Ciências da vida | Aprenda e compartilhe | Leica Microsystems

Ciências da vida

Ciências da vida

Este é o lugar para expandir seus conhecimentos, recursos de pesquisa e aplicações práticas de microscopia em vários campos científicos. Saiba como obter visualização precisa, interpretação de imagens e avanços na pesquisa. Encontre informações perspicazes sobre microscopia avançada, técnicas de geração de imagens, preparação de amostras e análise de imagens. Os tópicos abordados incluem biologia celular, neurociência e pesquisa do câncer, com foco em aplicações e inovações de ponta.

Dapi – Nucleus, GFP – Plasma Membrane, Thickness 100µm, 63x objektive, 469 Z planes, 2 channels, THUNDER Imager 3D Cell Culture. Courtesy M.Sc. Dana Krauß, Medical University of Vienna (Austria).

How Efficient is your 3D Organoid Imaging and Analysis Workflow?

Organoid models have transformed life science research but optimizing image analysis protocols remains a key challenge. This webinar explores a streamlined workflow for organoid research, starting…

How did Laser Microdissection enable Pioneering Neuroscience Research?

Dr. Marta Paterlini, a Senior Scientist at the Karolinska Institute, shares her experience of using laser microdissection (LMD) in groundbreaking research into adult human neurogenesis and offers…

Multiplexed Cell DIVE imaging of selected clusters and unique cell populations identified in mucinous cystadenocarcinoma of the ovary.

A Meta-cancer Analysis of the Tumor Spatial Microenvironment

Learn how clustering analysis of Cell DIVE datasets in Aivia can be used to understand tissue-specific and pan-cancer mechanisms of cancer progression

Multiplexed Cell DIVE imaging of Colon Adenocarcinoma (CAC) tissue. A panel of approximately 30 biomarkers targeted towards various leukocyte lineages, epithelial, stromal, and endothelial cell types was utilized to characterize the tumor immune microenvironment in human colon adenocarcinoma (CAC) tissue.

Mapping the Landscape of Colorectal Adenocarcinoma with Imaging and AI

Discover deep insights in colon adenocarcinoma and other immuno-oncology realms through the potent combination of multiplexed imaging of Cell DIVE and Aivia AI-based image analysis

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…

Single cell datasets

Exploring Subcellular Spatial Phenotypes with SPARCS

Discover spatially resolved CRISPR screening (SPARCS), a platform for microscopy-based genetic screening for spatial subcellular phenotypes at the human genome scale.

THY1-EGFP labeled neurons in mouse brain processed using the PEGASOS 2 tissue clearing method, imaged on a Leica confocal microscope. Neurons were traced using Aivia’s 3D Neuron Analysis – FL recipe. Image credit: Hu Zhao, Chinese Institute for Brain Research.

Unlocking Insights in Complex and Dense Neuron Images Guided by AI

The latest advancement in Aivia AI image analysis software provides improved soma detection, additional flexibility in neuron tracing, 3D relational measurement including Sholl analysis and more.

Microscopy for neuroscience research

What are the Challenges in Neuroscience Microscopy?

eBook outlining the visualization of the nervous system using different types of microscopy techniques and methods to address questions in neuroscience.

AI-based workflow for fast rare event detection in living biological samples using Autonomous Microscopy powered by Aivia

AI Microscopy Enables the Efficient Detection of Rare Events

Localization and selective imaging of rare events is key for the investigation of many processes in biological samples. Yet, due to time constraints and complexity, some experiments are not feasible…