Spatial Multi-omics: Key to Precision Medicine

Heterogeneous collection of resident host cells, infiltrating immune cells, secreted factors and extracellular matrix within the tumor microenvironment interacts with each other to actively promote cancer progression. The molecular architecture of the microenvironment has prognostic value as it varies based on the grade and type of the tumor. Thus, spatial context holds an unmatched promise in unraveling crucial information that can greatly improve our understanding of the pathological state in patients. Although the spatial context is preserved in immunohistochemistry (IHC) and in situ hybridization (ISH), only a maximum of three biomarkers can be assessed in a single assay. Therefore, conventional methods pose limitations in cancer diagnostics and personalized medicine considering the tissue input requirements.

The use of spatial multi-omics in characterizing the tumor microenvironments is a major leap in the field of cancer. Multiplexing is tissue-sparing and allows quantitative detection of multiple biomarkers at sub-cellular level in a single assay. Aberrations in proteomics and genomics can be easily assessed from the same sample using spatial multi-omics. This multi-dimensional approach provides deeper insights into diagnosis and therapeutic challenges in cancer and holds potential for application to personalized medicine.

HSA-miR-1
HSA-miR-17
HSA-miR-25
HSA-miR-21-3P

Journey Towards Precision Oncology

 

Spatial multi-omics technologies have the potential to improve precision medicine. For example, the spatial expression of multiple target genes or proteins or its post-translational modifications can be quantitatively assessed at a single cell level from patient biopsies. The cell type specific aberrations in gene expression profiles or signaling cascades will help decipher the underlying molecular mechanisms of the disease and also to personalize the therapeutic process. Further, the use of artificial intelligence and several other workflows patients can be categorized based on the pathological state and drug response. Data driven approaches will aid physicians in better diagnosis and to strategize effective precision therapies.

References:

  1. Shin, S. H., Bode, A. M. & Dong, Z. Precision medicine: the foundation of future cancer therapeutics. npj Precision Onc 1, 1–3 (2017).
  2. Allam, M., Cai, S. &Coskun, A. F. Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics. npj Precis. Onc.4, 1–14 (2020).
  3. Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature (2020) doi:10.1038/s41586-019-1876-x.
  4. Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. PNAS 116, 19490–19499 (2019).
  5. Sun, C. et al. Spatially resolved metabolomics to discover tumor-associated metabolic alterations. PNAS 116, 52–57 (2019).
  6. Schork, N. J. Personalized medicine: Time for one-person trials. Nature 520, 609–611 (2015).