In-House  Sample Processing

• FFPE Specimens: Expertise in handling FFPE samples from humans and rodents
• Processing of Organized Structures Samples including tumor specimens (core needle biopsies, tissue microarrays, surgical sections) and organoids.
• Sample Qualification: Rigorous quality assessment to ensure optimal processing and reliable results.

Cutting-Edge Xenium Platform

• Precise Region Analysis: Analyze and visualize regions up to 472 mm², validated by expert pathologists.
• Flexible Probe Sets: Choose from Prime 5K (5,001 genes) and IO (360 genes) panels, with the option to add up to 100 custom probes.
• 4 to 8 samples per week depending on the selected panel.

Comprehensive In-House Data Science

• Spatial Gene Expression Maps: High-resolution visualization of gene activity across distinct regions of the tumor.
• Cell Type Identification and Localization: Mapping and categorizing various cell types (e.g., cancer cells, immune cells) within the tumor microenvironment.
• Tumor Microenvironment Characterization: Profiling of signaling pathways, immune infiltration, and extracellular matrix components surrounding the tumor.
• Spatial Heterogeneity and Clonal Architecture: Analysis of genetic and transcriptomic diversity to identify and map distinct clonal populations within the tumor.

Custom Workflow from Sample to Data Analysis

• Collaborative Experimental Design: Partner with our experts to design experiments tailored to your specific scientific questions.
• Sample Preparation: Ship your samples to us, and we will prepare slides that meet Xenium standards before returning the processed material to you.
• Comprehensive Data Integration: Enhance your dataset with additional information from H&E staining and mIHF/IHC assays. 

Single-cell spatial profiling of sarcoma sample

(A) A formalin-fixed paraffin-embedded (FFPE) section of sarcoma tissue was analyzed using the Xenium 5k Prime panel (10X Genomics), which incorporates morphology marker staining for spatial profiling.

To accurately segment and identify individual cells, a deep-learning-based segmentation algorithm was employed in Qupath (B). Single-cell RNA sequencing (scRNA-seq) analysis was then performed to cluster cells based on transcriptomic profiles (C). Each cluster was characterized through the analysis of specific marker genes to determine cell identity (D), which was subsequently mapped back to the tissue for spatial annotation (E).

This mapping enabled comprehensive secondary analyses, including the quantification of marker expression, assessment of cell type abundances, and exploration of cell-cell communication networks within the tumor microenvironment.