Longevity Papers

Extracellular matrix (ECM), once regarded as a passive structural scaffold, is now recognized as a key hallmark of aging. In the context of female reproductive aging, ECM remodeling acts as a pivotal driver of functional deterioration. This review outlines how age-associated ECM alterations, including collagen cross-linking, elastin degradation, and perturbed biomechanics, orchestrate ovarian aging through the mechanical activation of Hippo signaling, compromise endometrial receptivity via dysregulated matrix metalloproteinase activity, and impair embryo invasion by altering ligand presentation. We also discuss emerging ECM-targeted strategies, such as decellularized scaffolds, engineered hydrogels, and 3D-bioprinted matrices, which have demonstrated potential for rejuvenating reproductive function in preclinical models. Furthermore, matrisome-based biomarkers provide novel prognostic insights into reproductive outcomes. Collectively, these advances identify the ECM as a promising target for innovative, non-hormonal interventions aimed at extending female reproductive longevity.

Aging clock models have emerged as a crucial tool for measuring biological age, with significant implications for anti-aging interventions and disease risk assessment. However, human aging clock models that offer single-cell resolution and account for cell and tissue heterogeneities remain underdeveloped. This study introduces scAgeClock, a novel gated multi-head attention neural network-based single-cell aging clock model. Leveraging a large-scale dataset of over 16 million single-cell transcriptome profiles from more than 40 human tissues and 400 cell types, scAgeClock demonstrates improved age prediction accuracy compared to baseline methods. Nearly half of the tissue-level cell types exhibit mean absolute errors of <10 years, with substantial variability in prediction accuracy observed across different cell types. Feature importance analysis reveals enrichment of aging clock genes related to ribosome, translation, defense response, viral life cycle, programmed cell death, and COVID-19 disease. A novel metric, the aging deviation index proposed by this study, revealed deceleration of ages in cells with higher differentiation potencies and tumor cells in higher phases or under metastasis, while acceleration of ages was observed in skin cells. Furthermore, scAgeClock is publicly available to facilitate future research and potential implementations.

Aging is a complex, multifactorial process, influencing disease risk and overall health. While chronological age (CA) is widely used in clinical practice, it fails to capture individual aging trajectories. Current approaches to estimate biological age (BA) often focus on single organs or predefined clinical biomarkers, limiting comprehensive assessment. We introduce a novel, purely imaging-driven deep learning framework for organ-specific BA estimation across seven organ systems. Our uncertainty-aware ResNet-based models autonomously learned aging-related features from imaging data in 70,000 UK Biobank participants, eliminating manual feature selection biases. Training on a healthy cohort, where CA approximates BA, allows learning normative aging patterns. When applied to a broader cohort, deviations from typical aging indicate older or younger BA. Our findings demonstrate the feasibility of BA estimation, even in organs with subtle aging features. While aging is largely heterogeneous across organs, we also identified correlations in aging patterns. We further showed that accelerated aging is prognostic of mortality and health outcomes, offering insights for personalized assessments.

Traumatic brain injury (TBI) disproportionately affects the elderly, yet the underlying mechanisms remain unclear. Here, we demonstrate that aged TBI brains predominantly harbor pro-inflammatory NLRP3+ microglia, in stark contrast to the neuroprotective Lysozyme+ microglia prevalent in young TBI brains. This age-dependent microglial dichotomy correlates with elevated mortality and impaired recovery in aged TBI mice. By leveraging an integrative multi-omics approach combined with metabolomics and epigenome analysis, we identify a previously unrecognized link between enhanced glycolysis and pro-inflammatory chromatin landscape in NLRP3+ microglia. Further investigation identifies ELF1 as a key transcription factor driving NLRP3+ microglia formation. Importantly, ablation of ELF1 reverses age-associated microglial dysfunction and improves TBI outcomes. Finally, we discover that Imeglimin, a clinically approved antihyperglycemic agent capable of crossing the blood brain barrier, inhibits ELF1 and reverses microglial phenotype, reducing acute mortality rate and leading to improved functional recovery of aged TBI mice. Our work elucidates the mechanistic basis of age-dependent TBI outcomes, reveals the crosstalk between metabolic rewiring and epigenetic regulation in microglial aging, and identifies ELF1 as a promising therapeutic target for improving TBI outcome.

Despite decades of biochemical study, a comprehensive map of the mammalian metabolome remains elusive. Mass spectrometry-based metabolomics detects thousands of small molecule-associated signals in mammalian tissues, but it is currently unclear how many of these reflect products of endogenous metabolism. Here, we leverage systematic in vivo isotope tracing to infer the biosynthetic origins of unidentified metabolites. We administered 26 different isotopically labelled nutrients to mice, measured circulating and tissue metabolite labelling by mass spectrometry, and developed a statistical framework to infer the number of carbon atoms incorporated from each of these precursors into more than 4,000 putative metabolites. We show this information can be harnessed for biosynthesis-aware structure elucidation using a multimodal AI model that co-embeds isotopic labelling patterns with chemical structures. This approach revealed several previously unrecognized families of mammalian metabolites, including cysteine-derived alkylthiazolidines, dithioacetal mercapturic acid derivatives, short-chain N-acyltaurines, acylglycyltaurines, and N-oxidized taurines. It further uncovered a family of mevalonate-derived isoprenoid metabolites that includes 2,3-dihydrofarnesoic acid, which is markedly depleted in both mouse and human aging. Age-related depletion of these isoprenoids is driven by impaired coenzyme A synthesis. Our work establishes the biosynthetic precursors for thousands of unidentified metabolites and reveals multiple previously unrecognized branches of mammalian metabolism.

