CosMx™ SMI vs. Xenium: Comparative Analysis of Breast Tissue
A Comparative Analysis: CosMx™ SMI versus Xenium – A Comprehensive Breast Cancer Study
Dive into the comparative analysis of breast cancer tissue samples with CosMx SMI & Xenium using data from Prof. Yutaka Suzuki at the University of Tokyo.
The CosMx™ SMI and decoder probes are not offered and/or delivered to the Federal Republic of Germany for use in the Federal Republic of Germany for the detection of cellular RNA, messenger RNA, microRNA, ribosomal RNA and any combinations thereof in a method used in fluorescence in situ hybridization for detecting a plurality of analytes in a sample without the consent of the President and Fellows of Harvard College (Harvard Corporation) as owner of the German part of EP 2 794 928 B1. The use for the detection of cellular RNA, messenger RNA, microRNA, ribosomal RNA and any combinations thereof is prohibited without the consent of the President and Fellows of Harvard College (Harvard Corporation).
Are you ready to witness a robust comparative study for single-cell spatial analysis? To provide a clear picture, we have conducted a comprehensive comparison between CosMx SMI and Xenium using the publicly available data, generated from same breast cancer samples, from Prof. Suzuki’s lab at the University of Tokyo.
Watch the video to see how CosMx SMI outperforms Xenium when it comes to delivering vital biological insights:
Conduct a comparison at your own pace by reviewing the full datasets from Dr. Suzuki’s website:
Key Takeaways from the study:
- Sensitivity (Transcript Counts): CosMx SMI delivers up to 5 times more transcripts per cell and over 10 times more transcripts per µm² compared to Xenium.
- Genomic Breadth (Panel Plex): CosMx SMI 1K Universal panel outshines Xenium’s Breast panel, offering 3.7 times the number of unique genes per cell.
- Data Accuracy with Cell Segmentation: Our cutting-edge cell segmentation leverages RNA, proteins, machine learning, and artificial intelligence to provide the most precise cell segmentation. Say goodbye to outdated nuclear expansion methods.
Why is this important?
- Questionable Cell Sizes: Precise cell segmentation is the most important parameter when determining data accuracy. Cell segmentation errors are by far the largest source of error in single-cell spatial analysis.
- Incorrect Cell Transcript Assignment: If the cell transcripts assigned to each cell are incorrect, downstream biological processes, such as cell typing, are inaccurately represented.
But we encourage you to be the judge. Download the data from Dr. Suzuki’s website to draw your own conclusions. We believe in transparency and empowering researchers like you to make a difference in the world of science.