A white paper for rejuvenation therapies and blueprint for clock.bio

Mark Kotter
49 min readAug 30, 2023

“You can’t turn back the clock, kid! But you can wind it up again.” Cars 3, 2017

by Koby Baranes and Mark Kotter

1. clock.bio’s mission is to extend health span by 20 years based on biomarkers of ageing in a Phase 3 trial by the end of this decade.

Age is the common risk factor for a diverse set of diseases that become the cause of our death. clock.bio seeks to increase health span by developing pharmacological modulators for biological age.

Figure 1. Cause of death at every age from 0 to 65 years. clock.bio aims to reduce the causes of death due to disease and increase health span by 20 years.
Underlying data sourced from CDC Underlying Cause of Death database 2005–2014.

2. Background summary of ageing and rejuvenation biology

“Every heart this May morning in joyance is beating,

Full merrily;

Yet all things must die.” Alfred Lord Tennyson

Ageing is considered an invariable reality of life and the root cause of mortality. For millennia humanity has dreamed of escaping its effects. Apart from maintaining a youthful state, there are good reasons for wanting to control the ageing process: ageing is the common risk factor for some of the deadliest diseases, including cancer, heart attack, and stroke. It underpins degenerative conditions such as Alzheimer’s, arthritis, and diabetes, which severely affect our quality of life. Age may in fact be considered the underlying disease and cause of these conditions.

Increasing evidence indicates that the ageing process is amenable to change. Over the past centuries, human lifespan has significantly lengthened. Factors associated with poverty are known to shorten human life whilst higher wealth and education extend it. Recent studies have demonstrated that the lifespan of an organism can be increased, e.g. by caloric restriction or via pharmacological approaches that alter cell metabolism. Furthermore, long life is not limited to lower species: amongst the most long-lived organisms are Greenland sharks, which are estimated to live between 300–500 years. This lends hope to our deep-rooted desire to control ageing.

The ability to reverse age, to rejuvenate an organism, was long thought to be an entirely fictitious dream. However, recent cell reprogramming experiments indicate that ageing might indeed be reversible.

Ageing may therefore not be an inescapable consequence of life. A better understanding of the ageing process will open new opportunities for maintaining health; provide unique insight in the pathophysiology of age-related diseases and may provide a means for preventing them.

2.1 The wear and tear theory of ageing

Several competing theories have been proposed to explain the ageing process. The “Wear and tear” theory proposes that ageing is the consequence of accumulative damage at a cellular (and extracellular) level. Many observations can be explained by this theory, such as the accumulation of damage within cells, including double strand breaks in the nuclear DNA and the increase of mitochondrial mutations 1–3. Increasing mitochondrial dysfunction, in turn leads to increased ROS production 4, which can cause further damage to DNA, lipids, and proteins 5–7. The accumulating damage broadly affects cellular function, generally slowing biological processes, and results in cell death. This in turn triggers a chronic inflammatory state in which a dysregulated immune system further adds to the damage 8. Finally, age-related changes in tissue-resident stem cells negatively affect their ability to repair, and thus further contribute to the decline in tissue homeostasis. Consequently, wear and tear wins over repair and accelerates with age.

The wear and tear theory of ageing predicts that ageing can be modulated (to a certain extent) by slowing or accelerating the damage leading to ageing. In keeping with this theory, longer life span is associated with a beneficial environment, such as present in the context of higher economic, educational, and better nutritional status (e.g. 9). Moreover, modulating mitochondrial activity, e.g. via caloric restriction 10, and the induction of autophagy, a process that is able to clear dysfunctional mitochondria 11, can prolong life.

2.2 Ageing as a cell (enabled) program

A competing but not mutually exclusive theory is that age is the result of a cellular program. Until recently, this was seen as highly unlikely. The suggestion that ageing is an active process has met harsh criticism. An alternative concept is that ageing occurs as a consequence of an ‘enabling’ program, i.e. that a genetic process determines that wear and tear wins over repair and leads to loss of the youthful state associated with the earliest stages of embryonic development.

The cell (enabled) ageing program theory has recently gained prominence following the finding that cells obtained from aged individuals can be reprogrammed back into stem cells and adopt a healthy and immature phenotype e.g. 12. During this process, the hallmarks of age disappear, and cells become virtually indistinguishable from their genuinely juvenile counterparts. Moreover, in vivo studies indicate that age-related processes can also be modulated in living organisms using the same set of reprogramming factors that is commonly used to generate induced pluripotent stem cells 13. This restoration of a youthful state leads to the reactivation of the embryonic repair processes that have been lost during adulthood and to counteract degenerative processes occurring as part of ageing 14. The traditional wear and tear of ageing theory therefore requires modification.

2.3 Embryonic stem cells maintain an eternally youthful state to ensure the survival of the species

Not all cells are subject to ageing in the same way. Germ cells, for example, must minimise ageing or be able to reset the age in the early stages of development to zero after fusion of the gametes when embryonic stem cells are formed 15. If this were not the case, age would be ‘inheritable’, and convey all the known disadvantages to the next generation. Even a small change in the age of the pluripotent stem cells that form the origin of all other cell types in our bodies would over generations lead to a gradual reduction in fitness and ultimately set up the affected species for extinction. Whilst it is tempting to speculate whether such a mechanism could have contributed to the disappearance of prehistoric species, the focus here is on the ageing of individual organisms and not at the species level.

Important lessons can be learnt from the eternal youthful state that is inherent in the fertilised egg, and the reduced ageing of germ cells. Nature seems to be particularly protective of mammalian oocytes that are generated during embryonic development and maintained in a quiescent state until individual oocytes are activated as part of the menstrual cycle. The cells of the germline maintain the highest levels of DNA repair, proteostasis and stress resistance in the body, and correspondingly experience the lowest levels of oxidative damage and mutation rates 16,17. The low energy state of oocytes is likely to minimise oxidative phosphorylation and thereby protect mitochondria and mitochondrial DNA. The bulk of mitochondria during embryogenesis are derived from the maternal lineage.

2.4 Ageing can confer an evolutionary advantage at a species level

Several species exist that do not seem to be affected by age, such as turritopsis dohrnii, a jellyfish that is able to cycle back to early-stage polyps, or hydra (phylum cnidaria), a species that maintains a youthful state via foxo-protein mediated maintenance of stem cells 18 (Figure 2). This begs the question why nature introduced ageing to other species? And what are the advantages of ageing?

Figure 2. Example of a non-ageing species: hydra.

These are unlikely to be found on an individual level. Our collaboration with Dr Thomas Fink, London Institute of Mathematical Sciences, therefore, seeks to explore the advantages of ageing at a species level. We have developed a mathematical model that considers resource availability, mutation rates, and adoptive fitness. Taken together, these axioms alone do not favour a theoretically ageing species as compared to an identical species that does not age. However, when the lightest amount of kinsmen ship or locality is introduced the balance tips and an ageing species wins over a non-ageing 19,20.

In support of the notion that ageing is a program introduced by evolution, a recent paper demonstrated two distinct mechanisms that facilitate ageing and death in yeast 21. One is determined by the nucleus the other one by the mitochondria. Together, these can be modulated to create cellular states that extend average survival of individual yeast cells.

2.5 Two possible locations of where the ageing program is stored: mitochondrial and genomic DNA

Having familiarised ourselves with the concept of a genetically determined ageing program, the next question is where in the genome is this program located? Most eukaryotic cells comprise two genomes that act synergistically, one in the nucleus and one in the mitochondria.

2.5.1 Mitochondrial function affects ageing phenotypes

Mitochondria play a fundamentally important role in the context of ageing 22,23. It has been long established that the activity of mitochondria declines with age, resulting in metabolic changes at the cellular level as well as on the level of the organism. A key question is whether the decline in mitochondrial function is a consequence or a cause of ageing?

