Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images

  1. Bohan Zhang
  2. Mei Li
  3. Qiang Kang
  4. Zhonghan Deng
  5. Hua Qin
  6. Kui Su
  7. Xiuwen Feng
  8. Lichuan Chen
  9. Huanlin Liu
  10. Shuangsang Fang
  11. Yong Zhang
  12. Yuxiang Li
  13. Susanne Brix
  14. Xun Xu

  • Curated by GigaByte

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    Editors Assessment:

    This paper describes a new spatial transcriptomics method that that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. STCellbin is an update of StereoCell, now using a more advanced cell segmentation technique, so more accurate cell boundaries can be obtained, allowing more reliable single-cell spatial gene expression profiles to be obtained. After peer review more comparisons were added and more description given on what was upgraded in this version to convince the reviewers. Demonstrating it is a more reliable method, particularly for analyzing high-resolution and large-field-of-view spatial transcriptomic data. And extending the capability to automatically process Stereo-seq cell membrane/wall staining images for identifying cell boundaries.

    This evaluation refers to version 2 of the preprint

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Abstract

Stereo-seq is a cutting-edge technique for spatially resolved transcriptomics that combines subcellular resolution with centimeter-level field-of-view, serving as a technical foundation for analyzing large tissues at the single-cell level. Our previous work presents the first one-stop software that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With recent advancements in Stereo-seq technology, it is possible to acquire cell boundary information, such as cell membrane/wall staining images. To take advantage of this progress, we update our software to a new version, named STCellbin, which utilizes the cell nuclei staining images as a bridge to align cell membrane/wall staining images with spatial gene expression maps. By employing an advanced cell segmentation technique, accurate cell boundaries can be obtained, leading to more reliable single-cell spatial gene expression profiles. Experimental results verify that STCellbin can be applied on the mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets and outperforms other competitive methods. The improved capability of capturing single cell gene expression profiles by this update results in a deeper understanding of the contribution of single cell phenotypes to tissue biology.

Availability & Implementation

The source code of STCellbin is available at https://github.com/STOmics/STCellbin .

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  1. GigaByte

    Editors Assessment:

    This paper describes a new spatial transcriptomics method that that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. STCellbin is an update of StereoCell, now using a more advanced cell segmentation technique, so more accurate cell boundaries can be obtained, allowing more reliable single-cell spatial gene expression profiles to be obtained. After peer review more comparisons were added and more description given on what was upgraded in this version to convince the reviewers. Demonstrating it is a more reliable method, particularly for analyzing high-resolution and large-field-of-view spatial transcriptomic data. And extending the capability to automatically process Stereo-seq cell membrane/wall staining images for identifying cell boundaries.

    This evaluation refers to version 2 of the preprint

  2. GigaByte

    ABSTRACTStereo-seq is a cutting-edge technique for spatially resolved transcriptomics that combines subcellular resolution with centimeter-level field-of-view, serving as a technical foundation for analyzing large tissues at the single-cell level. Our previous work presents the first one-stop software that utilizes cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With recent advancements in Stereo-seq technology, it is possible to acquire cell boundary information, such as cell membrane/wall staining images. To take advantage of this progress, we update our software to a new version, named STCellbin, which utilizes the cell nuclei staining images as a bridge to align cell membrane/wall staining images with spatial gene expression maps. By employing an advanced cell segmentation technique, accurate cell boundaries can be obtained, leading to more reliable single-cell spatial gene expression profiles. Experimental results verify that STCellbin can be applied on the mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets and outperforms other competitive methods. The improved capability of capturing single cell gene expression profiles by this update results in a deeper understanding of the contribution of single cell phenotypes to tissue biology.

    This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.110) as part of our Spatial Omics Methods and Applications series (https://doi.org/10.46471/GIGABYTE_SERIES_0005), and has published the reviews under the same license as follows:

    Reviewer 1. Chunquan Li

    Stereo-seq, an advanced spatial transcriptomics technique, allows detailed analysis of large tissues at the single-cell level with precise subcellular resolution. Author's prior software was groundbreaking, generating robust single-cell spatial gene expression profiles using cell nuclei staining images and statistical methods. They've enhanced their software to STCellbin, using cell nuclei images to align cell membrane/wall staining images. This update employs improved cell segmentation, ensuring accurate boundaries and more dependable single-cell spatial gene expression profiles. Successful tests on mouse liver and Arabidopsis seed datasets demonstrate STCellbin's effectiveness, enabling a deeper insight into the role of single-cell characteristics in tissue biology. However, I do have some suggestions and questions about certain parts of the manuscript.

    1. The authors should show the advantages and performance of STCellbin compared to other methods, such as its computational efficiency, accuracy, and suitability for various image types.
    2. To comprehensively assess the performance of STCellbin, the authors should consider integrating other commonly used cell segmentation evaluation metrics, such as F1-score, Dice coefficient, and so forth.
    3. To ensure the completeness and reproducibility of the data analysis, more detailed information regarding the clustering analysis of the single-cell spatial gene expression maps generated through STCellbin is requested. This information should encompass methods, parameters, and results such as cluster type annotations.
    4. The authors can use simpler and clearer language and terminology to describe the image registration process in the methods section, ensuring that readers can easily understand the workflow and principles of image registration.

    Reviewer 2. Zhaowei Wang

    In this manuscript, the authors propose STCellbin to generate single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images. The experiment results on mouse liver and Arabidopsis seed datasets prove the good performance of STCellbin. The topic is significant and the proposed method is feasible. However, there are still some concerns and problems to be improved and clarified.

    (1) STCellbin is an update version of StereoCell, but the explanation of StereoCell is not sufficient. The authors should give a more detailed explanation of StereoCell, such as its input and main process. (2) The authors list some existing dyeing methods in Lines 52-53, Page 3. They should clarify that these methods are used for nuclei staining, which differentiate them from the cell membrane/wall staining methods of following content. It can provide a more accurate explanation for readers and users. (3) The authors share the GitHub repository of STCellbin, and I noticed that when executing STCellbin, the input only requires the path of image data and spatial gene expression data, the path of the output results, and the chip number. Are there other adjustable parameters? (4) In Page 5, Line 85, “steps” should be “step”, and in Page 8, Line 145, “must” would be better revised to “should”. Moreover, the writing of “stained image” and “staining image” should be consistent.

  3. Version published to 10.1101/2023.12.25.573324v2 on bioRxiv
  4. Version published to 10.1101/2023.12.25.573324v1 on bioRxiv