Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.
Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.
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通过整合空间转录组学数据和组织图像的深层特征，发现形态景观背后的分子特征。
DOI：
10.1093/nar/gkab095
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发表时间：
2021-06-04
影响因子：
14.9
通讯作者：
Lee DS
中科院分区：
文献类型：
--
作者：
Bae S;Choi H;Lee DS
分析与组织形态学景观相关的分子特征对于研究组织生物学功能的结构和空间模式至关重要。在这项研究中，我们提出了一种新的方法，通过组织图像深度学习（SPADE）的空间基因表达模式，通过将空间转录组数据与共配准图像相结合来识别与形态学背景相关的重要基因。SPADE将深度学习衍生的图像模式与空间分辨的基因表达数据相结合，以提取形态背景标记。对应于转录组的空间图的形态学特征通过围绕每个斑点的图像块提取，并且随后由图像潜在特征表示。鉴定了与图像潜特征相关的分子谱。提取的基因可以进一步分析，以发现功能的条款和利用提取集群保持形态背景。我们将我们的方法应用于来自不同组织，平台和图像类型的空间转录组数据，以证明一种能够获得图像整合基因表达趋势的无偏方法。
Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene expression patterns by deep learning of tissue images (SPADE), to identify important genes associated with morphological contexts by combining spatial transcriptomic data with coregistered images. SPADE incorporates deep learning-derived image patterns with spatially resolved gene expression data to extract morphological context markers. Morphological features that correspond to spatial maps of the transcriptome were extracted by image patches surrounding each spot and were subsequently represented by image latent features. The molecular profiles correlated with the image latent features were identified. The extracted genes could be further analyzed to discover functional terms and exploited to extract clusters maintaining morphological contexts. We apply our approach to spatial transcriptomic data from different tissues, platforms and types of images to demonstrate an unbiased method that is capable of obtaining image-integrated gene expression trends.
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影响因子：
5.8
作者：
Gu, Zuguang;Eils, Roland;Schlesner, Matthias
通讯作者：
Schlesner, Matthias
影响因子：
64.5
作者：
Levine JH;Simonds EF;Bendall SC;Davis KL;Amir el-AD;Tadmor MD;Litvin O;Fienberg HG;Jager A;Zunder ER;Finck R;Gedman AL;Radtke I;Downing JR;Pe'er D;Nolan GP
通讯作者：
Nolan GP
下丘脑前区域的分子，空间和功能性单细胞分析。
DOI：
10.1126/science.aau5324
发表时间：
2018-11-16
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Moffitt JR;Bambah-Mukku D;Eichhorn SW;Vaughn E;Shekhar K;Perez JD;Rubinstein ND;Hao J;Regev A;Dulac C;Zhuang X
通讯作者：
Zhuang X
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
2.5
作者：
Gribaudo, S.;Bovetti, S.;De Marchis, S.
通讯作者：
De Marchis, S.