From Developmental Biology, 6th edition (Gilbert, 2000):
Aging can be defined as the time-related deterioration of the physiological functions necessary for survival and fertility.
From WHO (https://www.who.int/news-room/fact-sheets/detail/ageing-and-health):
At the biological level, aging results from the impact of the accumulation of a wide variety of molecular and cellular damage over time. This leads to a gradual decrease in physical and mental capacity, a growing risk of disease and ultimately death.
From Wikipedia (https://en.wikipedia.org/wiki/Ageing):
Aging is the process of becoming older. In humans, aging represents the accumulation of changes in a human being over time and can encompass physical, psychological, and social changes. Aging increases the risk of human diseases such as cancer, Alzheimer’s disease, diabetes, cardiovascular disease, stroke and many more.
Modern biological theories of human aging fall into two main categories: programmed aging and damage (or error) theories.
Damage Theory (Kirkwood, 1977; Medawar, 1952; Whittemore et al., 2019): This concept suggests that aging results from the accumulation of damage, such as DNA oxidation, which leads to the failure of biological systems over time. Damage-related factors include both internal and environmental stressors that cause cumulative harm at various levels of an organism’s physiology.
Programmed Aging Theory (Bowles, 1998; Guarente & Kenyon, 2000; Skulachev, 1997): This theory proposes that aging is driven by intrinsic biological processes, such as epigenetic regulation (e.g., DNA methylation). Programmed aging follows a biological timetable, potentially an extension of the mechanisms that regulate childhood growth and development. These processes influence gene expression, affecting maintenance, repair, and defense systems, ultimately contributing to aging.
Cellular senescence is a process in which cells cease dividing and undergo distinct phenotypic changes, including significant chromatin remodeling, alterations in their secretome, and activation of tumor-suppressor mechanisms (Huang et al., 2022).
The term senescence was first introduced by Hayflick and Moorhead (Hayflick & Moorhead, 1961) to describe the irreversible growth arrest observed in human diploid cell strains after extensive serial passaging in culture. This specific type of senescence, known as replicative senescence, was later linked to telomere attrition—a process that leads to chromosomal instability and promotes tumorigenesis. This discovery supported the original hypothesis that senescence serves as a protective mechanism against the uncontrolled proliferation of damaged cells.
Subsequent research has further established cellular senescence as a crucial safeguard against cancer (Saretzki, 2010). Emerging evidence suggests that its physiological role extends beyond tumor suppression, contributing to key biological processes such as embryonic development, wound healing, tissue repair, and organismal aging (Muñoz-Espín et al., 2013).
Senescent cells progressively accumulate in the tissues and organs of humans, primates, and rodents with age (Ogrodnik et al., 2019).
One possible explanation is that the rate of senescent cell production increases over time. Supporting this idea, several studies have shown that various stimuli capable of inducing senescence also rise with aging. If cellular stressors collectively drive senescence, their accumulation would take a prolonged period.
Alternatively, the efficiency of senescent cell clearance may decline with age. This process is likely impacted in aging humans and rodents, as the immune system undergoes a series of complex changes in both innate and adaptive immunity, ultimately leading to age-associated immunodeficiency. A key aspect of this decline may be a reduced ability to eliminate senescent cells. Indeed, age-related dysfunction of hematopoietic stem cells compromises immune function, which could contribute to the systemic accumulation of senescent cells in later life (Song et al., 2020).
Moreover, emerging evidence suggests that senescence is not a static state but rather a dynamic process, further influencing its role in aging and disease progression (Young et al., 2012).
One scenario is that cellular senescence contributes to the overall decline in tissue regenerative potential that occurs with aging. This idea is supported by the observation that progenitor cell populations in both skeletal muscle and fat tissue of BubR1 progeroid mice are highly prone to cellular senescence (Baker et al., 2013).
In addition to acting on stem cells in a cell-autonomous fashion by establishing a persistent growth arrest, senescence could act to disrupt the local stem-cell niche non-autonomously through the Senescence-Associated Secretory Phenotype (SASP) (Yanagi et al., 2017).
