Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model.
Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model.
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空间转录组学数据中的细胞类型鉴定可以通过使用贝叶斯概率模型利用细胞类型信息配对组织图像来改进。
DOI：
10.1093/nar/gkac320
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发表时间：
2022-08-12
影响因子：
14.9
通讯作者：
Geeleher, Paul
中科院分区：
文献类型：
--
作者：
Zubair, Asif;Chapple, Richard H.;Natarajan, Sivaraman;Wright, William C.;Pan, Min;Lee, Hyeong-Min;Tillman, Heather;Easton, John;Geeleher, Paul
空间转录组学技术最近已经成为一种强大的工具，用于直接在组织切片中测量空间分辨的基因表达，以前所未有的细节揭示细胞类型及其功能障碍。然而，空间转录组学技术分离转录相似细胞类型的能力有限，并且在转录本捕获较低的载玻片区域中识别细胞类型可能会遇到进一步的困难。在这里，我们描述了一种概念上新颖的方法，可以计算整合空间转录组学数据与细胞类型信息配对的组织图像，从，例如，相同的组织切片的反面，以提高空间转录组学数据中的组织细胞类型组成的推断。基本的统计方法可推广到任何空间转录组学协议，其中可以获得信息丰富的配对组织图像。我们展示了一个利用小鼠脑组织切片上获得的细胞类型特异性免疫荧光标记物的用例，以及一个利用AI注释的H&E组织图像输出的用例，我们用于显着改善乳腺癌组织中临床相关免疫细胞浸润的识别。因此，将空间转录组学数据与配对的组织图像相结合有可能改善细胞类型的识别，从而改善依赖于准确细胞类型识别的空间转录组学的应用。
Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.
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影响因子：
12.3
作者：
Li B;Liu JS;Liu XS
通讯作者：
Liu XS
影响因子：
64.5
作者：
Liu Y;Yang M;Deng Y;Su G;Enninful A;Guo CC;Tebaldi T;Zhang D;Kim D;Bai Z;Norris E;Pan A;Li J;Xiao Y;Halene S;Fan R
通讯作者：
Fan R
影响因子：
5.9
作者：
Andersson A;Bergenstråhle J;Asp M;Bergenstråhle L;Jurek A;Fernández Navarro J;Lundeberg J
通讯作者：
Lundeberg J
影响因子：
16.6
作者：
Karaayvaz M;Cristea S;Gillespie SM;Patel AP;Mylvaganam R;Luo CC;Specht MC;Bernstein BE;Michor F;Ellisen LW
通讯作者：
Ellisen LW
影响因子：
22.7
作者：
Kather JN;Heij LR;Grabsch HI;Loeffler C;Echle A;Muti HS;Krause J;Niehues JM;Sommer KA;Bankhead P;Kooreman LF;Schulte JJ;Cipriani NA;Buelow RD;Boor P;Ortiz-Brüchle NN;Hanby AM;Speirs V;Kochanny S;Patnaik A;Srisuwananukorn A;Brenner H;Hoffmeister M;van den Brandt PA;Jäger D;Trautwein C;Pearson AT;Luedde T
通讯作者：
Luedde T