PT  - JOURNAL ARTICLE
AU  - Taku Monjo
AU  - Masaru Koido
AU  - Satoi Nagasawa
AU  - Yutaka Suzuki
AU  - Yoichiro Kamatani
TI  - Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation
AID  - 10.1101/2021.04.22.440763
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.04.22.440763
4099  - http://biorxiv.org/content/early/2021/04/22/2021.04.22.440763.short
4100  - http://biorxiv.org/content/early/2021/04/22/2021.04.22.440763.full
AB  - Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expressions in unmeasured regions and tissues can enhance biologists’ histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region that is difficult to identify using raw data of spatial transcriptome alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.Competing Interest StatementThe authors have declared no competing interest.