Spatial Omics Solutions for Cancer and Oncology

Spatial Omics Solutions for Cancer and Oncology

Cancer, which encompasses a wide range of diseases characterized by the dynamic evolution of different cellular entities interacting in their environment, remains one of the most daunting challenges in precision medicine today. Understanding tumor heterogeneity promises to address prominent questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analysis, particularly RNA sequencing and other genomic modalities, cannot provide a complete picture of tumor biology.

How we are using spatial-omics technologies to advance the next generation of cancer and oncology research

CD Genomics wants to facilitate the entire workflow, from genome selection, sample preparation, and the instrument used to downstream analysis, by offering a diverse range of solutions. Such combined data provides a more complete picture of the transcriptional diversity and spatial organization of individual cells in the visual region. Provides high-resolution spatial context of cells as well as more accurate detection and quantification of weakly expressed genes. Enables our customers to not only view transcripts but also elucidate chromatin structure, thus exploring the relationship between chromatin organization, transcriptional activity, and single-cell nuclear structure.

Technology features and benefits

  • Provides our clients with more phenotypic, biomarker, and sample information at faster project completion.
  • Allows spatial phenotyping, enabling our clients to visualize and quantify dozens of biomarkers in a single tissue sample, while we also provide cellular and subcellular details.
  • Allows our clients to see how the same cell types behave differently depending on their cellular microenvironment and to identify different cell clustering patterns that may predict clinical outcomes.
  • An integrated scheme of different multiplexed techniques can define the spatiotemporal abundance of different molecules, from RNA transcripts to protein biomarkers and cellular metabolites. Ultimately, this helps to resolve the complexity of the tumor environment in its interactions with cells surrounding the tissue.
  • Quantitative resolution of RNA transcript abundance and location in the tumor microenvironment can highlight subtle differences in morphologically similar or even identical regions to reveal tumor microenvironment complexity.
  • Combinations on the same tissue section and between different marker combinations can reveal regulatory patterns and association relationships that are not detected by other methods.

We do it better

We are committed to expanding a platform of spatial-omics solutions with multifaceted applications across multiple dimensions and breadth. CD Genomics is experimenting with combining deep learning and pattern recognition algorithms with spatial-omics data. to identify specific spatial patterns of gene expression and predict the transcriptome based on histopathology. to drive spatial omics in many aspects to provide new perspectives to speed up breakthroughs in spatial biology.

For research use only, not intended for any clinical use.

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