Other SASP-based mechanisms may also contribute to tissue dysfunction. For example, proteases chronically secreted by senescent cells may perturb tissue structure and organization by cleaving membrane-bound receptors, signaling ligands, extracellular matrix proteins or other components in the tissue microenvironment (Frankowska et al., 2022).
In addition, other SASP components, including IL-6 and IL-8, may stimulate tissue fibrosis in certain epithelial tissues by inducing EMT (Laberge et al., 2012).
Chronic tissue inflammation, which is characterized by infiltration of macrophages and lymphocytes, fibrosis and cell death, is associated with aging and has a causal role in the development of various age-related diseases. One idea, which remains untested, is that senescent cells that accumulate with aging and that are present at sites of age-related pathologies promote this type of inflammation through the proinflammatory growth factors, cytokines and chemokines they secrete. These may include GM-CSF, GROα, IL-1, IL-6, IL-8, macrophage inflammatory proteins (MIPs), as well as monocyte chemo-attractant proteins (MCPs). Together with matrix metalloproteinases, proinflammatory SASP components are thought to create a tissue microenvironment that promotes survival, proliferation and dissemination of neoplastic cells, which may explain, at least in part, why cancer rates markedly increase beyond middle age (Takasugi et al., 2023).
Finally, the SASP may intensify age-related tissue deterioration through paracrine senescence, a recently discovered mechanism by which senescent cells spread the senescence phenotype to healthy neighboring cells through secretion of IL-1β, TGFβ and certain chemokine ligands (Van Deursen, 2014).
However, senescence can be a highly dynamic, multi-step process, during which the properties of senescent cells continuously evolve and diversify (De Cecco et al., 2013; Ivanov et al., 2013; Van Deursen, 2014; Wang et al., 2011).
The initiating step is the transition of temporal to stable cell-cycle arrest, which typically involves prolonged inhibition of Cdk–cyclin activity by p21, p16Ink4a, or both. A change in p53 expression from intermittent to continuous may be a critical event in the transition from temporal to persistent growth arrest (Purvis et al., 2012).
For the progression to full senescence, it seems that lamin B1 downregulation triggers both global and local modifications in chromatin methylation (Freund et al., 2012). Some mammalian cell types form regions of highly condensed chromatin called senescence-associated heterochromatin foci (SAHFs) (Funayama et al., 2006). SAHFs, which are enriched in chromatin modifications such as S83-HP1γ, HIRA, ASF1, macroH2A, H3K9me3 and γH2AX, sequester genes implicated in cell-cycle control, a phenomenon that seems to reinforce the senescence-associated growth arrest. Among the assortment of upregulated genes is a prominent subset of genes that encode secreted proteins, including cytokines and chemokines with proinflammatory properties, as well as various growth factors and proteases that together alter tissue structure and function. Collectively, these factors are referred to as the senescence-associated secretory phenotype (SASP) (Coppé et al., 2008).
Senescent cells continue to evolve even after extended periods of culture, thereby progressing to a stage that has been termed ‘deep’ or ‘late’ senescence. This phenomenon is evidenced by a dramatic increase in the transcription of transposable elements, including members of the L1, ALU and SVA transposon families, which occur several months after senescence onset (De Cecco et al., 2013; Wang et al., 2011). These newly synthesized retrotransposon transcripts can indeed engage in active transposition and accumulate in late-senescent cell genomes. Increased retrotransposon activity is associated with senescence-associated opening of gene-poor heterochromatic regions where these elements reside. A second process driving continued change in senescent cells is characterized by the extrusion of chromatin into the cytoplasm, resulting in the formation of cytoplasmic chromatin fragments (CCFs) (Ivanov et al., 2013).
The human longing for eternal youth is universal and timeless. In ancient times, people sought it through religion. During the Middle Ages, alchemists—part mystics, part proto-chemists—attempted to create a mysterious elixir of eternal youth, a potion believed to grant indefinite vitality to those who dared to drink it.
But the question is: Is rejuvenation possible? The answer is YES.
