通过深度数据融合实现超分辨空间转录组学
Super-resolved spatial transcriptomics by deep data fusion.
作者信息
Bergenstråhle Ludvig, He Bryan, Bergenstråhle Joseph, Abalo Xesús, Mirzazadeh Reza, Thrane Kim, Ji Andrew L, Andersson Alma, Larsson Ludvig, Stakenborg Nathalie, Boeckxstaens Guy, Khavari Paul, Zou James, Lundeberg Joakim, Maaskola Jonas
机构信息
SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
出版信息
Nat Biotechnol. 2022 Apr;40(4):476-479. doi: 10.1038/s41587-021-01075-3. Epub 2021 Nov 29.
Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone.
当前的空间转录组学方法受到低空间分辨率的限制。在此，我们介绍一种方法，该方法将空间基因表达数据与来自同一组织切片的组织学图像数据相结合，以推断更高分辨率的表达图谱。利用深度生成模型，我们的方法能够表征微米级解剖特征的转录组，并且仅从组织学图像就能预测空间基因表达。
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