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通过单细胞分子谱分析对完整组织进行自动细胞类型分类。

Automated cell-type classification in intact tissues by single-cell molecular profiling.

作者信息

Nagendran Monica, Riordan Daniel P, Harbury Pehr B, Desai Tushar J

机构信息

Department of Internal Medicine, Division of Pulmonary & Critical Care, Stanford University School of Medicine, Stanford, United States.

Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States.

出版信息

Elife. 2018 Jan 10;7:e30510. doi: 10.7554/eLife.30510.

Abstract

A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of 'housekeeping' genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research.

摘要

生物学中的一个主要挑战是识别不同的细胞类别并绘制它们在体内的相互作用图谱。组织解离技术能够实现深度单细胞分子分析,但无法提供空间信息。我们开发了一种原位邻近连接杂交技术(PLISH),该技术在组织中具有出色的信号强度、特异性和灵敏度。使用条形码探针和快速标记-图像擦除循环可以获取多重数据集,并通过自动计算单细胞图谱,实现细胞聚类和解剖学重新映射。我们将PLISH应用于完整小鼠肺中约2900个细胞的表达谱分析,识别并定位了已知的细胞类型,包括稀有细胞类型。对细胞进行无监督分类表明,不同细胞类型之间“管家”基因的表达存在差异,对Clara细胞两个亚类的重新映射突出了它们在终末气道中的隔离空间域。通过能够原位对各种RNA种类进行单细胞分析,PLISH可以影响基础研究和医学研究的许多领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e76/5802843/28093b154c36/elife-30510-fig1.jpg

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