In addition to the two main aging theories mentioned above, at Rejuve Therapeutics we posit that aging arises from both the progressive accumulation of cellular damage and the inactivation of an intrinsic rejuvenation program present in every cell of the body. This rejuvenation program is robustly activated only during the reproductive process. In humans, reproduction represents a form of rejuvenation at the species level. During fertilization, the fusion of sperm and egg effectively resets the biological clock (Rando & Chang, 2012). In this process, genetic information is reorganized and reprogrammed, leading to the erasure of cumulative cellular damage and age-associated epigenetic marks inherited from the parents (J.-H. Yang et al., 2023). As a result, offspring are biologically younger and largely free of the age-related cellular damage accumulated by the parents, thereby ensuring the continuity of the species through a renewed and relatively undamaged generation.
A fundamental question is why this rejuvenation program is activated only during reproduction, but not in somatic cells as individuals age. One of the most plausible explanations is that the primary goal of evolution is to maximize species continuity, rather than individual longevity, under conditions of limited resources.
What if the rejuvenation program that exists within cells could be identified and applied to somatic cells? Such an approach could prevent or treat most age-related diseases and may ultimately extend both human lifespan and healthspan.
Today, science may have uncovered the real biological fountain of rejuvenation, the cytoplasm of the oocyte. The journey toward biological rejuvenation began in the early 1960s when John Gurdon and his collaborators discovered animal cloning in frogs (Gurdon, 1962). Three decades later, in 1996, mammalian cloning was achieved with the birth of Dolly the sheep (Campbell et al., 1996), followed by the cloning of other mammalian species. These breakthroughs demonstrated that the cytoplasm of a mature oocyte contains molecules capable of reprogramming a somatic nucleus into an embryonic state, enabling the development of a new organism. Scientists hypothesized that the oocyte’s cytoplasm must contain a complex constellation of reprogramming factors essential for resetting cellular age. But what are those factors in the oocyte’s cytoplasm that caused the reprogramming?
To identify the molecular drivers of rejuvenation, we must:
Humans have approximately 20,000 protein-coding genes, but the total number of genes, including non-coding RNA genes, is much higher, with some estimates exceeding 46,000 (Amaral et al., 2023). Even when restricted to protein-coding genes, the number of possible combinations involving two to six genes out of approximately 20,000 amounts to 8.88 × 10²². Such an astronomical number far exceeds the capacity of current experimental or computational approaches to be tested exhaustively. What makes this task more complicated is that some genes in a combination need to be over expressed, and some need to be inhibited, and sometime need to be spatially and timely regulated.
Thanks to the discovery of iPSC (induced pluripotent stem cell) technologies in 2006 by Yamanaka (Takahashi & Yamanaka, 2006), who, because of it, got Nobel Prize in 2012, scientists know that only 4 genes (Oct4, Klf4, Sox, and Myc) (OKSM), so called Yamanaka factors, can reprogram somatic cells into iPSC. Though iPSC reprogramming successfully rejuvenated cells, it also caused dedifferentiation of the cells, thus the cells lost their original phenotype. As a result, iPSC technologies cannot be used directly in the body for rejuvenation purposes, because it could cause teratoma or other type of tumors in the body (Liu, 2008). However, there are still a lot of achievements on rejuvenation applications based on iPSC technology:
Partial reprogramming:
An important approach is partial reprogramming. 2-5 days partial reprogramming is a method of using Yamanaka factors (or alternative reprogramming factors, in the wider context) to revert aged cells to a younger state without completing the reprogramming cycle, thus retaining their cellular identity. However, partial reprogramming still faces the challenge of partial dedifferentiation of the somatic cells (though it was claimed the dedifferentiation is reversable) and limited rejuvenation capability (Browder et al., 2022; Ocampo et al., 2016; Olova et al., 2019).
Clearance of senescent cells:
Using senolytic, among a class of small molecules that can selectively induce death of senescent cells and improve health in humans. Studies in BubR1 progeroid mice provided proof-of-principle that clearance of senescent cells can delay age-related degenerative pathologies. However, the long-term effects of senolytics are still unknown. In addition, there is a risk of off-target effects, as senolytics may also kill healthy cells (Czajkowski et al., 2025).