Foundational AI models have recently shown promise for predicting the impact of perturbations on cell states. However, current models typically consider only one cell state at a time, limiting their ability to learn how cellular responses unfold over time, particularly across long trajectories such as diseases of aging. Here, we develop a temporal AI model, MaxToki, trained on nearly 1 trillion gene tokens including cell state trajectories across the human lifespan to generate cell states across long timelapses of human aging. MaxToki generalized to unseen trajectories through in-context learning and predicted novel age-modulating targets that were experimentally verified to influence age-related gene programs and functional decline in vivo. MaxToki represents a promising strategy for temporal modeling to accelerate the discovery of interventions for programming therapeutic cellular trajectories.

Long noncoding RNAs (lncRNAs) regulate transcriptional and epigenetic programs during aging and senescence. However, no comprehensive studies have systematically integrated multilayered analyses to reveal their diverse regulatory roles. Moreover, lncRNAs with therapeutic potential in age-related diseases remain unexplored. Here we systematically perturbed 32 high-abundance aging- and senescence-associated lncRNAs (PtbAlncs) using a Perturb-seq-based CRISPR-dCas9-KRAB knockdown system coupled with single-nucleus multiomics profiling, enabling simultaneous transcriptomic and chromatin accessibility analysis. This analysis uncovered essential roles for previously uncharacterized lncRNAs in senescence regulation, validated computationally and experimentally. These lncRNAs modulate distinct single-cell RNA-sequencing modules through diverse yet overlapping epigenetic motifs in single-cell ATAC-sequencing modules. Among them, HOTAIRM1, a DNA repair-associated PtbAlnc, stabilizes DNA repair by cooperating with BANF1 and p53 at double-strand break loci within condensates. Its deficiency impairs DNA repair and triggers p53-mediated senescence. In aged mouse lungs, adeno-associated virus-mediated HOTAIRM1 overexpression reduced fibrosis, alleviated tissue damage, and promoted cellular proliferation, underscoring its therapeutic potential.

Brain aging is accompanied by profound cellular and microstructural changes that precede overt tissue loss, yet in vivo MRI studies largely emphasize macroscopic measures or isolated diffusion and relaxation metrics, providing limited insight into how cellular-scale tissue architecture is altered across the adult lifespan. Here, we apply multidimensional diffusion-relaxation MRI (MD-MRI) to map voxel-wise microstructural phenotypes in cognitively unimpaired adults spanning early adulthood to late life (23-77 years). Rather than relying on predefined compartment models, MD-MRI resolves continuous voxel-wise distributions in a joint diffusion-relaxation space, enabling an integrated, model-free description of how cellular shape, size, restriction, and chemical environment vary with age. Using this approach, we reveal age-related multilateral shifts within a complex microstructural landscape, marked by increasing heterogeneity and disorder across brain tissue. With age, cellular-scale features showed a systematic transition from small to larger length-scale structures accompanied by reduced microscopic restriction, indicating a loss of fine cellular barriers and expansion of extracellular space. In parallel, we show tissue-dependent alterations in fast-relaxing properties aligned with known gray- and white-matter aging processes, including iron accumulation and myelin loss. Together, these findings indicate that normative brain aging involves progressive reorganization of structure and composition at the cellular level, rather than uniform shifts in bulk tissue properties. By decoupling cellular-scale shape, size, restriction, and chemical environment in vivo, MD-MRI identifies increasing cellular heterogeneity and breakdown of microscopic restriction as central features of human brain aging and provides a biologically interpretable framework for linking microstructural reorganization to age-related functional decline.

Loss of skeletal muscle mass and strength are common manifestations of frailty in older people and are linked to reduced quality of life. However, whether mitochondria are mechanistically linked to frailty and how physical activity, or lack thereof, is involved in age-related functional decline are still unknown. We report that exercise-induced improvements in functional capacity, including reduced frailty in old mice, are dependent on mitochondrial adaptations in skeletal muscle at structural, enzymatic, and functional levels. Our preclinical study included a healthy aging mouse line, a transgenic model of robustness, and a muscle-specific mitochondrial-deficient mutant mice, allowing us to assess both mitochondrial plasticity with aging and the necessity of intact mitochondrial function for exercise-induced adaptations. These findings were corroborated by a cross-sectional human study examining the relationship between skeletal muscle mitochondrial function, age, and physical capacity. We analyzed biopsies from 30 donors (men and women, aged 17 to 99 y) stratified into young and older adults with varying functional statuses. Our results indicate that mitochondrial dysfunction in skeletal muscle is associated with the decline in locomotor muscle function in the elderly, highlighting the potential role of exercise or habitual physical activity in mitigating this phenotype. Notably, we demonstrate that skeletal muscle mitochondria maintain plasticity during aging in mice and humans, and that this preserved adaptability can be leveraged to improve muscle performance and overall functional capacity.