PO.BCS01.13 · 生物信息与计算
STCS：空间转录组学细胞分割在多张切片上优于现有方法
STCS: Spatial transcriptomics cell segmentation outperforms existing methods on multiple slides
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
空间转录组学（ST）长期以来被公认为一种先进技术，能够提供超出单细胞 RNA 测序所能获得的空间信息洞察。然而，广泛使用的基于测序的 ST 方法无法提供细胞水平的数据，因为其结果被聚合到离散的区块（bin）中，而非分配到单个细胞。随着 Visium HD 和其他亚细胞分辨率平台的出现，准确的细胞分割对于提取具有生物学意义的细胞水平信息变得至关重要。在此，我们提出了 STCS（空间转录组学细胞分割），一个专为高分辨率 ST 数据量身定制的分割框架。我们使用一张同时具有 Visium HD 和 Xenium 结果的切片，将 STCS 与若干现有方法（包括 STHD、bin2cell 和 Space Ranger）进行了基准测试。使用真实的 Xenium 细胞边界注释进行评估表明，STCS 提供了最佳性能，在细胞类型预测中达到 40% 的准确率，并显示出最低的空间混乱评分（一种量化聚类空间连续性程度的指标）。我们还将 STCS 应用于另一张来自小鼠肠道再生模型的 Visium HD 切片，该切片包含辐射后不同时间点的组织。与默认的 Visium HD 分区相比，STCS 分割的细胞在不同时间点显示出明显的转录差异，并识别出若干稀有免疫细胞类型。因此，诸如空间细胞间相互作用推断和区域模式表征等下游分析可以在细胞水平进行，从而纳入更多细胞类型以及更多免疫相关通路，例如借助 STCS 识别的 JAK-STAT 通路。
此外，STCS 具有通用性，可应用于其他基于测序的 ST 方法，如提供纳米级分辨率的 Stereo-seq。它也是一个开源工具，具有可针对不同组织类型调整的参数。总之，STCS 是一个稳健且灵活的细胞分割工具，为从高分辨率、基于测序的 ST 数据集中获取具有生物学意义的细胞水平信息提供了一站式解决方案。
查看英文原文 English abstract
Spatial transcriptomics (ST) has long been recognized as an advanced technique that provides insights on spatial information beyond what can be obtained from single-cell RNA sequencing. However, widely used sequencing-based ST approaches cannot provide cell level data because their results are aggregated into discrete bins rather than assigned to individual cells. With the advent of Visium HD and other subcellular-resolution platforms, accurate cell segmentation has become essential for extracting biologically meaningful, cell-level information.Here, we present STCS (Spatial Transcriptomics Cell Segmentation), a segmentation framework tailored for high-resolution ST data. We benchmarked STCS against several existing methods-including STHD, bin2cell, and Space Ranger-using a slide with both Visium HD and Xenium results. Evaluation using ground-truth Xenium cell boundary annotations demonstrated that STCS delivers the best performance, achieving 40% accuracy in cell-type prediction and showing the lowest spatial chaos score, a metric that quantifies how spatially continuous clusters are. We also applied STCS to another Visium HD slide from mouse intestinal regeneration model which contains tissue from different time points after radiation. Compared to default Visium HD binning, STCSsegmented cells show clear transcriptional differences by timepoints and identify several rare immune cell types. As a result, downstream analyses such as spatial cell-cell interaction inference and regional pattern characterization can be done in cell level which include more cell types and more immune related pathways like JAK-STAT pathway with STCS.
In addition, STCS is versatile and can be applied to other sequencing-based ST methods like Stereo-seq, which offers nanometer-scale resolution. And it's also an open-source tool with adjustable parameters for different tissue types.In summary, STCS is a robust and flexible cell segmentation tool that provides a one-stop solution for deriving biologically meaningful, cell-level information from high-resolution sequencing-based ST datasets.
利益披露 Disclosure
X. Hu, None..
F. Zhan, None..
L. C. Wu, None..
J. Gonzalez, None..
C. Sun, None..
R. Ofer, None..
T. Tran, None..
M. Verzi, None..
J. Yang, None.