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基于细胞边界图像生成用于高分辨率空间转录组学的单细胞基因表达谱。

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

作者信息

Zhang Bohan, Li Mei, Kang Qiang, Deng Zhonghan, Qin Hua, Su Kui, Feng Xiuwen, Chen Lichuan, Liu Huanlin, Fang Shuangsang, Zhang Yong, Li Yuxiang, Brix Susanne, Xu Xun

机构信息

BGI Research, Shenzhen, 518083, China.

BGI Research, Beijing, 102601, China.

出版信息

GigaByte. 2024 Feb 20;2024:gigabyte110. doi: 10.46471/gigabyte.110. eCollection 2024.

DOI:10.46471/gigabyte.110
PMID:38434932
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10905256/
Abstract

UNLABELLED

In spatially resolved transcriptomics, Stereo-seq facilitates the analysis of large tissues at the single-cell level, offering subcellular resolution and centimeter-level field-of-view. Our previous work on StereoCell introduced a one-stop software using cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With advancements allowing the acquisition of cell boundary information, such as cell membrane/wall staining images, we updated our software to a new version, STCellbin. Using cell nuclei staining images, STCellbin aligns cell membrane/wall staining images with spatial gene expression maps. Advanced cell segmentation ensures the detection of accurate cell boundaries, leading to more reliable single-cell spatial gene expression profiles. We verified that STCellbin can be applied to mouse liver (cell membranes) and seed (cell walls) datasets, outperforming other methods. The improved capability of capturing single-cell gene expression profiles 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.

摘要

未标注

在空间分辨转录组学中，Stereo-seq有助于在单细胞水平上分析大型组织，提供亚细胞分辨率和厘米级视野。我们之前关于StereoCell的工作引入了一个一站式软件，该软件使用细胞核染色图像和统计方法为Stereo-seq数据生成高可信度的单细胞空间基因表达谱。随着能够获取细胞边界信息（如细胞膜/细胞壁染色图像）的技术进步，我们将软件更新到了新版本STCellbin。STCellbin利用细胞核染色图像将细胞膜/细胞壁染色图像与空间基因表达图谱对齐。先进的细胞分割确保了准确细胞边界的检测，从而产生更可靠的单细胞空间基因表达谱。我们验证了STCellbin可应用于小鼠肝脏（细胞膜）和种子（细胞壁）数据集，性能优于其他方法。捕获单细胞基因表达谱能力的提高有助于更深入地理解单细胞表型对组织生物学的贡献。

可用性与实现方式

STCellbin的源代码可在https://github.com/STOmics/STCellbin获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/dda3e5c1a5fc/gigabyte-2024-110-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/b2158727a5da/gigabyte-2024-110-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/bb422b60b21c/gigabyte-2024-110-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/b7e39461b811/gigabyte-2024-110-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/de0ee3f98cff/gigabyte-2024-110-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/dda3e5c1a5fc/gigabyte-2024-110-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/b2158727a5da/gigabyte-2024-110-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/bb422b60b21c/gigabyte-2024-110-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/b7e39461b811/gigabyte-2024-110-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/de0ee3f98cff/gigabyte-2024-110-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3be0/10905256/dda3e5c1a5fc/gigabyte-2024-110-g005.jpg

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