A Better Way of Measuring Senescent Cell Burden Across Tissues and Species
Researchers here propose a better way of measuring the burden of cellular senescence in aged tissues, one that works well across different tissues and species. It is complicated, involving expression of many genes, but the existing simple metrics, such as measurement of senescence-associated beta-galactosidase levels, are increasingly thought inadequate to the task. Senescent cells likely vary in character and metabolism between tissues in ways that have become meaningful now that researchers are past the period of early validation of therapies targeting senescent cells. Now it is important to obtain a much better idea as to the effectiveness of various potential treatments in mice or humans than is presently the case.
Cellular senescence is now recognized as a fundamental mechanism of aging in animals and humans. Senescent cells can develop a senescence-associated secretory phenotype (SASP), consisting of pro-inflammatory cytokines, chemokines, extracellular matrix-degrading proteins, and other factors that have deleterious paracrine and systemic effects. Further, because senescent cells accumulate in multiple tissues in temporal and spatial synchrony with age-associated functional decline in both animals and humans, they have been hypothesized to drive the deterioration linked to numerous chronic diseases. Importantly, the SASP as a feature of cellular senescence represents not just a locally or systemically detrimental set of factors that, in the aging organism, cause physical, metabolic, and cognitive decline, but is also a therapeutic target of interest. Thus, given the broad availability of next-generation sequencing, there is considerable interest in monitoring responses to senolytic treatments. However, this has been challenging, especially at the single cell level. In part, this is due to an imprecise definition of the heterogeneous population of senescent cells and their associated SASP which complicates appropriate monitoring of senescent cell clearance.
Due to variations in the composition of a "senescence gene set" in the current literature, in the present study we sought to identify commonly regulated genes in various age-related datasets in a transcriptome-wide approach that included whole-transcriptome as well as single cell RNA-sequencing (scRNA-seq). Based on an extensive review of the literature, we defined a panel of 125 genes as our senescence gene set ("SenMayo"), which we then validated in our own as well as publicly available datasets of tissues from aged humans and mice, including changes in this gene set following the clearance of senescent cells. Recognizing the difficulty of identifying senescent cells within scRNA-seq analyses, we next applied SenMayo to available scRNA-seq data from human and murine bone marrow/bone hematopoietic and mesenchymal cells, ascertained the identity of the senescent cells in these analyses, and characterized the communication patterns of senescent hematopoietic or mesenchymal cells with other cells in their microenvironment. Finally, we experimentally validated key predictions from our in silico analyses in a mouse model of aging and following genetic clearance of senescent cells.
We hope that SenMayo will help researchers to detect senescent cells within their datasets. Even more, that senolytic therapies can be properly monitored with SenMayo.