Substantial literature exists demonstrating the age-related deterioration of mitochondria. This decline is at least in part driven by the accumulation of mitochondrial DNA mutations leading to a reduction of the fitness of mitochondria in cells 24. Nuclear ageing phenotypes, such as those observed in the rapid ageing phenotype of progeria models that harbour nuclear defects (e.g in DNA repair mechanisms) indicate that the decline in mitochondria function is controlled from outside of the mitochondrial DNA. This points to a causal link in which age-related effects on the nucleus cause mitochondrial decline. This is further supported by the age-reversing effects of partial reprogramming, which can restore mitochondrial function to youthful levels, albeit not in all resulting iPSCs 25.

More recently, studies have investigated the effects of artificially deteriorating mitochondria in cells: mutator mice, which harbour defects in the proofreading domain of the mitochondrial DNA polymerase, suffer from an increasing load of mitochondrial mutations. Therefore, oxidative phosphorylation and other mitochondrial functions decline. The resulting phenotype strongly affects tissues with high cellular turnover and e.g. leads to ageing phenotypes in the skin, including hair loss 26.

Similarly, depletion of mitochondrial DNA in transgenic mice harbouring a non-functional mitochondrial DNA polymerase leads to loss of mitochondrial function and induces hair loss and wrinkling in the skin 27. In this model, the non-functional enzyme was under the control of a doxycycline system, and thus enabled reversal of the experimentally induced impairment of mitochondrial function.

Together, these findings indicate that mitochondrial dysfunction drives phenotypic ageing.

2.5.2 Ageing controlled by a nuclear program

Other findings point to a nuclear ageing programme. For example, telomeres, repetitive nuclear sequences at the end of chromosomes, are known to shorten with age. However, recent studies aiming to correlate telomere length with biological age failed to demonstrate a strong correlation 28. Instead, the main function of telomeres may consist in a “counter function” of cell divisions; once telomeres decrease below a certain size threshold, cells become senescent and stop dividing.

Genomic studies have revealed that ageing is associated with distinct changes in gene expression, including e.g. upregulation of factors associated with chronic inflammation. However, whilst RNA expression patterns can be used to generate biological markers of age within sample sets, they have not been able to reliably predict biologic age across tissues. In contrast, epigenetic changes occurring at the level of the DNA may be highly correlated to age across cell types 29. These form the basis of Horvath’s clock.

2.6 Ageing as the result of a creeping deactivation of cellular repair mechanisms

How is a nuclear program able to control features of ageing that are seemingly independent from transcriptional regulation? Specifically, how can the programmed ageing theory be reconciled with increasing deficiencies, such as increased DNA double strand breaks, accumulation of mitochondrial gene defects, deficits in intracellular transport, and alterations in lipid and protein pathways? These defects were long considered irreversible. Yet they disappear when aged cells are reprogrammed into pluripotent stem cells e.g. 12.

A likely explanation for the remarkable restoration of cellular integrity during iPSC induction is the reactivation of cellular repair processes that are sufficient to reverse the multiple age-related defects. Ageing therefore emerges as a consequence of a slow but ever-increasing deactivation of cellular repair mechanisms, which enables the accumulation of cellular damage observed. As outlined above, there is an absolute physiological necessity for highly efficient repair programs in germ cells and the restoration of zero age in the fertilised egg.

The notion that variation of epigenetic age measures correlates with the health of cells is supported by several findings. For example, accelerated ageing occurs and is associated with a vulnerability to disease is supported by epigenetic clock measures that demonstrated accelerated ageing in tissues that are at high risk of malignant transformation, such as breast tissue 29. Similarly, accelerated epigenetic clock readings have been found in cancer, whereas germ cells, including sperm show reduced ageing 30.

2.7 The information theory of age

The different trajectories of ageing at a cellular level brings about the question whether and how age remains synchronised at the organ level, or even an organismal level. Again, experiments involving Horvath’s clock measurements provide interesting insights. Clinical transplantation of juvenile bone marrow maintains the age gap towards their host and the transplant continues to follow its own ageing trajectory 31. Similarly, parabiosis and plasma transfer experiments suggest that a youthful state may be transferrable 32,33. These findings can be summarised in the ‘information theory of age’.

2.7.1 Extracellular factors that influence ageing on the organ and organismal level

At a local level, synchronising of ageing could theoretically be achieved by cell-to-cell signalling, including via gap junctions, cell contact signalling, or paracrine factors. Intriguing results from Robin Franklin and Kevin Chalute’s groups provide an additional form of local synchronisation based on physical stimuli: the stiffness of the extracellular matrix modulates the phenotypic age of oligodendrocyte precursor cells (OPCs) via the mechanoresponsive ion channel PIEZO1 34,35. In their study they demonstrate that aged OPCs taken from old mice transplanted into neonatal brain, or onto neonatal extracellular matrix (ECM) regain juvenile function. Atomic force microscopy (AFM) demonstrated that the prefrontal cortex progressively stiffens with age and that modulation of stiffness of the extracellular matrix profoundly affected OPCs. Aged OPCs cultured on soft matrices demonstrated rejuvenated phenotypes and transcriptomic changes in age-related pathways such as proteostasis, metabolism, DNA replication and DNA repair. Further functional studies in vitro and vivo demonstrated a role of mechanoreponsive Ca2+ signalling via PIEZO1. Finally, they also shed light on a potential physiological role of PIEZO1 signalling pathways regulating proliferation and the spatial density of OPCs during development. Beyond the brain, alteration in ECM stiffness and mechanosensory signalling has been implicated in ageing across multiple organ system 36.

2.7.2 Paracrine factors that locally induce ageing of cells

Senescent cells could be seen as ‘chemical bombs’ that accumulate with age and negatively influence their surrounding environment. Such cells exhibit a senescence-associated secretory phenotype, secreting high levels of inflammatory cytokines, growth factors and proteases, triggering a local immune response 37. Transplanting senescent cells is able to induce an osteoarthritis-like condition mice 38, whilst clearance of senescent cells delays the onset of ageing-related diseases, such as cancer, neurodegenerative disorders or cardiovascular diseases, among others 39–41. In human skin, the number of senescent cells correlates with biological age 42.

Mitochondrial ageing also forms part of the paracrine distribution of ageing factors: e.g. in mice, respiratory chain deficient neurons harbouring pathogenic mitochondrial DNA mutations induce degeneration in adjacent neurons 43.

2.7.3 Transmissive, blood-borne factors that are able to influence ageing at the organismal level

On an organismal level, the long history of parabiosis experiments have demonstrated that juvenile function can be transmitted from young animals to old animals, thus restoring youthful function. Again, remyelination studies involving heterochronic parabiosis conducted by Robin Franklin have led the way 44. By literally stitching together young and old mice so that they form a common circulatory system, these studies implicated cellular components, in the form of blood borne macrophages and acellular factors shared via a common blood stream in the restoration of the regenerative potential of aged mice with regards to remyelination.

Further evidence from studies involving the transfer of ‘young’ plasma to aged animals demonstrate wide-ranging functional benefits with regards to common ageing markers, across multiple organ systems, including metabolic and immunological processes 45. Measurements of Horvath’s clock demonstrated a partial reversal of age in line with the functional improvements. This provides further exciting proof of principle that external cues can be used to manipulate the age on an organismal level and has prompted the notion that unknown ‘chronokines’ may be involved in synchronization of age across organ systems.

One possibility is that these chronokines provide ‘youth signals’. More recently, a disruptive paper indicated the presence of ‘age signals’ in the blood: simple plasma exchange based dilution was able to mimic previous experiments involving youthful plasma transfer 46.

2.8 Within an organ, individual cell populations contribute disproportionately to its overall age

So far, the epigenetic and transcriptomic ageing clock measures are based on bulk data from populations of cells. Hence, this data is unable to discern ageing heterogeneity between single cells when a sample is analysed. However,it is conceivable that age is not homogeneously distributed amongst a population of cells. I.e. age could be unequally distributed within a particular cell type of an organ system.

Similarly, a heterogeneity of age could also exist across cell types. As a consequence, not all cell types would contribute equally to the overall age of an organ. Considering that with age ‘wear and tear wins over repair’, tissue resident stem cell niches are strong candidates with regards to their potential impact on the overall age of the organ

In conclusion, not all cellular components of an organ may contribute equally to the overall age of the organ, and in this respect, tissue resident stem cells are likely to be the most important component.

