RT Journal
A1 Mao, Shulin
A1 Su, Jiayu
A1 Wang, Longteng
A1 Bo, Xiaochen
A1 Li, Cheng
A1 Chen, Hebing
T1 A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures
JF Genome Research 
JO Genome Research 
YR 2023 
FD August 01 
VO 33 
IS 8 
SP 1381 
OP 1394 
DO 10.1101/gr.277491.122 
UL http://genome.cshlp.org/content/33/8/1381.abstract 
AB Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and interpret the biological significance of the traits used for estimation. Here we present SCALE, a statistical pipeline that quantifies biological aging in different tissues using explainable features learned from literature and single-cell transcriptomic data. Applying SCALE to the “Mouse Aging Cell Atlas” (Tabula Muris Senis) data, we identified tissue-level transcriptomic aging programs for more than 20 murine tissues and created a multitissue resource of mouse quantitative aging-associated genes. We observe that SCALE correlates well with other age indicators, such as the accumulation of somatic mutations, and can distinguish subtle differences in aging even in cells of the same chronological age. We further compared SCALE with other transcriptomic and methylation “clocks” in data from aging muscle stem cells, Alzheimer's disease, and heterochronic parabiosis. Our results confirm that SCALE is more generalizable and reliable in assessing biological aging in aging-related diseases and rejuvenating interventions. Overall, SCALE represents a valuable advancement in our ability to measure aging accurately, robustly, and interpretably in single cells.