Cell Mapping with Advanced Multiplexing & Spatial Profiling

17 Jun 2025 09:00 UTC

United States

Webinar

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Spatial profiling introduces an innovative approach to drug discovery and translational research by employing sophisticated techniques that visualize and analyze the spatial relationships within and between cells. This method provides essential insights into the biological context of interactions, such as cell-cell and protein-protein relationships, which are critical for understanding disease mechanisms and various cell states. While spatial profiling holds great promise, its success depends on selecting the right reagents and imaging technologies to generate high-quality, meaningful insights. 


This presentation will highlight how selecting the right tools can help researchers uncover key insights of cellular biology using a multiplexed and spatial profiling approach. You will hear about the work from Wayne Stallaert’s lab at the University of Pittsburgh, which is visualizing cell cycle plasticity to generate proteomic signatures and cell cycle maps. Dr. Stallaert will present how his lab is advancing biomarker discovery through the integration of Abcam’s validated conjugated antibodies and Leica Microsystems’ imaging and sample preparation technologies. By enabling high-content, spatially resolved profiling, these tools support his team’s investigation into cell cycle dynamics and are instrumental in uncovering mechanisms of resistance to RAS/ERK and CDK inhibitors in pancreatic ductal adenocarcinoma (PDAC) and breast cancers. 

 

Learning Objectives:

  • Learn how Abcam’s antibody portfolio of validated conjugation-ready antibodies ensures unrivaled reproducibility, scalability and ease of use for multiplex panel design
  • Learn how Leica Microsystems’s Cell DIVE is purpose-built for multiplexed, spatial profiling for cell state segmentation experiments
  • Explore a Case Study from Wayne Stallaert, Ph.D. (University of Pittsburgh) titled: Visualizing cell cycle plasticity in human cells and tissues using highly-multiplexed single-cell imaging
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