2.9 An integrated model of ageing

The present white paper only touches superficially on some of the important emerging concepts of ageing. Based on these, we propose the following model.

We hypothesise that 1) ageing primarily originates at a cellular level. That 2) ageing at the organ level is strongly influenced by the physical and chemical properties of the environment, and 3) that there are chronokines, global factors that distribute ‘age information’ across organ systems (Figure 5).

Figure 5. Age-information at the organismal and the organ level converges to impact ageing on a cellular level.

2.10 Ageing can be defined as the confluence of multiple cellular phenotypes

In 2013, a seminal review titled ‘The Hallmarks of Ageing’ was published, which for the first time provided a holistic model describing the cellular damage patterns that are associated with ageing 7 (Figure 6). This common landscape was updated in 202247,48 and summarises the phenotypic changes associated with age. It includes nine phenotypic hallmarks of ageing.

1. Genomic instability increases with age

2. Telomere attrition limits the proliferative capacity of cells

3. Epigenetic alterations occur, that are likely to influence gene expression and generate an ‘imprint’ of age that can be accurately correlated to the biological age of an individual (see 3.1.2)

4. Loss of proteostasis, characterized by a dysfunction in protein homeostasis

5. Disabled macroautphagy, a degradation/recycling system in eukaryotic cells, which contributes to the turnover and rejuvenation of cellular components

6. Deregulated nutrient sensing adds to the changes in cell metabolism

7. Mitochondrial dysfunction leads to decreasing cell metabolism,

8. Cellular senescence is associated with exhaustion of cellular functions

9. Stem cell exhaustion leads to reduction of the regenerative capacity at a tissue level

10. Altered intercellular communication induced changes in intercellular behaviour

11. Chronic inflammation is reflected in an age-related increase in the levels of pro-inflammatory markers in blood and tissues, is a strong risk factor for multiple diseases

12. Dysbiosis is an “imbalance” in the gut microbial community that is associated with age and disease

Figure 6. The 12 hallmarks of ageing 48.

2.11 The ultimate goal of reversing age is the reversal of all ageing hallmarks

As outlined in 2.9, ageing is a multifaceted process and the relationship between the different ageing hallmarks or damage patterns is still poorly understood. The most successful strategy for controlling age will affect all the various ageing phenotypes simultaneously. This requires a holistic understanding of the various cellular repair mechanisms that are able to address the ageing hallmarks. The first step is to understand the genes and gene regulatory networks that are involved and control these processes.

Taken together, we need to consider all ageing phenotypes. However, whilst comprehensive rejuvenation may only be possible when all phenotypes are addressed, modulating selective phenotypes is also highly desirable and may enable important advances in the treatment of particular conditions and disease.

2.12 Learning from the extreme forms of accelerated ageing

Extreme perturbations of ageing occur when ageing biomarkers and phenotypes are reversed during cell reprogramming 29 and in the context of genetic progeria syndromes 7,22,49. Both provide powerful tools to investigate the ageing program, which will be fully leveraged in our work program.

Progeria syndromes are a group of rare genetic diseases which lead to accelerated ageing phenotypes. Most are caused by mutations in specific enzymes that take part in cellular repair mechanisms and e.g. lead to defective DNA repair. A good example in which Horvath’s epigenetic clock has been validated is Werner’s disease 23. Werner’s syndrome is caused by a mutation in the WRN gene, a DNA helicase belonging to the RecQ-like type 3 family. However, it has several disadvantages, including the fact that it modulates the DNA. Moreover, Werner’s disease results in relatively slow DNAm acceleration, with an onset after puberty.

Hutchison-Gilford Progeria Syndrome (HGPS), which has been termed “the rosetta stone of ageing” 29, results from a mutation in Lamin A. Alternative splicing of mutated Lamin A leads to expression of progerin, a peptide that causes structural changes of the nuclear membrane.

The mechanism by which progerin leads to cellular ageing remains unclear. One possibility is that because of the defective nuclear membrane, chromatin is exposed to factors, which are usually strictly excluded from the nucleus. An alternative hypothesis is that the progerin-induced deformation of the nucleus leads to conformation changes of the DNA, which cause epigenetic dysregulation; this in turn results in altered transcription and ultimately the ageing phenotype. Indeed, a recent report indicated that disorganisation of heterochromatin at the lamina acts as a driver of human ageing 50 and provides a mechanism for degenerative changes such as seen in fatty liver disease 51.

Such alterations are likely to affect DNA integrity, and in turn may lead to accelerated DNA methylation. As a result, the intricate balance between damage and repair within the cell deteriorates and ageing-related damage accelerates. This is echoed by findings that demonstrated defects in the repair of DNA double strand breaks in the context of progerin 52.

2.13 Protein-driven ageing is highly relevant to physiological ageing

To start with, Progerin spontaneously accumulates as humans grow old 53. Secondly, Progerin-induced ageing can be prevented by treatments that slow or reverse physiological ageing. Rapamycin slows Progerin-driven ageing, increases the lifespan of genetically heterogenous aged mice 54 and is currently being investigated for age-decelerating effects in a clinical dog trial 55. Some of its effects are likely the result of increased cellular repair processes, such as autophagy 56,57. Cellular reprogramming can successfully reprogram fibroblasts from HGPS patients into perfectly viable pluripotent stem cells 12,29, demonstrating that even extreme forms of phenotypic ageing can be reversed.

Recent experiments indicate that the rapid ageing process induced by progerin and the resulting reduction of lifespan can also be modulated in live organisms: genetically modified mice which express progerin were crossed with mice in which the Yamanaka reprogramming factors could be activated by treatment with doxycycline 13. These mice demonstrate an accelerated ageing phenotype and reduced survival, which can be partially ameliorated by low level activation of the reprogramming cassette. This is the first demonstration that in vivo partial reprogramming can extend mammalian lifespan.

Of note, the accumulation of progerin has only minor effects on the epigenetic age measured by the classical Horvath’s clock 58, but can be predicted with a revised clock algorithm 59.

3. An introduction to clock.bio

3.1 clock.bio aims to leverage recent progress in rejuvenation research to design regenerative treatment strategies for age-related disease and ageing itself

In chapter 2 we outlined the basic concepts that have influenced our thinking about age and age reversal. In this chapter we dive deeper into some of the pivotal studies (Figure 7) that shape the program for clock.bio, an enterprise that seeks to leverage recent progress to develop age-reversing treatments.

Figure 7. Pivotal studies in the scientific field of anti-ageing.

3.1.1 Definition of ageing hallmarks

The seminal 2013 review titled ‘The Hallmarks of Ageing’ 7 (Figure 8) provided a holistic model for ageing which was embraced as a framework for scientific investigation and therapeutic development by academics and industry respectively. It highlights nine cell-intrinsic hallmarks of ageing including genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication.

Figure 8. The hallmarks of ageing 48.

3.1.2 Epigenetic ageing clock

The next pivotal step forward was the development of accurate biomarkers for ageing and mortality risk, which enable the study of age and age-related interventions in much shorter time frames. Systematic efforts to find ageing biomarkers identified epigenetic signatures that are most correlated with biological age. Specifically, DNA methylation, particularly at 5’-C-phosphate-G-3’ (CpG) sites, is modified predictably with age. Several algorithms are able to accurately predict biological age on the basis of methylation patterns. Whilst some are predictive only for a specific cell type 19, others, such as Horvath’s clock demonstrate a remarkable consistency across cells of various tissues 29. Horvath’s clock integrates methylation of 353 CpG sites and predicts age with an accuracy of ±3.6 years, and ticks faster during development than during adulthood (Figure 9).

Figure 9. Horvath’s epigenetic ageing clock is highly correlated with ageing and ticks faster during development (0–20yrs) than during adulthood 29.

