Suppr超能文献

基于 TEM、SEM 和 STEM 的免疫-CLEM 工作流程提供了互补的优势。

TEM, SEM, and STEM-based immuno-CLEM workflows offer complementary advantages.

机构信息

Ramaciotti Centre for Cryo EM, Monash University, Melbourne, VIC, 3800, Australia.

European Molecular Biology Laboratory, Electron Microscopy Core Facility, Heidelberg, Germany.

出版信息

Sci Rep. 2021 Jan 13;11(1):899. doi: 10.1038/s41598-020-79637-9.

Abstract

Identifying endogenous tissue stem cells remains a key challenge in developmental and regenerative biology. To distinguish and molecularly characterise stem cell populations in large heterogeneous tissues, the combination of cytochemical cell markers with ultrastructural morphology is highly beneficial. Here, we realise this through workflows of multi-resolution immuno-correlative light and electron microscopy (iCLEM) methodologies. Taking advantage of the antigenicity preservation of the Tokuyasu technique, we have established robust protocols and workflows and provide a side-by-side comparison of iCLEM used in combination with scanning EM (SEM), scanning TEM (STEM), or transmission EM (TEM). Evaluation of the applications and advantages of each method highlights their practicality for the identification, quantification, and characterization of heterogeneous cell populations in small organisms, organs, or tissues in healthy and diseased states. The iCLEM techniques are broadly applicable and can use either genetically encoded or cytochemical markers on plant, animal and human tissues. We demonstrate how these protocols are particularly suited for investigating neural stem and progenitor cell populations of the vertebrate nervous system.

摘要

鉴定内源性组织干细胞仍然是发育和再生生物学的一个关键挑战。为了在大型异质组织中区分和分子表征干细胞群体,细胞化学细胞标记物与超微结构形态的结合是非常有益的。在这里,我们通过多分辨率免疫相关的光镜和电子显微镜(iCLEM)方法学的工作流程来实现这一点。利用 Tokuyasu 技术的抗原性保存,我们已经建立了稳健的方案和工作流程,并提供了 iCLEM 与扫描电镜(SEM)、扫描透射电镜(STEM)或透射电镜(TEM)联合使用的并排比较。每种方法的应用和优点的评估突出了它们在识别、定量和表征健康和患病状态下的小生物体、器官或组织中的异质细胞群体方面的实用性。iCLEM 技术具有广泛的适用性,可以在植物、动物和人体组织上使用遗传编码或细胞化学标记物。我们展示了这些方案如何特别适合研究脊椎动物神经系统中的神经干细胞和祖细胞群体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c14e/7806999/2eaca5096c23/41598_2020_79637_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验