Overexpression of the pluripotency factor NANOG:
Overexpression of the pluripotency factor NANOG in progeroid or senescent myogenic progenitors reversed cellular aging and fully restored their ability to generate contractile force. The effect was mediated by the reactivation of the ROCK and TGF-β pathways (Mistriotis et al., 2017).
3 factors for vivo rejuvenation
Using the eye as a model CNS tissue, that ectopic expression of Oct4, Sox2, and Klf4 (OSK) in mouse retinal ganglion cells restores youthful DNA methylation patterns and transcriptomes, promotes axon regeneration after injury, and reverses vision loss in a mouse model of glaucoma and in aged mice (Lu et al., 2020).
As mentioned above, though iPSC reprogramming successfully rejuvenated cells, it also caused pluripotency of the cells. As a result, iPSC technologies cannot be used directly in the body for rejuvenation purposes. However, iPSC technologies provided a great starting point that we could use to identify the rejuvenation gene cocktails.
iPSC reprogramming involves both dedifferentiation and rejuvenation, meaning the two processes are coupled during reprogramming. We believe, however, that they can be separated. In humans, a fertilized zygote gives rise to a fully developed neonate comprising nearly all differentiated somatic cell types within approximately 40 weeks, underscoring that cellular differentiation is completed over a remarkably short developmental timescale, whereas the biological processes of aging have scarcely been initiated. This fundamental observation suggests that aging and differentiation are governed by largely independent biological programs. It therefore raises the question of why the ostensibly opposing processes of aging and differentiation—namely rejuvenation and dedifferentiation—are coupled during the reprogramming process.
We hypothesize that this coupling during the reprogramming process arises because the canonical reprogramming factors—namely the four originally identified by Yamanaka, as well as additional factors reported subsequently (Yu et al., 2007)— incidentally co-regulate gene networks involved in both processes. In other words, there may exist alternative gene combinations that specifically regulate either rejuvenation or dedifferentiation. iPSC reprogramming is orchestrated by a highly interconnected gene regulatory network (Van Den Hurk et al., 2016), which we propose comprises at least two partially overlapping sub-networks: one governing dedifferentiation and the other governing rejuvenation. The Yamanaka factors likely occupy a central position at the apex of this regulatory hierarchy, exerting control over both modules. By systematically analyzing their downstream targets and network topology during reprogramming, we aim to delineate the distinct genetic circuits responsible for each process. Such insights would enable targeted genetic and epigenetic interventions designed to selectively activate the rejuvenation program, thereby achieving cellular rejuvenation without loss of cellular identity. This hypothesis forms the conceptual foundation of our approach.
Aging is believed to commence as early as embryonic development (Gallardo et al., 2025), reflecting intrinsic biological programs that accompany cellular differentiation and tissue formation. Nevertheless, the pathophysiological manifestations of aging—such as functional decline and age-associated diseases—predominantly arise during adulthood. Accordingly, our rejuvenation research centers on adult cellular aging (cellular senescence).
Among the various methods for inducing cellular senescence, replicative senescence is the closest to natural cellular aging, as it occurs naturally in cells that divide over time, particularly in actively proliferating cells. This process closely mimics the gradual decline in cellular function observed during organismal aging, making it a suitable model for studying cellular aging (Ahn et al., 2025). Here we employ adult human arterial endothelial cells (HAECs) as an experimental model. There are two major reasons that we chose HAEC. One is HAECs from the same location in an artery are relatively homogeneous compared to other types of human primary cells. They have unique shapes in cell culture as well as unique surface markers, making it easier to identify and quantify. In addition, because of the relative homogeneousness, it would be easier to identify the minor shift on phenotype changes as well as genes expression profile changes. Another reason we chose HAECs is that endothelial cells are known to be one of the first cell types to become senescent with advancing age (Bloom et al., 2023; Grosse et al., 2020). In vivo, endothelial senescence occurs in multiple vascular beds including kidney (Cohen et al., 2021), retina (Shosha et al., 2018), liver (Grosse et al., 2020), brain (Kiss et al., 2020) and aorta (Yokoi et al., 2006), suggesting that endothelial senescence contributes to a variety of pathological processes associated with vascular dysfunction. Therefore, rejuvenating endothelial cells holds significant therapeutic potential for preventing and treating many major diseases associated with vascular dysfunction.