Following Horvath, in 2017, Thomas Stubbs and Wolf Reik developed an equivalent biomarker for mice 60 and in 2019, Meng Wang and Bernardo Lemos identified the first cross-species ageing clock for humans, mice and dogs based on DNA methylation of ribosomal DNA 61.

Characterization of Horvath’s multi-tissue ageing clock revealed it was close to age ground zero for embryonic and induced pluripotent stem cells, but insensitive to the cellular ageing hallmarks of telomere attrition and cellular senescence 29,62. However, in 2019 Ake Lu and Steve Horvath defined a DNA methylation-based estimator of telomere length that surprisingly is more correlated with age than actual telomere length 63. A comprehensive cross-species mammalian clock, which as the name suggests is applicable to all species of the mammalian kingdom 64. These will aid identification of therapeutic interventions targeting conserved ageing processes facilitating their translation to human clinical applications.

In 2018, Morgan Levine and Steve Horvath developed a DNA methylation based ageing biomarker incorporating composite clinical measures of phenotypic age, called DNAm PhenoAge 65. This blood-based ageing biomarker outperforms previous ageing biomarkers for predictions of a variety of ageing outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer’s disease. In 2019, Ake Lu and Steve Horvath defined the ageing biomarker DNA methylation GrimAge 66, which outperforms DNAm PhenoAge with respect to predictions of time-to-death, time-to-cancer and time-to-coronary heart disease.

Though tissue specific and sometimes less accurate, transcriptomic ageing clocks exhibit greater sensitivity to progerin-driven ageing 67,68. Cellular senescence and mitochondrial DNA mutations are ageing processes sufficient to accelerate phenotypic ageing and reduce lifespan in murine models 26,39, though DNA methylation or transcriptomic based estimators have not yet been defined. However, both ageing processes are easily quantified with alternative biomarkers 26,37.

3.1.3 Partial reprogramming reverses epigenetic age and restores juvenile function

The identification by Yamanaka in 2006, of four critical genes (Oct-4, Sox2, KLf4 and c-myc) that can revert adult cells to embryonic-like cells 69, even in adult cells from centenarian or super-centenarians individuals 12,70, ignited genuine optimism that reversing the ageing process might indeed be possible. In 2011, Guang-Hui Liu and Juan Belmonte expressed OSKM in human progeroid cells and successfully reverted them to pluripotent cells that were devoid of the ageing phenotypes 71, and in 2016, Alejandro Ocampo and Juan Belmonte dramatically increased lifespan in progeroid mice with pulsed expression of OSKM 13.

Figure 10. Optic nerve regeneration in young mice. (left) Crush site marked by ***. (right) Rescue of visual acuity in old mice following AAV delivery of an OSK expression cassette 14.

In 2019, Yuancheng Lu and David Sinclair, using OSK, reversed epigenetic age and restored blindness to injured and aged mice 14 (Figure 10) and in 2020, Tapash Sarkar and Vittorio Sebastiano used pulsed expression of OSKMLN to reverse epigenetic age and restore youthful functions to aged human muscle stem cells 72. In a Reddit AMA, David Sinclair shared that he plans to pursue clinical application of partial reprogramming for glaucoma 73.

Figure 11. >40 years reversal of epigenetic age following 15 days exposure to OSKM without comprehensive activation of pluripotency gene clusters 74.

In 2018, Nelly Olova and Tamir Chandra showed that 15 days reprogramming reversed epigenetic age by >40 years without loss of cell identity 74(Figure 11).

Figure 12. >30 years reversal of epigenetic age in fibroblasts expressing the pluripotency marker SSEA4 following 13 days exposure to OSKM and 4 weeks reversion 75.

Wolf Reik and Diljeet Gill demonstrated that 13 days of OSKM partial reprogramming of human fibroblasts followed by 4 weeks reversion reversed epigenetic age by >30 years, but that the extent of epigenetic rejuvenation diminished with further reprogramming 75 (Figure 12).

3.1.4 Phenotypic age-reversal in progeria using partial reprogramming

As outlined above, a number of genetic disorders termed progeria, partially recapitulate classical ageing phenotypes without necessarily affecting the epigenetic biomarkers of ageing. To maintain or restore youth, ultimately it is the phenotype that needs to change.

Progeria models, such as HGPS provide an opportunity to distinguish between mechanisms that act on the biomarkers of ageing and those that act on the phenotypic changes that are associated with age. Importantly, partial reprogramming has been shown to benefit phenotypic age in HGPS-mice as well as extend their lifespan 13 (Figure 13). In fact, this was the first demonstration that partial reprogramming can affect the lifespan of an organism.

Figure 13. Progeroid mice exhibit reduced phenotypic ageing (left) and increased lifespan (right) in response to dox-inducible cycles of OSKM expression 13.

3.1.5 Chronokines are systemically transmissible ageing factors

In 2005, Irina Conboy and Thomas Rando showed that exposure to a youthful circulatory system by heterochronic parabiosis can restore proliferation and regenerative capacity to aged muscle stem cells and hepatic progenitor cells 76. In 2011, Saul Villeda and Tony Wyss-Coray then showed that heterochronic parabiosis can reverse the loss of neurogenesis in aged mice 77. In 2012, Julia Rückh and Robin Franklin demonstrated that heterochronic parabiosis rejuvenates regeneration by remyelination in the ageing central nervous system 44. In 2013, Saul Villeda and Tony Wyss-Coray showed that heterochronic parabiosis reverses age-related impairments in cognitive function and synaptic plasticity in mice 78 and Tony Wyss-Coray coined the term ‘Chronokines’ to describe rejuvenating circulatory factors. In the same year, Francesco Loffredo and Richard Lee showed heterochronic parabiosis reverses age-related cardiac hypertrophy in mice 79.

In 2016, Justin Rebo and Irina Conboy showed that blood heterochronicity, without the parabiotic sharing of organs and environment, quickly rejuvenates muscle and liver but not hippocampal neurogenesis or cognitive performance of old mice 80. In 2020, Melod Mehdipoyr and Irina Conboy showed rejuvenation of hippocampal neurogenesis, muscle repair, liver adiposity and fibrosis simply by exchanging old plasma for saline-albumin 46. An FDA-approved equivalent called therapeutic plasma exchange (TPE) anecdotally reduced incidents of viral diseases to zero in patients over the course of a year when just for flu-related hospitalizations, ~60% were from the same age group. This suggests that dilution of autoregulatory proteins in old blood is largely responsible for the rejuvenation benefits of heterochronic parabiosis and blood heterochronicity. In 2020, Alana Horowitz and Saul Villeda showed that improvements in neurogenesis and cognition in the aged brain in response to exercise are also mediated by blood factors released by the liver 81.

In 2020, Steve Horvath and Harold Katcher reported the reversal of epigenetic age in rats by >50% (Figure 14) through administration of a blood plasma fraction from young rats 45. The treatment also restored organ function biomarkers to youthful levels and restored youthful cognitive function.

Figure 14. Dramatic reversal of epigenetic age in rats following intravenous injection of circulatory factors 45.

In 2019, Nicholas Schaum and Tony Wyss-Coray showed that gene expression in the muscle and adipose tissue correlates most with age-related changes in the mouse plasma proteome 82 (Figure 15).

Figure 15. Correlation between plasma proteins and organ-specific gene expression, colored by organ type 82.

In 2019, Benoit Lehallier and Tony Wyss-Coray characterized the plasma proteome from >4000 young adults to nonagenarians, revealing marked non-linear alterations with age 83 (Figure 16). Three waves of changes in the fourth, seventh and eight decades of life were uncovered.

Figure 16. Number of plasma proteins differentially expressed during ageing with three local peaks at the ages of 34, 60 and 78 (left). Top 10 differentially expressed proteins at age 34, 60 and 78 (right) 83.

Tony Wyss-Coray (Alkahest) has progressed circulatory factors into phase 2 trial for Alzheimer’s, whilst Amy Wagers (Elevian), Irina Conboy (Juvena Therapeutics) and Thomas Rando (Fountain Therapeutics) are also linked to commercial ventures for clinical development of circulatory factors for age-related diseases. Irina Conboy is also progressing TPE to phase 2 trial for vaccination outcomes and improved recovery from viral illnesses.

