The integration of mass cytometry with other omics technologies represents a frontier in biomedical research, promising to provide a more comprehensive understanding of cellular biology. This chapter explores the exciting developments in multi-omics approaches, with a focus on how mass cytometry data can be integrated with other high-dimensional datasets.
Multi-omics Approaches Combining Mass Cytometry
The power of integrating mass cytometry with other omics technologies was beautifully demonstrated in a study by Spitzer et al. (2015) published in Science, “An interactive reference framework for modeling a dynamic immune system” (Spitzer et al., 2015, Science, 349(6244), 1259425). This groundbreaking work combined CyTOF with single-cell RNA sequencing to create a comprehensive map of the immune system, revealing new cell states and developmental trajectories.
Building on this foundation, a more recent study by Frohlich et al. (2022) in Nature Biotechnology, “Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics,” integrated CyTOF data with bulk RNA sequencing and clinical information to develop a powerful predictive model for acute myeloid leukemia outcomes (Frohlich et al., 2022, Nature Biotechnology, 40(11), 1650-1658).
As often, papers using CyTOF are most of the time also using single-cell RNA sequencing, since both are deeply analysing immune cells.
Restricted content
You must be logged in and have a valid subscription to see this content. Please visit our subscription page for more info. If you are already a VIP member, be sure you are logged in with the same email address you made your purchase.
