Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Nat Methods. 2023 Aug;20(8):1237-1243. doi: 10.1038/s41592-023-01939-3. Epub 2023 Jul 10.
Spatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10-15 cells per spot, recent technologies provide a much denser spot placement leading to subcellular resolution. A key challenge for these newer methods is cell segmentation and the assignment of spots to cells. Traditional image-based segmentation methods are limited and do not make full use of the information profiled by spatial transcriptomics. Here we present subcellular spatial transcriptomics cell segmentation (SCS), which combines imaging data with sequencing data to improve cell segmentation accuracy. SCS assigns spots to cells by adaptively learning the position of each spot relative to the center of its cell using a transformer neural network. SCS was tested on two new subcellular spatial transcriptomics technologies and outperformed traditional image-based segmentation methods. SCS achieved better accuracy, identified more cells and provided more realistic cell size estimation. Subcellular analysis of RNAs using SCS spot assignments provides information on RNA localization and further supports the segmentation results.
空间转录组学有望极大地提高我们对组织架构和细胞间相互作用的理解。虽然当前大多数空间转录组学平台仅提供多细胞分辨率，每个点有 10-15 个细胞，但最近的技术提供了更密集的点放置，从而实现了亚细胞分辨率。这些新技术的一个关键挑战是细胞分割和点到细胞的分配。传统的基于图像的分割方法具有局限性，并且不能充分利用空间转录组学所提供的信息。在这里，我们提出了亚细胞空间转录组学细胞分割（SCS），它将成像数据与测序数据相结合，以提高细胞分割的准确性。SCS 通过使用变压器神经网络自适应地学习每个点相对于其细胞中心的位置，将点分配给细胞。SCS 在两种新的亚细胞空间转录组学技术上进行了测试，表现优于传统的基于图像的分割方法。SCS 实现了更高的准确性，识别出更多的细胞，并提供了更真实的细胞大小估计。使用 SCS 点分配对 RNA 进行亚细胞分析提供了 RNA 定位的信息，并进一步支持分割结果。
