Suppr超能文献

histoCAT:多重图像细胞术数据中细胞表型和相互作用的分析

histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data.

作者信息

Schapiro Denis, Jackson Hartland W, Raghuraman Swetha, Fischer Jana R, Zanotelli Vito R T, Schulz Daniel, Giesen Charlotte, Catena Raúl, Varga Zsuzsanna, Bodenmiller Bernd

机构信息

Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.

Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland.

出版信息

Nat Methods. 2017 Sep;14(9):873-876. doi: 10.1038/nmeth.4391. Epub 2017 Aug 7.

Abstract

Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.

摘要

组织的单细胞、空间分辨组学分析有望改变生物医学研究和临床实践。我们开发了一个开源的计算组织学地形细胞术分析工具箱(histoCAT),以实现对完整组织内单个细胞表型、细胞间相互作用、微环境和形态结构的交互式、定量和全面探索。我们通过分析人类乳腺癌组织的高度多重质谱流式细胞术图像,突出了histoCAT的独特能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c8a/5617107/cb50ec5a6b25/emss-73432-f001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验