3.1.7 Senolytics

Figure 17. A combination of senolytic molecules increase post-treatment survival by 36% in already aged mice 84.

In 2018, Ming Xu and James Kirkland showed that a combination of senolytic molecules dasitinib and quercetin reversed accumulation of senescent cells in human tissue in vitro and increased post-treatment survival by 36% in already aged mice 84 (Figure 17).

Figure 18. Relationship between epigenetic clock and mitochondrial function evaluated in in vitro-cultured neonatal human dermal keratinocytes (HDK) derived from foreskin: impairment of mitochondrial function with CCCP results in increased epigenetic age whilst enhancement with bezafibrate (Beza.) reduces it (left). Similarly, treatment with Beza. Increases lifespan of cells whilst CCCP shortens it (right).

Since 2014, Steve Horvath and Ken Raj have empirically investigated the cellular and molecular mechanisms of the epigenetic ageing clock 28 (data including but not limited to Figure 18). This has led to the tentative understanding that ‘mitochondrial dysfunction and deregulated nutrient sensing drives the depletion or dysfunction of stem cells, resulting in epigenetic ageing’.

Figure 19. Epigenetic age of mouse cells following exposure to restriction endonucleases that increase non-mutagenic double strand breaks, as measured on two variant clocks 85.

In 2019, Jae-Hyun Yang and David Sinclair showed that the process of double-strand break DNA repair also accelerates the epigenetic ageing clock (Figure 19) and erodes the epigenetic landscape 85, suggesting this could also be driving epigenetic ageing in stem cells. This finding also helps reconcile the wear and tear and programmed theories of ageing.

3.1.8 Hotspots of transcriptomic ageing

In 2019, Nicholas Schaum, Angela Oliveira Pisco and Tony Wyss-Coray published bulk and single cell transcriptome data for the entirety of the ageing mouse 82,86. This revealed systemic and organ specific changes in molecular pathways including hallmarks of ageing (Figure 20), changes in cellular composition of organs, changes in the plasma proteome and their linkage to gene expression changes in specific organs.

Figure 20. Systemic and organ-specific changes in molecular pathways during mouse ageing, including changes in hallmarks of ageing 82.

3.1.9 Pharmacological modulation of lifespan in model organisms

Recently, Caglar Berkel and Ercan Cacan interrogated the DrugAge database (https://genomics.senescence.info/drugs/) 87, which ‘contains an extensive compilation of drugs, compounds and supplements (including natural products and nutraceuticals) with anti-ageing properties that extend longevity in model organisms (Figure 21).’ This yielded a range of compounds that are able to regulate lifespan in multiple species. After all, lifespan may turn out to be something that can be modulated pharmacologically, at least in other species.

Figure 21. A compilation of drugs, compounds and supplements that extend or shorten longevity in model organisms 87.

3.2 Proof of translatability: reversal of epigenetic age in human trials

A key question is whether the observations gained in preclinical studies also translate into human. Translatability is supported by an increasing number of studies. For example, the anti-ageing small molecule Rapamycin is able to regulate T-cell exhaustion but with variable results 88. Topical rapamycin can reduce senescent markers and improve the physical appearance of skin in a randomized trial including 17 participants 89. There have been no scientific breakthroughs in skin anti-ageing since the 1980s, when Albert Kligman demonstrated significant clinical improvement of photoaged skin with retinoids 90–92.

Timothy Hore and Wolf Reik showed retinol promotes reprogramming of mouse embryonic fibroblasts to induced pluripotent stem cells 93, though large-scale clinical trials are still needed to confirm whether retinoids can reverse chronological ageing in skin 94.

Amongst the recent and ongoing initiatives, the results of Greg Fahy and Steve Horvath are noteworthy as they demonstrated epigenetic age reversal in humans with a combination of clinically approved drugs 95 (Figure 22). Notably, following completion of the treatment regime, the GrimAge clock remained persistently reversed by >2 years.

Figure 22. Reversal of epigenetic age in healthy men in response to a combination of rhGH, DHEA and Metformin, according to blood measurements. Changes were observed in Horvath’s multi-tissue methylation clock (a) Phenoage clock (b) GrimAge clock (d) and Hannum’s blood-based ageing clock © 95.

4. clock.bio’s approach and objectives

Technical advances in research tools now enable unbiased genetic and therapeutic screens for ageing. Unbiased screens would support a deep mechanistic understanding of ageing biomarkers and therapeutics needed to help overcome later challenges in clinical development. We believe the field is ready for disruptive innovation and aggressive pursuit of a vision to extend healthspan by 20 years based on biomarkers of ageing in a Phase 3 trial by the end of this decade (Figure 1).

4.1. clock.bio in the context of other rejuvenation initiatives

Few encounters in life are transformational. For the author, a 45-minute meeting with Rick Klausner in 2020 that spontaneously extended to 4 hours was such a watershed moment. It led to an investment in bit.bio; yet more impactful was the generous time and mentorship that came with it. In May 2021, the topic of discussion strayed away from the question of the transcriptional programs of cell identity and an experiment investigating epigenetic mechanisms was discussed which led to the subsequent discovery of what is now the foundation of clock.bio. This revived Rick’s interest in ageing and prompted the author to write the first version of the present white paper. A few months later, an eclectic group of some of the leading scientists working on ageing and rejuvenation were invited together in Palo Altos, ultimately leading to foundation of Altos. The author assembled the initial team in the UK, consisting of Robin Franklin, Ken Raj, Wolf Reik, and Thore Graepel who now are all part of the senior scientific leadership at Altos. The UK team is a good representation of the breath of Altos, ranging from partial reprogramming to mechanisms of homeostasis, and the influence of environmental factors. Altos represents a true moon shot that leverages the expertise of some of the most distinguished scientists in a collaborative environment.

Still, there is sufficient space for further initiatives. Rejuvenation biology is complex, and a plethora of targets exist that are worth pursuing. Small and nimble teams such as in Shift Biosciences must not be under-estimated, as well as one mustn’t disregard other big bets in the space such as Calico. More recently, big pharma has also developed an awareness of the profound impact that rejuvenation biology can have when it comes to designing treatments for age-related conditions.

4.2 clock.bio’s approach is orthogonal and complementary to the field

Most initiatives outlined above follow a traditional investigator-led and hypothesis-driven paradigm. In contrast, clock.bio’s approach is inductive, based on the following set of axioms.

1. Embryonic/pluripotent stem cells are at age ground zero, a state of ‘perfect’ homeostasis that allows them to compensate the constant pressures of entropy and enables them to repair the damage occurring.

2. Ageing is a program that gradually downregulates the cell-intrinsic repair mechanisms present at early stages and thus leads to accumulation of cellular damage.

3. Re-activation of the repair mechanisms is able to restore cell function and lead to rejuvenation when all ageing hallmarks are addressed.

We believe that a comprehensive and unbiased understanding of the repair/rejuvenation mechanisms in human cells stands the best chance of success with regards to developing effective preventative and therapeutic interventions. Further differentiating factors are as follows.

· clock.bio’s primary aim is not to understand ageing but to focus on finding out how to prevent or how to repair the phenotypic changes associated with ageing.

· clock.bio does not pursue a hypothesis-driven approach; it seeks to generate a complete understanding of the genes and gene-regulatory networks involved in repairing the damage associated with ageing. This will be achieved using large-scale experimentation combined with advanced statistical and ML tools.

4.3 clock.bio’s mission is to design and develop targeted and translatable therapies for age-related conditions and age itself.

