Single-molecule imaging at the tissue scale has revolutionized our understanding of biology by providing unprecedented insight into the molecular expression of individual cells and their spatial organization within tissues. However, achieving precise image stitching at the single-molecule level remains a challenge, primarily due to heterogeneous background signals and dim labeling signals in single-molecule images. This paper introduces Spot-Based Global Registration (SBGR), a novel strategy that shifts the focus from raw images to identified molecular spots for high-resolution image alignment. The use of spot-based data enables straightforward and robust evaluation of the credibility of estimated translations and stitching performance. The method outperforms existing image-based stitching methods, achieving subpixel accuracy (83 ± 36 nm) with exceptional consistency. Furthermore, SBGR incorporates a mechanism to surgically remove duplicate spots in overlapping regions, maximizing information recovery from duplicate measurements. In conclusion, SBGR emerges as a robust and accurate solution for stitching single-molecule resolution images in tissue-scale spatial transcriptomics, offering versatility and potential for high-resolution spatial analysis.

中文翻译：

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基于点的全局配准，用于组织尺度空间转录组学的单分子分辨率图像的亚像素拼接

组织尺度的单分子成像为单个细胞的分子表达及其在组织内的空间组织提供了前所未有的见解，彻底改变了我们对生物学的理解。然而，在单分子水平上实现精确的图像拼接仍然是一个挑战，这主要是由于单分子图像中的异质背景信号和暗淡的标记信号。本文介绍了基于点的全局配准（SBGR），这是一种新颖的策略，它将焦点从原始图像转移到已识别的分子点以进行高分辨率图像对齐。使用基于点的数据可以对估计翻译和拼接性能的可信度进行直接而可靠的评估。该方法优于现有的基于图像的拼接方法，实现了亚像素精度 (83 ± 36 nm) 和卓越的一致性。此外，SBGR 还采用了一种机制，可以通过手术去除重叠区域中的重复点，从而最大限度地从重复测量中恢复信息。总之，SBGR 成为一种强大而准确的解决方案，用于在组织尺度空间转录组学中拼接单分子分辨率图像，为高分辨率空间分析提供了多功能性和潜力。