Si Yichen, Lee ChangHee, Hwang Yongha, Yun Jeong H, Cheng Weiqiu, Cho Chun-Seok, Quiros Miguel, Nusrat Asma, Zhang Weizhou, Jun Goo, Zöllner Sebastian, Lee Jun Hee, Kang Hyun Min
Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA.
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
bioRxiv. 2023 Nov 7:2023.11.04.565621. doi: 10.1101/2023.11.04.565621.
Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.
空间转录组学（ST）技术已经取得进展，能够在大面积上以亚微米分辨率进行全转录组范围的基因表达分析。高分辨率ST数据的分析严重依赖基于图像的细胞分割或网格化，由于细胞大小和形状的多样性和不规则性，这在复杂组织中常常失败。现有的无分割分析方法仅适用于小区域和少数基因，限制了它们在高通量研究中的效用。在此，我们提出了FICTURE，一种无分割的空间分解方法，它可以处理用数十亿个亚微米分辨率空间坐标标记的全转录组数据。FICTURE比现有方法效率高几个数量级，并且与基于测序和基于成像的ST数据都兼容。FICTURE揭示了具有挑战性的组织的微观ST结构，例如在先前方法失败的真实数据中的血管、纤维化、肌肉和脂质丰富区域。FICTURE的跨平台通用性、可扩展性和精度使其成为探索高分辨率ST的强大工具。