Our bold aim is to extend health span by 20 years based on a biomarker in a clinical trial before the end of the decade. This will be facilitated by

  1. A proprietary ageing-intervention able to force-age iPSCs
  2. Unbiased genome-wide screens for cell-autonomous rejuvenation biology
  3. Identification of KB rejuvenation targets
  4. Validation of KB-target genes in human cell models
  5. Further validation of treatment approaches using primary human tissue and animal models (including models of age-related diseases)
  6. Prioritisation of therapeutic leads
  7. Mapping drug targets from genomics for translation into medicines
  8. Translation of therapeutic leads into clinical application

4.4 clock.bio’s first scientific objective is to decode all repair/rejuvenation programs in human cells in the shortest possible time frame

Figure 23. outline of clock.bio’s approach for the identification of rejuvenation/repair genes: 1. clock.bio leverages a proprietary model which transiently ages pluripotent stem cells without inducing a change in cell identity; 2. Stem cells repair the age-related damage, which enables interrogation of genes using CRISPR and pharmacological approaches. 3. With the help of the transcriptomic ageing measure candidate KB-target genes that modulation of transcriptomic age are identified. 4. Treatment of aged neurons with pharmacological modulators of KB targets shows repair of ageing hallmarks.

Significant progress has been made in the development of ageing biomarkers and candidate rejuvenation interventions. clock.bio is rapidly identifying the complete set of rejuvenation genes and programs in human cells. This is facilitated by a novel screening paradigm outlined below. The resulting KB-rejuvenation targets from the screens are control and effector genes involved in repair processes that can prevent and reverse the damage associated with the ageing hallmarks (Figure 23).

A systematic and rigorous understanding of repair/rejuvenation biology and how KB-targets relate to individual, or combinations of ageing hallmarks allows the design of treatment strategies for age-related conditions. For example, neurodegenerative conditions have been associated with defects in autophagy; aberrant nutrient sensing is a root cause of type 2 diabetes mellitus. Preventative or restorative treatments for these conditions will focus on the specific and relevant hallmarks.

When it comes to designing rejuvenation therapies to tackle ageing, the most successful approaches are likely those that can beneficially modulate the complete set of ageing hallmarks. Proof of concept that this may be possible is indicated by several approaches that were able to modulate various aspects of phenotypic and epigenetic ageing 26,34,85.

4.5 Embryonic stem cells hold the key to cellular rejuvenation

We believe that embryonic stem cells (or iPSCs) hold the key to unlocking rejuvenation biology. At the start of embryonic development, when pluripotent stem cells are generated, their age resets to ‘ground zero’, as eloquently proposed by Vadim Gladyshev 15. This is critical for the survival of a species, as any drift in the age of embryonic stem cells over generations would ultimately risk its extinction. Because the age of the gametes is not as tightly controlled and indeed a certain amount of ageing has been observed in gametes, the cellular age of embryonic stem cells is reversed when these are formed 96. It is very likely that similar processes are at work as those that are responsible for the reversal of age during Yamanaka reprogramming.

4.5.1 clock.bio’s proprietary high throughput discovery paradigm is based on the observation that embryonic stem cells can be force-aged to display the hallmarks of age

Stem cells exist in a highly dynamic state that can counteract the constantly occurring damage that puts pressure on all living cells. Age ‘ground zero’ could therefore be described as a state of ‘perfect homeostasis’ where repair mechanisms are in balance with the forces of entropy.

Because the state is dynamic, we hypothesised that by increasing the ‘ageing pressure’, the repair processes that maintain age ground zero in stem cells could be overcome and the cells would display some of the hallmarks of ageing. Indeed, our experiments demonstrated that pluripotent stem cells can be artificially aged. clock.bio’s ageing intervention results in multifactorial changes affecting all cell-autonomous hallmarks of ageing. We found that forced-age PSCs display (Figure 24):

· Genetic instability in the form of increased DNA-double strand breaks

· Telomere attrition and changes in regulatory genes

· Epigenetic alterations, reflected in significant changes in DNA methylation leading to increased Shannon Entropy

· Loss of proteostasis – confirmatory MS experiments are ongoing

· Changes in macroautophagy with decreases in regulatory genes

· Deregulated nutrient sensing reflected in e.g. altered gene expression

· Mitochondrial dysfunction leading to a decrease of mitochondrial activity

· Cellular senescence, increase in the number of senescent cells

· Stem cell exhaustion, a clear reduction in proliferation rate

clock.bio’s ageing model represents the cell-intrinsic ageing hallmarks

4.5.2 The rejuvenation/repair programs active in pluripotent stem cells are able to reverse the hallmarks of ageing

Whilst the highly active processes maintaining homeostasis in pluripotent stem cells can compensate the usual forces of entropy, when stem cells are forced to age, the repair mechanisms can be overwhelmed. As a consequence, damage accumulates, which leads to the manifestation of the cell-autonomous hallmarks of ageing.

What happens if the artificial ‘ageing pressure’ is relieved? Will the balance between wear and tear and repair tip back to the mechanisms that are involved in maintaining age ground zero in stem cells? If this were the case, force-aged pluripotent stem cells should be able to reverse the ageing hallmarks. Indeed, this is what we found:

Pluripotent stem cells have the capability to rapidly repair age-associated damage and restore ‘ground zero age[*].

4.5.3 KB-screens enable large-scale discovery of repair mechanisms involved in rejuvenation

To maintain age ground zero, stem cells likely need to be able to repair all forms of damage that occur in the usual context of ageing. The cell-autonomous rejuvenation observed in pluripotent stem cells provides an ideal experimental paradigm to read out the repair mechanisms that are responsible for reversing the ageing hallmarks. Unlike partial reprogramming using Yamanaka factors, the ageing intervention and subsequent rejuvenation does not involve a change in cell identity for the cells to reach age ground zero. This allows a clear separation for the discovery of the processes involved in repair/rejuvenation from those involved in cell fate changes that occur during Yamanaka/partial reprogramming.

clock.bio’s proprietary rejuvenation/regeneration target discovery platform stands out with regards to the following characteristics.

· Scale: PSCs are highly proliferative and allow for large-scale experimentation

· Speed: the hallmarks of ageing appear within one week of applying the ageing pressure and rejuvenation can be observed within a further week

· Contrast: unlike partial reprogramming where rejuvenation and cell identity changes are somewhat conflated and not all cells are affected to the same extent, the present paradigm involves a homogenous population of stem cells in which rejuvenation occurs without cell fate change

· Functional understanding: the functional screening paradigm leads to a genome-wide understanding of cause-effect relationships with regards to drivers of rejuvenation

· Comprehensive ageing read out: our single-cell transcriptomic ageing measure integrates information across ageing hallmarks and is designed as a surrogate marker for phenotypic age

· Selective disease-relevant read outs: focussed screens are directed towards the restoration of individual ageing hallmarks and enable discovery of ‘disease-specific’ repair processes

· PoC: our CRISPR-screens have already identified a number of rejuvenation targets, which when modulated appropriately are able to restore a youthful phenotype in aged somatic cells.

4.5.4 Identification of genes involved in rejuvenation

A key advantage of the KB approach is that stem cells are highly proliferative and allow for large-scale experimentation and unbiased screens. To identify rejuvenation targets, we have developed transcriptomic ageing signatures that are able to measure ‘ageing and rejuvenation’ in single-cell sequencing experiments. The signatures range from a surrogate marker of ageing that extends across all ageing hallmarks to composite markers that pursue a more targeted approach and tailored towards individual ageing hallmarks.

Iterative genome-wide CRISPR-ko/a/i screens and combinatorial screens will provide a holistic overview of rejuvenation/repair processes in human cells. We expect that this approach will identify the vast majority if not all gene sets involved in the repair processes that are able to reverse individual or combinations of ageing hallmarks.

4.3.6 Identification of genes that repair specific disease-relevant ageing hallmarks

As mentioned above, screens can also be designed to target specific ageing hallmarks. This may be useful to identify repair processes that are able to restore particular disease-relevant damage at the cellular level. For example, if one would want to restore deregulated nutrient sensing to address, e.g. age-associated metabolic syndrome, the screen could be directed towards such a read-out to identify the repair processes involved. Similarly, screens can be designed to target autophagy (e.g. neurodegenerative conditions), metabolic changes (e.g. immunological targets), etc (Figure 25).

Figure 25. Schematic representation of a comprehensive atlas of disease and rejuvenation targets that will be constructed from clock.bio’s data.