We have built a propriety rejuvenation screening platform using HAECs (US patent pending, Application# 63/960,217) for the high throughput screening of rejuvenation gene cocktails. In this screening model, we use replicative aging human artery endothelial cells (HAEC) (non-growing) as starting cells and use their growth capability as the first parameter of rejuvenation, followed by identity verification and other parameters, as well as RNA sequencing analysis.
Publicly available databases provide extensive datasets capturing differential gene expression across tissues and cell types, as well as dynamic gene expression profiles during embryonic stem cell (ESC) and induced pluripotent stem cell (iPSC) differentiation. The primary challenge lies in applying appropriate analytical strategies and computational tools to extract candidate genes with functional relevance to differentiation.
Leveraging advanced bioinformatics and data science methodologies, we systematically analyzed these datasets to identify genes exhibiting distinct expression patterns between young somatic cells and early embryonic or pluripotent cells. Specifically, we focused on two major categories of genes:
Because the reprogramming process intrinsically encompasses both dedifferentiation and rejuvenation, we hypothesize that genes differentially regulated during this process are involved in differentiation pathways, aging-associated processes, or both. Through this analysis, we identified 982 genes within this category.
Notably, substantial overlap exists among genes identified across differentiation-related, reprogramming-associated, and aging-related datasets. This overlap is biologically plausible, as senescent cells are typically terminally differentiated.
Following careful evaluation and integrative analysis, we selected approximately 200 genes that are strongly associated with differentiation while exhibiting minimal association with aging. These genes were subsequently subjected to further downstream screening and analysis.
3. Apply those genes identified in the above procedure 2 to the above rejuvenation screening system while doing reprogramming. For example, we knock down those genes with high expression in early embryo or embryonic stem cells and low expression in adult cells in the senescent HAECs while we treat the senescent HAEC with reprogramming factors. Or the opposite, we overexpress genes that have high expression in adult cells and low expression in early embryo and embryonic stem cells in senescent HAEC while we treat the senescent HAEC with reprogramming factors.
4. Identify rejuvenated cells that keep original cell identity, so we could identify the genes that are related to dedifferentiation (either promote or inhibit) while not affecting the rejuvenation process during reprogramming process from our rejuvenation platform.
We have identified a set of genes that after perturbation, specifically suppress dedifferentiation during the reprogramming of senescent HAECs. By selectively inhibiting the dedifferentiation pathway, we achieved cellular rejuvenation while maintaining the original endothelial phenotype (U.S. Patent Application No. 63/960,217). Although there are reports that suggested the possibility of rejuvenating senescent cells without inducing dedifferentiation (de Lima Camillo et al., 2025), the identities of the implicated genes or gene cocktails were not disclosed, precluding independent evaluation. Our findings therefore provide, to our knowledge, the first experimentally verified and molecularly defined demonstration of gene-mediated rejuvenation that preserves native cell identity.
Figure 1: Characterization of young (Y_HAEC) and senescent (O_HAEC) HAECs.
As expected, Y_HAECs are small and exhibit typical endothelial morphology under a light microscope. They are β-Gal negative, indicating that they are not senescent, as confirmed by their continuous growth capability. Additionally, they are CD31 and VWF positive, confirming their endothelial identity.
In contrast, O_HAECs appear larger while retaining endothelial morphology under a light microscope. However, they are β-Gal positive, signifying cellular senescence, as further evidenced by their lack of proliferative capacity. Despite senescence, these cells remain CD31 and VWF positive, confirming that they still maintain their endothelial identity.
Figure 2. Rejuvenation of Senescent HAECs Using Our Gene Cocktail.
Following treatment with our gene cocktail, the treated senescent HAECs (T2_HAEC) exhibit morphology similar to young endothelial cells under a light microscope. They are β-Gal negative, indicating the loss of senescence, and their restored growth capability confirms their rejuvenated state.
