Human PBMC scRNA-seq-based aging clocks reveal ribosome to inflammation balance as a single-cell aging hallmark and super longevity

A metabolic atlas of mouse aging

Humans are living longer, but this is accompanied by an increased incidence of age-related chronic diseases. Many of these diseases are influenced by age-associated metabolic dysregulation, but how metabolism changes in multiple organs during aging in males and females is not known. Answering this could reveal new mechanisms of aging and age-targeted therapeutics. In this study, we describe how metabolism changes in 12 organs in male and female mice at 5 different ages. Organs show distinct patterns of metabolic aging that are affected by sex differently. Hydroxyproline shows the most consistent change across the dataset, decreasing with age in 11 out of 12 organs investigated. We also developed a metabolic aging clock that predicts biological age and identified alpha-ketoglutarate, previously shown to extend lifespan in mice, as a key predictor of age. Our results reveal fundamental insights into the aging process and identify new therapeutic targets to maintain organ health.

Application of AI in biological age prediction

The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.

Mapping Cell Atlases at the Single-Cell Level

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

Extracellular vesicle-derived TP53BP1, CD34, and PBX1 from human peripheral blood serve as potential biomarkers for the assessment and prediction of vascular aging

BACKGROUND

Vascular aging is an important pathophysiological basis for the senescence of various organs and systems in the human body, and it is a common pathogenetic trigger for many chronic diseases in the elderly.

METHODS

The extracellular vesicles (EVs) from young and aged umbilical vein endothelial cells were isolated and identified by qPCR the differential expression levels of 47 mRNAs of genes closely related to aging in the two groups.

RESULTS

There were significant differences in the expression levels of 18 genes (we noted upregulation in PLA2G12A, TP53BP1, CD144, PDE11A, FPGT, SERPINB4, POLD1, and PPFIBP2 and downregulation in ATP2C2, ROBO2, RRM2, GUCY1B1, NAT1-14, VEGFR2, WTAPP1, CD146, DMC1, and GRIK2). Subsequent qPCR identification of the above-mentioned genes in PBMCs and plasma-EVs from the various age groups revealed that the trend in expression levels in peripheral blood plasma-EVs of the different age groups was approximately the same as that in PBMCs. Of these mRNAs, the expression of four genes-PLA2G12A, TP53BP1, OPRL1, and KIAA0895-was commensurate with increasing age. In contradistinction, the expression trend of four genes (CREG1, PBX1, CD34, and SLIT2) was inversely proportional to the increase in age. Finally, by taking their intersection, we determined that the expression of TP53BP1 was upregulated with increasing human age and that CD34 and PBX1 were downregulated with increasing age.

CONCLUSION

Our study indicates that human peripheral blood plasma-EV-derived TP53BP1, CD34, and PBX1 potentially comprise a noninvasive biomarker for assessing and predicting vascular aging.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.