Studying heterogeneity in aging and cancer through single-cell gene expression networks

In health, gene expression is strongly regulated to ensure proper function of cells and organs. This leads to strong coordination of gene expression among cells of the same type in a tissue. However, throughout aging, stochastic damage to DNA and gene regulation accumulates and can lead to gene expression discoordination. This process can be accelerated in cancer, where more severe genomic damage is typical.

The recent development of high-throughput technologies that quantify RNA expression of thousands of genes in many discrete cells in parallel, allows us to obtain an intricate view of gene expression coordination among cells. This and international endeavors to use this technology to profile gene expression in all human cell-types and numerous cancers, provides a unique opportunity to study the loss of gene expression coordination during aging and cancer and to identify shared and unique patterns in these two processes. To achieve just that, we developed a network analysis framework to compute, characterize and compare gene expression correlation networks in healthy, aging and cancer tissues. In concordance with our hypothesis, by comparing single-cell RNA-sequencing data from young versus old individuals in multiple organisms, we identify a quantifiable decrease in expression coordination across cells. Beyond aging, we are currently exploring how gene coordination is further deregulated in cancer vs aging. Our network approach, which identifies deregulated gene circuits in these conditions, can potentially provide not only fundamental biological insights, but also new tools for cancer stage determination, prognostics and more.