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Superresolved spatial transcriptomics transferred from a histological context

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Abstract

Spatially resolved transcriptomics (SRT) is a vital technique in biology that allows for gene expression measurement at the resolution of individual spots while preserving spatial information. However, owing to technical limitations, single-spot resolution often includes data from multiple cells, leading to suboptimal results and opportunities for improvement. In this study, we propose a deep learning-based, plug-and-play method for enhancing spot resolution to obtain higher-resolution SRT data. Our approach involves training a convolutional neural network (CNN) model and introducing a shift-predict operation to obtain superresolution spots. Using a human breast cancer SRT dataset, we demonstrate that our method achieves 9 × superresolution, outperforming traditional superresolution techniques. Crucially, our method decreased the mean squared error (MSE) to 1.379 for all genes, 2.287 for tumor-related genes at 4 × superresolution, 1.866 for all genes, and 3.371 for tumor-related genes at 9 × superresolution, reflecting substantial improvements compared to the traditional approaches, including Gaussian RBF, multiquadric RBF, linear RBF, resize-predict, bilinear, and bicubic methods. Furthermore, we verify our method's effectiveness using external and simulated datasets. Our proposed method offers a substantial advancement in SRT by enabling higher-resolution gene expression data generation. By providing a deeper understanding of gene expression patterns and their underlying biological significance, this method contributes to progress in biology and medicine.

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Data availability

The human breast cancer in situ capturing transcriptomics we used in this paper is available at https://data.mendeley.com/datasets/29ntw7sh4r. The Breast Cancer Semantic Segmentation (BCSS) dataset utilized is accessible at https://github.com/PathologyDataScience/BCSS.

Abbreviations

BCSS:

Breast cancer semantic segmentation

CNN:

Convolutional neural network

GAP:

Global average pooling

HVS:

Human visual system

MSE:

Mean square error

NMF:

Nonnegative matrix factorization

PSNR:

Peak-signal-to-noise ratio

RBF:

Radial basis function

ROI:

Region of interest

scRNA-seq:

Single-cell RNA sequencing

SP:

Senior pathologist

SRT:

Spatially resolved transcriptomics

SSIM:

Structural similarity index measure

WSIs:

Whole-slide images

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Acknowledgements

The computations in this paper were run on the \(\pi\) 2.0 cluster supported by the Center for High-Performance Computing at Shanghai Jiao Tong University.

Funding

This work was supported by the Neil Shen's SJTU Medical Research Fund (to YK, HL), SJTU Trans-med Awards Research (STAR) 20210106 (to HL), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20212200, to HL).

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Authors and Affiliations

  1. State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

    Shu Wang, Yan Kong & Hui Lu

  2. SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Shu Wang, Yan Kong & Hui Lu

  3. Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China

    Xiaocheng Zhou

  4. NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai, China

    Hui Lu

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  1. Shu Wang
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  3. Yan Kong
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  4. Hui Lu
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Contributions

S. W. participated in the data acquisition, performed the model construction and the statistical analysis, and drafted the manuscript. X. C. Z. participated in the study design and was involved in interpreting study findings and implications. Y. K. participated in its design and drafted the manuscript. H.L. supervised all aspects of the study. All authors read and approved the final manuscript.

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Correspondence to Hui Lu.

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Key Messages

1. This study proposes a deep learning-based, plug-and-play method for enhancing spot resolution in spatially resolved transcriptomics (SRT) data using a convolutional neural network (CNN) model and shift-predict operation.

2. The method achieves 9 × superresolution on a human breast cancer SRT dataset and outperforms traditional superresolution techniques.

3. The effectiveness of the proposed method is demonstrated using an external dataset and a simulated dataset, providing deeper insights into gene expression patterns and cellular structures.

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Wang, S., Zhou, X., Kong, Y. et al. Superresolved spatial transcriptomics transferred from a histological context. Appl Intell 53, 31033–31045 (2023). https://doi.org/10.1007/s10489-023-05190-3

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