4.5.5 Combinatorial perturbation of genes in cells

An unbiased, comprehensive understanding of rejuvenation biology is enabled by recent tools that allow genetic screening at a large scale. In particular, protocols for pooled CRISPR screening with single cell transcriptome profiling, including CROP-seq 97, Perturb-seq 98 and CRISP-seq 99 have opened the door to gain unprecedented insight into the biology of ageing.

CRISPR screening can take the form of gene activation (CRISPRa), gene inhibition (CRISPRi), or knockout (CRIPR-ko). Recently, the power of these tools was significantly increased by the development of combinatorial screens, including screens that enable simultaneous activation and deactivation of genes in cells, thus removing the final bottleneck with regards to studying gene-gene interaction on a single cell large throughput basis.

4.5.7 Experimental read outs are able to target individual ageing hallmarks and/or integrate across hallmarks

Experimental read outs form a critical component of gene perturbation studies. We consider recent advances by our group a quantum leap in this regard. The development of transcriptomic ageing measures that can be applied at a single cell level is one of the most important approaches for the proposed studies, as they provide a readout that is amenable to our screening approaches. (Figure 26).

Figure 26. Cells display a range of ageing phenotypes based on clock.bio’s transcriptomic ageing clock

In addition, we incorporate phenotypic ageing readouts in our screens to ensure a comprehensive assessment of ageing phenotypes. These include but are not limited to, e.g measurements of epigenetic age, mitochondrial function, senescence, telomere length and structural components of cells that are relevant to cell ageing.

4.6 Identification of KB-rejuvenation targets using genetic perturbations

Carefully designed gene perturbation screens provide a comprehensive picture of phenotypic and epigenetic rejuvenation. Applying the approach outlined above, we have identified target genes that are able to modulate the measure of our transcriptomic ageing signature (Figure 27). These fall into two classes.

  1. KB-targets that promote ageing

These are gene or gene combinations that promote ageing at a phenotypic and epigenetic level. Inhibiting these genes may beneficially influence phenotypic or epigenetic ageing and induce rejuvenation. This knowledge will substantially add to the interpretation of age-reversing screens and potentially to the overall understanding of the ageing process.

  1. KB-targets that reverse ageing

Genes that reverse ageing phenotypes are targets prime candidates for rejuvenation. We believe that combinatorial screens will be of particular importance as will the discovery of genes that are able to restore youthful states in mitochondria. (We have made a case for the importance of mitochondria as the bearer of the second ageing program.)

Based on the principles outlined above, we are generating a comprehensive dataset that maps out the function of the age-modulating biology in human cells.

Figure 27. KB-targets identified in a gene knock-out sceen: targets KB1,2 enhance ageing, knockouts of genes KB3–15 promote rejuvenation.

4.7 Systematic and holistic validation of rejuvenation/repair targets in scalable human cell models

The proposed systematic discovery of repair/rejuvenation targets leads to holistic insights of the genes and gene regulatory networks involved. We are continuing a similar structured and comprehensive path and leverage scalable human cell models to systematically triage candidate targets for the development of both rejuvenation and disease-specific therapeutic interventions. This is enabled by bit.bio’s opti-ox portfolio of human cells and disease models. bit.bio’s cells will again be force-aged with clock.bio’s primary ageing intervention. We have conducted deep phenotypic characterisations of e.g. aged iPSC-derived neurons and demonstrated that they faithfully reproduce the ageing hallmarks. As they display the same sorts of damage that are observed in physiologically aged cells, they serve as a good model for validating rejuvenation/repair candidate target genes. Initial validation will take three forms.

1. We will leverage our CRISPR technology to perturb proposed targets in the opposite way of their effects in iPSCs. E.g., if a gene knockout in iPSCs resulted in a defect of age-reversal, we will activate the gene in aged somatic cells; if a gene knock out in iPSCs resulted in accelerated age-reversal, we will inhibit the gene in aged somatic cells and so forth.

2. We will conduct experiments using drug candidates that are known to modulate the identified target genes following similar principles.

3. We will leverage bit.bio’s opti-ox disease model portfolio (and create further model systems) to further study the effects of target candidate.

4.6 Validation of targets in the literature and ML analysis of large datasets

We have built and are constantly updating a database that will integrate our results across experiments and connect them to the wider literature. The functional data acquired in our screens provides the opportunity to leverage ML approaches in order to generate a more comprehensive understanding of rejuvenation biology. Once the model is sufficiently trained, we will connect it with highly curated literature data. Our aim is to decode rejuvenation biology in the context of disease and help refine the design of preventative and restorative treatments.

4.8 Further validation in scarce human primary cells and animal models

The next validation step involves scarce human primary cell materials and animal models. We will assess the function of candidate targets in scarce primary human cells derived from aged donors. To achieve an understanding at the organismal level, we will leverage animal models in the form of aged animals and disease models. Of note, our main mitigation of the often-quoted translation gap that is caused by differences between species is that the entirety of our systematic and early discovery work is based on human cells. Non-human species will primarily serve as validation; in other words,

to optimise translatability, clock.bio is reversing the traditional target and drug discovery process, taking a ‘human first’ approach.

4.9 Prioritisation of therapeutic leads using ageing hallmark biomarkers

The comprehensive, multi-dimensional and functional datasets and analyses will enable to view ‘the entire room’ of rejuvenation biology. We propose that this is the best starting point for designing interventions for the extension of health span.

Ultimately, the knowledge that clock.bio is developing covers the ‘repair kit’ of human cells. While still speculative, we expect that this knowledge can support the development of interventions targeting age-related disease. For example, understanding how to reverse the age-related damage in neurons that ultimately enables dementias to manifest might be an important aspect with regards to prioritising therapeutic targets.

A key advantage of this knowledge may turn out to be that it is agnostic. clock.bio’s approach does not favour one target above the other. It follows a discovery process that minimises the wriggle room for the preference of individual research interests.

clock.bio’s knowledge of regeneration targets may hence turn out to be very useful for making strategic and scientific decisions with regards to prioritising a pipeline aiming to develop treatments for age-related conditions.

Figure 28. Quantification of individual ageing hallmarks allows precise classification and prioritization of leads for later clinical development.

Comprehensive quantification of individual ageing phenotypes in response to CRISPR-based therapeutic leads or other leads will enable their precise classification and prioritization for later clinical development (e.g. pan-hallmark or hallmark specific lead). For some ageing phenotypes, single cell quantification is achievable (Clock surrounded by a cell membrane, Figure 28). Quantification of individual ageing phenotypes will be facilitated based on the research outlined below.

· DNA methylation-based biomarkers of ageing (epigenetic 29, telomere 63)

· Single cell epigenetic/transcriptomic ageing

· Single cell senescence 100

· Single cell telomere 101

· Single cell mitochondrial DNA ageing 102

· Epigenetic noise or drift 85

· Stem cell depletion 44

5. Proof of concept

To demonstrate the validity of our approach, we have included illustrative data. We tested several targets that have been identified in one of the KB-iPSC screens outlined above. Class 1 KB-targets are genes that following knockout either resulted in an increase of the ageing measure and/or prevented the reversal of the ageing measure. Class 2 KB-targets are genes that following knockout prevented ageing of iPSCs. Pharmacological modulation of targets with known substances resulted in the restoration of ageing hallmarks, e.g. in the form of the mitochondrial presence and activity in aged human neurons.

These findings close the loop and provide some validation of the underlying paradigm and hypothesis. The repair processes that are able to maintain age zero in human pluripotent stem cells can be efficiently decoded and re-purposed to repair the damage associated with the hallmarks of ageing. The findings are remarkable in so far as they demonstrate the ability to bridge from the transcriptomic read-out that has been used to identify these KB-targets to functional changes with regards to the presence and functional of mitochondria.

The bulk of the work remains and consists in further validation of targets in different paradigms.