Additionally, the treated cells remain CD31 and VWF positive, demonstrating that they have retained their endothelial identity. These findings confirm that our gene combination successfully rejuvenates senescent HAECs while preserving their original cell phenotype.
Figure 3. RNA sequencing (RNA-Seq) heatmap of the Old (O_HAEC), Treated (T2_HAEC), and Young (Y_HAEC). The heatmap displays gene expression (z-score normalized) from blue (low) to red (high) across ~110 strongly regulated genes.
Old cells (O_HAEC) show high expression of: Fibrosis / ECM remodeling genes such as VCAN, FAP, COL, ECM-related genes (SULF1, CHPF), TAGLN, MYL9 (smooth muscle / contractile markers), SERPINE2, ANGPTL4, PALM, IGFBP3, NID2, ITGA11; SASP / Inflammatory genes, such as CXCL8, RELN, MMP10, SELE; Pro-aging regulators such as HDAC9 (known aging marker) and PPP1R3C and PALLD
Young HAECs (Y_HAEC) show high expression of: Cell Cycle / Proliferation / Mitosis genes, such as CDK1, CDC20, KIF20A, DLGAP5, HMMR, ASPM, MYBL2, TK1, HIF3A, NUP210, PIMREG, RGS7BP; Histone and chromatin genes (replicative youth signature) such as H2BC, H3C, H4C, H2AC, multiple variants, CENP-A related histones.
Treatment Signature (T2_HAEC) shows that treatment reduces Old-like genes: treatment cells show blue (downregulation) for many Old-high genes: SASP genes drop (CXCL8, MMP10, SELE); ECM/fibrosis markers drop (VCAN, FAP, TAGLN); Aging regulators drop (HDAC9, IGFBP3, SERPINE2). This is direct partial reversal of aging.
Treatment partially restores Young-like genes: in the lower half of the heatmap, many Young-high cell-cycle and chromatin genes that are suppressed in Old become yellow-red (re-activated) in Treatment: CDK1, KIF20A, DLGAP5, CDC20, Histones (H2BC, H3C, H4C), ASPM, MYBL2, H3C10, H1-5, etc.
Figure 4. The expression of endothelia markers. Young (Y_HAEC), Old (O_HAEC), and Rejuvenated (T_HAEC). Expression levels of eight endothelial marker and activation-related genes are shown across Young (Y_HAEC), Old (O_HAEC), and Rejuvenated (T_HAEC) human aortic endothelial cells. Panel A displays FPKM expression values; Panel B shows raw RNA-seq counts; Panels C and D present the same data on a log10 scale for clearer visualization across expression ranges. Across all metrics, canonical endothelial identity genes — PECAM1, CD34, CDH5, ANGPT2, VWF, SELP, ICAM1, and VCAM1 — remain similar robust expression among all three groups, including rejuvenated cells, indicating preservation of endothelial lineage.
The endothelial marker gene expression analysis demonstrates that young, senescent, and rejuvenated HAECs all maintain the core endothelial gene expression program, confirming that the rejuvenation intervention does not cause dedifferentiation or loss of vascular identity.
Figure 5. Doubling Times (n) of Rejuvenated HAECs Before Growth Cessation
The rejuvenated HAECs treated with our gene combination were continuously cultured, and their doubling times (d) were calculated. The rejuvenated endothelial cells exhibited doubling times of approximately 22–24 before reaching senescence again.
The initial rejuvenation screening was conducted on senescent HAECs (O_HAEC, also pX+d12.97), where the senescent cells had an unknown passage number (pX) with an additional 12.97 doublings (d12.97). After rejuvenation, the cells regained proliferative capability, completing an additional 22–24 doublings before ceasing growth once more.
These results demonstrate that rejuvenated cells retain a limited proliferative capacity, confirming that they are non-tumorigenic and do not exhibit uncontrolled growth.
