Liao Linbu, Madan Esha, Palma António M, Kim Hyobin, Kumar Amit, Bhoopathi Praveen, Winn Robert, Trevino Jose, Fisher Paul, Brakebusch Cord Herbert, Kim Gahyun, Kim Junil, Gogna Rajan, Won Kyoung Jae
Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
Department of Surgery, School of Medicine, Virginia Commonwealth University, 1200 E Broad St., P.O. Box 980011, Richmond, VA, 23298, USA.
BMC Bioinformatics. 2025 Jun 2;26(1):148. doi: 10.1186/s12859-025-06173-6.
Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates.
We present SC2Spa, a deep learning-based approach that learns intricate spatial relationships from spatial transcriptomics (ST) data. Benchmarking tests show that SC2Spa outperformed other predictors and accurately detected tissue architecture from transcriptome. SC2Spa successfully mapped single cell RNA sequencing (scRNA-seq) to Visium assay, providing an approach to enhance the resolution for low resolution ST data. Our test showed that SC2Spa performs well for various ST technologies and robust to spatial resolution. In addition, SC2Spa can suggest spatially variable genes that cannot be identified from previous approaches.
SC2Spa is a robust and accurate approach to provide single cells with their spatial location and identify spatially meaningful genes.
了解组织内的细胞异质性取决于对其空间背景的认识。然而，将细胞精确映射到其空间坐标仍然具有挑战性。
我们提出了SC2Spa，一种基于深度学习的方法，可从空间转录组学（ST）数据中学习复杂的空间关系。基准测试表明，SC2Spa优于其他预测器，并能从转录组中准确检测组织结构。SC2Spa成功地将单细胞RNA测序（scRNA-seq）映射到Visium分析中，为提高低分辨率ST数据的分辨率提供了一种方法。我们的测试表明，SC2Spa在各种ST技术中表现良好，并且对空间分辨率具有鲁棒性。此外，SC2Spa可以识别出先前方法无法鉴定的空间可变基因。
SC2Spa是一种强大而准确的方法，可提供单个细胞的空间位置并识别具有空间意义的基因。
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