Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Nat Methods. 2024 Oct;21(10):1830-1842. doi: 10.1038/s41592-024-02408-1. Epub 2024 Sep 3.
Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.
细胞间通讯（CCC）对于生命的形成和功能至关重要。然而，直到最近，通过引入空间分辨转录组学（SRT）技术，特别是那些实现单细胞分辨率的技术，才有可能准确、高通量地绘制出一个细胞中所有基因的表达如何影响另一个细胞中所有基因的表达。然而，要正确分析如此复杂的数据，仍然存在很大的挑战。在这里，我们引入了一种多实例学习框架 Spacia，通过独特地利用其空间模式，从 SRT 生成的数据中检测 CCC。我们强调了 Spacia 克服流行的 CCC 推断分析工具的基本限制的能力，包括丧失单细胞分辨率、限于配体-受体关系和先前的相互作用数据库、高假阳性率，以及最重要的是，缺乏对多发送方到一接收方范式的考虑。我们评估了 Spacia 对三种商业化的单细胞分辨率 SRT 技术的适用性：MERSCOPE/Vizgen、CosMx/NanoString 和 Xenium/10x。总的来说，Spacia 代表了在推进细胞通讯定量理论方面的显著进展。