The gene cocktails identified in Milestone 1—combining reprogramming with inhibition of dedifferentiation—are not the final formulations for rejuvenation. These cocktails involve numerous genes, particularly those associated with reprogramming, which are unlikely to be directly used in humans. Nevertheless, Milestone 1 established a critical foundation and provided essential tools for our next step studies. Building on this foundation, our current and ongoing efforts under Milestone 2 focus on three major goals:
Critically, all datasets are derived from the same primary human cell type, adult HAECs. This single-cell-type design eliminates cross-lineage variability and maximizes our ability to detect subtle, temporally resolved regulatory changes that would be obscured in heterogeneous datasets.
Moreover, all datasets include high-quality supervised labels, such as treatment conditions, cell-cycle and doubling-time metadata, and defined cellular states. These rich annotations substantially increase the value of the atlas for downstream machine-learning applications, particularly for supervised and semi-supervised model fine-tuning.
This integrated atlas will represent the first complete, time-resolved Longitudinal Human Aging and Rejuvenation Gene Expression Atlas. It will serve as a unique, comprehensive resource that captures the regulatory dynamics underlying human cellular aging and rejuvenation. The atlas will also form the foundation for the next phase of our program: AI-powered computational discovery and gene-cocktail optimization.
2. Development of a biological clock based on gene expression signatures
By analyzing the Longitudinal Human Aging/Rejuvenation Gene Expression Atlas, using AI technology, we will identify gene expression signatures corresponding to different days of continuous HAEC culture on its way to replicative senescence, which represents the signatures of different stages of cellular senescence. These reference signatures will then be compared with gene expression signatures from human samples of different ages to estimate the proportion of cells at various senescence stages within human tissues. Since senescent cells accumulate in tissues as humans age, algorithms built on large-scale comparisons of these datasets are expected to generate a highly precise biological aging clock for humans.
3. AI-Driven Analysis of the Longitudinal Aging/Rejuvenation Atlas and Identification of Core Rejuvenation Regulators
We will apply state-of-the-art foundation models, deep learning algorithms to systematically interrogate the Longitudinal Human Aging/Rejuvenation Gene Expression Atlas.
Our strategy includes transfer learning on leading large-scale single-cell foundation models—such as Geneformer (Theodoris et al., 2023), scGPT (Cui et al., 2024), scFoundation (Hao et al., 2023), CellFM (Zeng et al., 2025), or scBERT (F. Yang et al., 2022)—to specialize them for aging- and rejuvenation-related biological processes. Fine-tuning these models on our highly curated, longitudinally structured datasets will enable them to achieve superior accuracy, interpretability, and biological fidelity for downstream analyses relevant to aging biology.
These optimized models will then support in silico gene perturbation, enabling us to map causal gene–gene interactions, predict rejuvenation trajectories, and computationally screen gene combinations with unprecedented efficiency and depth.
Using this AI-driven framework, we will achieve the following:
Upon successful preclinical validation, the optimized rejuvenation gene cocktails and their corresponding small-molecule mimetics will advance to early-stage clinical evaluation. The primary objectives will be to assess their safety and therapeutic efficacy in treating age-related diseases and, ultimately, to develop interventions capable of systemic rejuvenation and extension of human healthspan and lifespan.
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Dr. Gordon Ma is a geneticist, stem cell biologist, and biotech founder with over 20 years of experience in epigenetics, gene regulation, and rejuvenation biology. His PhD work produced the first evidence of CDH1 promoter methylation in precancerous tissue—a foundational contribution to modern epigenetics. He completed postdoctoral training at Harvard Medical School and the University of Colorado, where he studied XIST-mediated epigenetic regulation.
At the NIH (NHLBI), Dr. Ma conducted research in embryonic stem cell differentiation and cardiovascular regenerative biology and made key findings on mitochondrial regulation of pluripotency. He later founded several life science companies while continuing NIH research as a special volunteer for nearly a decade.
As Founder and CEO of Rejuve Therapeutics, Dr. Ma is the principal inventor of a patented method demonstrating gene-driven rejuvenation without dedifferentiation. His ongoing work includes large-scale single-cell sequencing studies and the development of the first Longitudinal Human Aging and Rejuvenation Gene Expression Atlas. He is collaborating closely with AI scientists on transfer learning and biological foundation models to identify key regulatory genes driving aging and rejuvenation and to develop clinically viable rejuvenation gene cocktails