InquiryBreast cancer consists of a complex array of cell types including tumor cells, stromal cells, and immune cells, each of which can present a different phenotype. Moreover, the widespread tumor heterogeneity and different tumor microenvironment can affect breast cancer progression, treatment response, and drug resistance. Therefore, monitoring the heterogeneity occurrence and microenvironment of breast cancer may help facilitate the development of new therapies and improve prognosis.
How we use spatial omics to address important questions in breast cancer research
Because single-cell analysis methods typically do not preserve spatial organization, and spatially resolved imaging methods currently multiplex far fewer measurements. CD Genomics, therefore, combines single-cell sequencing of isolated samples with spatially resolved in situ multiplexed imaging analysis. to simultaneously interrogate the composition and structure of the breast cancer ecosystem and map the molecular profile of tissue samples in the context of the disease.
Figure 1. The applications of single-cell analysis and spatial pathologies in studies of breast cancer heterogeneity. (Na, Z., et al., 2021)
We combine different data patterns to define a high-resolution map of cellular interactions in breast cancer and provide a scheme for generalization across tissues. To elucidate the relationship of different cell subpopulations in breast cancer with each other, with other cells in the tumor microenvironment, and with disease state and progression.
Available to our clients
- Reveal the heterogeneity of breast cancer cells
- Characterize the interactions of breast cancer-associated cell types
- High molecular resolution transcriptional mapping of breast tumors
Technical features and advantages
- Patterns of cell state co-localization are inferred by deconvolution of expression profiles at each site with single-cell data.
- Immunophenotyping uses cellular indexing of transcriptomes and epitopes to provide high-resolution immunoblots.
- Apply expression-based clustering and single-cell data integration to explore spatial expression profiles and cell type interactions in the data.
- Spatial mapping of cell types and states defined in single-cell datasets enables you to investigate the spatial distribution of breast cancer cell types and their co-localization patterns.
- Extract marker genes for each cluster and perform functional enrichment analysis, which allows you to annotate them biologically.
Reference
- Na, Z., et al., (2021). "Breast cancer heterogeneity through the lens of single-cell analysis and spatial pathologies." Seminars in Cancer Biology.
