Individuals of the same age may not age at the same rate. Quantitative biomarkers of aging are valuable tools to measure physiological age, assess the extent of ‘healthy aging’, and potentially predict health span and life span for an individual. Given the complex nature of the aging process, the biomarkers of aging are multilayered and multifaceted. Here, we review the phenotypic and molecular biomarkers of aging. Identifying and using biomarkers of aging to improve human health, prevent age-associated diseases, and extend healthy life span are now facilitated by the fast-growing capacity of multilevel cross-sectional and longitudinal data acquisition, storage, and analysis, particularly for data related to general human populations. Combined with artificial intelligence and machine learning techniques, reliable panels of biomarkers of aging will have tremendous potential to improve human health in aging societies.

Summary: Dietary restriction ( DR ) increases lifespan and attenuates age‐related phenotypes in many organisms; however, the effect of DR on longevity of individuals in genetically heterogeneous populations is not well characterized. Here, we describe a large‐scale effort to define molecular mechanisms that underlie genotype‐specific responses to DR . The effect of DR on lifespan was determined for 166 single gene deletion strains in Saccharomyces cerevisiae . Resulting changes in mean lifespan ranged from a reduction of 79% to an increase of 103%. Vacuolar p H homeostasis, superoxide dismutase activity, and mitochondrial proteostasis were found to be strong determinants of the response to DR . Proteomic analysis of cells deficient in prohibitins revealed induction of a mitochondrial unfolded protein response (mt UPR ), which has not previously been described in yeast. Mitochondrial proteotoxic stress in prohibitin mutants was suppressed by DR via reduced cytoplasmic m RNA translation. A similar relationship between prohibitins, the mt UPR , and longevity was also observed in C aenorhabditis elegans . These observations define conserved molecular processes that underlie genotype‐dependent effects of DR that may be important modulators of DR in higher organisms.

Aging is associated with many complex diseases. Reliable prediction of the aging process is important for assessing the risks of aging-associated diseases. However, despite intense research, so far there is no reliable aging marker. Here we addressed this problem by examining whether human 3D facial imaging features could be used as reliable aging markers. We collected > 300 3D human facial images and blood profiles well-distributed across ages of 17 to 77 years. By analyzing the morphological profiles, we generated the first comprehensive map of the aging human facial phenome. We identified quantitative facial features, such as eye slopes, highly associated with age. We constructed a robust age predictor and found that on average people of the same chronological age differ by ± 6 years in facial age, with the deviations increasing after age 40. Using this predictor, we identified slow and fast agers that are significantly supported by levels of health indicators. Despite a close relationship between facial morphological features and health indicators in the blood, facial features are more reliable aging biomarkers than blood profiles and can better reflect the general health status than chronological age.

Summary: Caenorhabditis elegans is a leading model organism for studying the basic mechanisms of aging. Progress has been limited, however, by the lack of an automated system for quantitative analysis of longevity and mean lifespan. To address this barrier, we developed ‘WormFarm’, an integrated microfluidic device for culturing nematodes. Cohorts of 30–50 animals are maintained throughout their lifespan in each of eight separate chambers on a single WormFarm polydimethylsiloxane chip. Design features allow for automated removal of progeny and efficient control of environmental conditions. In addition, we have developed computational algorithms for automated analysis of video footage to quantitate survival and other phenotypes, such as body size and motility. As proof‐of‐principle, we show here that WormFarm successfully recapitulates survival data obtained from a standard plate‐based assay for both RNAi‐mediated and dietary‐induced changes in lifespan. Further, using a fluorescent reporter in conjunction with WormFarm, we report an age‐associated decrease in fluorescent intensity of GFP in transgenic worms expressing GFP tagged with a mitochondrial import signal under the control of the myo‐3 promoter. This marker may therefore serve as a useful biomarker of biological age and aging rate.

Single-cell RNA sequencing (scRNA-seq) is a powerful method for dissecting intercellular heterogeneity during development. Conventional trajectory analysis provides only a pseudotime of development, and often discards cell-cycle events as confounding factors. Here using matched cell population RNA-seq (cpRNA-seq) as a reference, we developed an “iCpSc” package for integrative analysis of cpRNA-seq and scRNA-seq data. By generating a computational model for reference “biological differentiation time” using cell population data and applying it to single-cell data, we unbiasedly associated cell-cycle checkpoints to the internal molecular timer of single cells. Through inferring a network flow from cpRNA-seq to scRNA-seq data, we predicted a role of M phase in controlling the speed of neural differentiation of mouse embryonic stem cells, and validated it through gene knockout (KO) experiments. By linking temporally matched cpRNA-seq and scRNA-seq data, our approach provides an effective and unbiased approach for identifying developmental trajectory and timing-related regulatory events.