Stitching and registering highly multiplexed whole-slide images of tissues and tumors using ASHLAR.
Stitching and registering highly multiplexed whole-slide images of tissues and tumors using ASHLAR.
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DOI：
10.1093/bioinformatics/btac544
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发表时间：
2022-09-30
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将显微镜图像拼接成马赛克是分析和可视化大型生物标本，特别是人类和动物组织的重要步骤。最近的方法，高度复用成像产生高丛数据从顺序轮的低丛成像。这些多重成像方法有望产生精确的分子单细胞数据和细胞邻域和组织结构的信息。然而，获得具有单细胞精度的马赛克图像需要现有方法不能满足的鲁棒的图像拼接和图像配准能力。我们描述了ASHLAR的开发和测试，ASHLAR是一种Python工具，用于协调拼接和注册103个或更多个人的多路复用图像，以生成准确的整体幻灯片马赛克。ASHLAR从大多数商业显微镜和幻灯片扫描仪读取图像格式，我们表明它比现有的开源和商业软件性能更好。ASHLAR输出标准的OME-TIFF图像，可供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，可以在https://github.com/labsyspharm/ashlar上获得MIT许可证。本文中最新发表的数据可在Sage Synapse上获得，网址为https：//dx.doi.org/10.7303/syn25826362;本文中重新分析的其他先前发表的数据的可用性在补充表S4中描述。一个包含用户指南和测试数据的信息网站可在https://labsyspharm.github.io/ashlar/上找到。 补充数据可在Bioinformatics在线获得。
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.
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影响因子：
64.5
作者：
Goltsev Y;Samusik N;Kennedy-Darling J;Bhate S;Hale M;Vazquez G;Black S;Nolan GP
通讯作者：
Nolan GP
影响因子：
64.5
作者：
Burger ML;Cruz AM;Crossland GE;Gaglia G;Ritch CC;Blatt SE;Bhutkar A;Canner D;Kienka T;Tavana SZ;Barandiaran AL;Garmilla A;Schenkel JM;Hillman M;de Los Rios Kobara I;Li A;Jaeger AM;Hwang WL;Westcott PMK;Manos MP;Holovatska MM;Hodi FS;Regev A;Santagata S;Jacks T
通讯作者：
Jacks T
影响因子：
4.6
作者：
Chalfoun J;Majurski M;Blattner T;Bhadriraju K;Keyrouz W;Bajcsy P;Brady M
通讯作者：
Brady M
影响因子：
5.4
作者：
Dean, Roger T.;Dunsmuir, William T. M.
通讯作者：
Dunsmuir, William T. M.
影响因子：
48
作者：
Giesen, Charlotte;Wang, Hao A. O.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd