐Tormo,Moritz Gerstung,Louisa K. James,Oliver Stegle,Omer Ali Bayraktar
标识
DOI:10.1038/s41587-021-01139-4
摘要
Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present сell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. We assessed cell2location in three different tissues and show improved mapping of fine-grained cell types. In the mouse brain, we discovered fine regional astrocyte subtypes across the thalamus and hypothalamus. In the human lymph node, we spatially mapped a rare pre-germinal center B cell population. In the human gut, we resolved fine immune cell populations in lymphoid follicles. Collectively, our results present сell2location as a versatile analysis tool for mapping tissue architectures in a comprehensive manner.
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