Leica Microsystems: 显微镜科学与教学知识中心

显微镜科学与教学知识中心

徕卡显微系统的知识库提供有关显微镜学科的科学研究和教学材料。内容旨在对显微镜初学者、有经验的显微镜操作实践者和使用显微镜的科学家在他们的日常工作和实验有所帮助。这里有探索交互式教程和应用笔记，你可以找到你需要的显微镜的基础知识以及前沿技术——快来加入徕卡显微知识社区，分享您的专业知识！

[Translate to chinese:] Multiplexed Cell DIVE imaging of Adult Human Alzheimer’s brain tissue section demonstrating expression of markers specific to astrocytes (GFAP, S100B), microglia (TMEM119, IBA1), AD-associated markers (p-Tau217, β-amyloid) and immune cells such as CD11b+, CD163+, CD4+, and HLA-DRA+, clustered around the β-amyloid plaques.

Spatial Analysis of Neuroimmune Interactions in Alzheimer’s Disease

Alzheimer’s disease (AD) is a complex neurodegenerative disorder characterized by neurofibrillary tangles, β-amyloid plaques, and neuroinflammation. These dysfunctions trigger or are exacerbated by…

A Guide to Spatial Biology

What is spatial biology, and how can researchers leverage its tools to meet the growing demands of biological questions in the post-omics era? This article provides a brief overview of spatial biology…

[Translate to chinese:] Multiplexed Cell DIVE imaging to characterize the spatial landscape in Human Alzheimer’s Cortical Tissue

使用空间多重化探测人类阿尔茨海默病皮层切片

阿尔茨海默病（AD）是最常见的神经退行性疾病，其特征是认知功能的逐渐下降。对 AD 大脑的空间分析可能揭示细胞关系，从而促进对疾病病因的更好理解。本研究捕捉了 AD 皮层组织成分的全球概述，并强调了 Cell DIVE 成像的简化工作流程，从数据采集到使用 Aivia 软件的基于人工智能的分析，最终实现更快的洞察。

2D slice of colon cancer tissue stained with 30 markers and imaged using the Cell DIVE system. Analysis performed using Aivia 13’s new multiplex cell detection recipe and automatic clustering tool. Each phenotype denoted in a different color.

Transforming Multiplexed 2D Data into Spatial Insights Guided by AI

Aivia 13 handles large 2D images and enables researchers to obtain deep insights into microenvironment surrounding their phenotypes with millions of detected objects and automatic clustering up to 30…

Protist Paramecium (Paramecium tetraurelia) stained to show the nucleus

复杂3D数据集——人工智能赋能的空间数据分析

本期MicaCam为您提供切实的建议，教您从显微镜图像中提取可发表级别的分析结果。本期的特邀嘉宾来自徕卡显微系统的Luciano Lucas，他将为大家展示如何使用MICA的AI赋能软件进行图像分析。他将深度分析两张MICA的3D成像，探究不同可见生物元素之间的空间关系。本期的最后将会介绍如何创作高保真视频动画以及其他可用于发表文章的结果。