Human Tumor Atlas Network, Harvard Medical School, Boston, MA 02115, USA.
Harvard Ludwig Cancer Center and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA.
Bioinformatics. 2022 Sep 30;38(19):4613-4621. doi: 10.1093/bioinformatics/btac544.
Stitching microscope images into a mosaic is an essential step in the analysis and visualization of large biological specimens, particularly human and animal tissues. Recent approaches to highly multiplexed imaging generate high-plex data from sequential rounds of lower-plex imaging. These multiplexed imaging methods promise to yield precise molecular single-cell data and information on cellular neighborhoods and tissue architecture. However, attaining mosaic images with single-cell accuracy requires robust image stitching and image registration capabilities that are not met by existing methods.
We describe the development and testing of ASHLAR, a Python tool for coordinated stitching and registration of 103 or more individual multiplexed images to generate accurate whole-slide mosaics. ASHLAR reads image formats from most commercial microscopes and slide scanners, and we show that it performs better than existing open-source and commercial software. ASHLAR outputs standard OME-TIFF images that are ready for analysis by other open-source tools and recently developed image analysis pipelines.
ASHLAR is written in Python and is available under the MIT license at https://github.com/labsyspharm/ashlar. The newly published data underlying this article are available in Sage Synapse at https://dx.doi.org/10.7303/syn25826362; the availability of other previously published data re-analyzed in this article is described in Supplementary Table S4. An informational website with user guides and test data is available at https://labsyspharm.github.io/ashlar/.
Supplementary data are available at Bioinformatics online.
将显微镜图像拼接成马赛克是分析和可视化大型生物样本（尤其是人类和动物组织）的重要步骤。最近的高通量成像方法通过连续多轮低通量成像生成高通量数据。这些高通量成像方法有望提供精确的分子单细胞数据以及关于细胞邻居和组织架构的信息。然而，要实现具有单细胞精度的马赛克图像，需要稳健的图像拼接和图像配准功能，而现有的方法无法满足这些需求。
我们描述了 ASHLAR 的开发和测试，这是一种用于协调拼接和注册 103 个或更多个个体的多路复用图像以生成准确的全幻灯片马赛克的 Python 工具。ASHLAR 可以读取来自大多数商业显微镜和幻灯片扫描仪的图像格式，我们表明它的性能优于现有的开源和商业软件。ASHLAR 输出标准的 OME-TIFF 图像，可用于其他开源工具和最近开发的图像分析管道进行分析。
ASHLAR 是用 Python 编写的，可在 MIT 许可证下从 https://github.com/labsyspharm/ashlar 获得。本文所依据的新发布的数据可在 Sage Synapse 上 https://dx.doi.org/10.7303/syn25826362 获得；本文重新分析的其他先前发布的数据的可用性在补充表 S4 中描述。一个带有用户指南和测试数据的信息网站可在 https://labsyspharm.github.io/ashlar/ 获得。
补充数据可在 Bioinformatics 在线获得。