Our platform is validated from identification of rejuvenation factors through to therapeutic effects in human cells

7. Conclusion

This white paper attempted an overview of the current state of rejuvenation biology – a field that has the potential to address nearly unlimited opportunities and unmet clinical needs. The authors acknowledge that is difficult to strike the right balance between providing sufficient theoretic and scientific detail without overburdening the reader.

It is not usual for a bio company to broadly communicate its scientific approach. Our rationale for doing so is a fierce belief in transparency and the benefit of wider discussion and criticism from the scientific community. At the same time, clock.bio’s growing portfolio of IP and the sensitivity of pharma around IP provides sufficient moats.

We believe in partnership. – The faster we can bring new treatments into clinical practice the better. Internally, clock.bio is structured as a partnership of scientists and operators bound together by a shared outlook on ethics. We strongly believe that rejuvenation treatments need to be accessible to everyone, everywhere.

clock.bio’s approach, aims to ‘turn on the light in the room’ to generate comprehensive visibility and understanding of rejuvenation biology. This differs from the traditional ‘shining a torch into the darkness’ approach, which is sometimes the constraint of focussed and hypothesis-driven science. We believe that a comprehensive understanding of all relevant repair processes is the best starting point for designing regenerative and healthspan-increasing treatments.

8. Acknowledgements

First and foremost, as a surgeon at heart, I would like to acknowledge my patients, who constantly remind me about the importance of our work. Thank you for your inspiration and the privilege to be part of your healing journey. Your insights provide the guidance of my research.

I am grateful to the members of my academic lab, clock.bio, and bit.bio for your relentless commitment to scientific rigor and the depth of your scientific knowledge.

A special thanks to Dr Daniel Ives, who has inspired me to look deeper into the ageing and rejuvenation field. Daniel runs a cutting-edge company called Shift Bioscience and is a pioneer in the field. He helped to draft the first version of this white paper, which re-sparked the interest of Rick Klausner in the ageing field.

Thank you also to Jonathan Milner and the Evolution Education Trust. Without your financial backing the experiments that led to the paradigm of clock.bio would not have been conducted.

A big thanks to Jason Whitmire and the BlueYard team, who are the most inspiring early-stage investors I know. Jason led clock.bio’s seed round, ignoring my advice to not invest. - This might just have been the best thing that has happened to clock.bio.

Thank you, Rick Klausner and Bob Nelsen, for your mentorship; the creation of Altos Laboratories has super-charged the rejuvenation field and will have profound impact on humanity.

Mark Kotter

9. References

1. Smigrodzki, R. M. & Khan, S. M. Mitochondrial Microheteroplasmy and a Theory of Aging and Age-Related Disease. https://home.liebertpub.com/rej 8, 172–198 (2005).

2. Chistiakov, D. A., Sobenin, I. A., Revin, V. V., Orekhov, A. N. & Bobryshev, Y. V. Mitochondrial Aging and Age-Related Dysfunction of Mitochondria. Biomed Res. Int. 2014, (2014).

3. Edgar, D. & Trifunovic, A. The mtDNA mutator mouse: Dissecting mitochondrial involvement in aging. Aging (Albany NY) 1, 1028 (2009).

4. Harman, D. Free radical theory of aging. Mutat. Res. 275, 257–266 (1992).

5. HARMAN, D. The Biologic Clock: The Mitochondria? J. Am. Geriatr. Soc. 20, 145–147 (1972).

6. Cui, H., Kong, Y. & Zhang, H. Oxidative Stress, Mitochondrial Dysfunction, and Aging. J. Signal Transduct.2012, 1–13 (2012).

7. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The Hallmarks of Aging. Cell 153, 1194–1217 (2013).

8. Jurk, D. et al. Chronic inflammation induces telomere dysfunction and accelerates ageing in mice. Nat. Commun. 2014 51 5, 1–14 (2014).

9. Brønnum-Hansen, H. Socially disparate trends in lifespan variation: a trend study on income and mortality based on nationwide Danish register data. BMJ Open 7, (2017).

10. Colman, R. J. et al. Caloric restriction delays disease onset and mortality in rhesus monkeys. Science 325, 201–204 (2009).

11. Mizushima, N., Levine, B., Cuervo, A. M. & Klionsky, D. J. Autophagy fights disease through cellular self-digestion. Nat. 2008 4517182 451, 1069–1075 (2008).

12. Lapasset, L. et al. Rejuvenating senescent and centenarian human cells by reprogramming through the pluripotent state. Genes Dev. 25, 2248–2253 (2011).

13. Ocampo, A. et al. In Vivo Amelioration of Age-Associated Hallmarks by Partial Reprogramming. Cell 167, 1719-1733.e12 (2016).

14. Lu, Y. et al. Reversal of ageing- and injury-induced vision loss by Tet-dependent epigenetic reprogramming. bioRxiv (2019). doi:10.1101/710210

15. Gladyshev, V. N. The Ground Zero of Organismal Life and Aging. (2021). doi:10.1016/j.molmed.2020.08.012

16. Maklakov, A. A. & Immler, S. The Expensive Germline and the Evolution of Ageing. Curr. Biol. 26, R577–R586 (2016).

17. Zaidi, A. A. et al. Bottleneck and selection in the germline and maternal age influence transmission of mitochondrial DNA in human pedigrees. Proc. Natl. Acad. Sci. U. S. A. 116, 25172–25178 (2019).

18. Boehm, A. M. et al. FoxO is a critical regulator of stem cell maintenance in immortal Hydra. Proc. Natl. Acad. Sci. U. S. A. 109, 19697–19702 (2012).

19. Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell49, 359–367 (2013).

20. Werfel, J., Ingber, D. E. & Bar-Yam, Y. Programed death is favored by natural selection in spatial systems. Phys. Rev. Lett. 114, 238103 (2015).

21. Li, Y. et al. A programmable fate decision landscape underlies single-cell aging in yeast. Science 369, 325–329 (2020).

22. Scaffidi, P. & Misteli, T. Lamin A-dependent misregulation of adult stem cells associated with accelerated ageing. Nat. Cell Biol. 10, 452–459 (2008).

23. Maierhofer, A. et al. Accelerated epigenetic aging in Werner syndrome. Aging (Albany. NY). 9, 1143–1152 (2017).

24. Zhang, W. et al. A Werner syndrome stem cell model unveils heterochromatin alterations as a driver of human aging. Science (80-. ). 348, 1160–1163 (2015).

25. Kang, E. et al. Age-Related Accumulation of Somatic Mitochondrial DNA Mutations in Adult-Derived Human iPSCs. Cell Stem Cell 18, 625–636 (2016).

26. Trifunovic, A. et al. Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nat. 2004 4296990 429, 417–423 (2004).

27. Singh, B., Schoeb, T. R., Bajpai, P., Slominski, A. & Singh, K. K. Reversing wrinkled skin and hair loss in mice by restoring mitochondrial function. Cell Death Dis. 2018 97 9, 1–14 (2018).

28. Vaiserman, A. & Krasnienkov, D. Telomere Length as a Marker of Biological Age: State-of-the-Art, Open Issues, and Future Perspectives. Front. Genet. 11, 630186 (2021).

29. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).

30. Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, 1–20 (2013).

31. Søraas, A. et al. Epigenetic age is a cell-intrinsic property in transplanted human hematopoietic cells. Aging Cell 18, (2019).

32. Kang, S., Moser, V. A., Svendsen, C. N. & Goodridge, H. S. Rejuvenating the blood and bone marrow to slow aging-associated cognitive decline and Alzheimer’s disease. Commun. Biol. 2020 31 3, 1–4 (2020).

33. Conese, M., Carbone, A., Beccia, E. & Angiolillo, A. The Fountain of Youth: A Tale of Parabiosis, Stem Cells, and Rejuvenation. Open Med. 12, 376 (2017).

34. Segel, M. et al. Niche stiffness underlies the ageing of central nervous system progenitor cells. Nature 573, 130–134 (2019).

35. Segel, M. et al. Author Correction: Niche stiffness underlies the ageing of central nervous system progenitor cells. Nature 573, E3 (2019).

36. Phillip, J. M., Aifuwa, I., Walston, J. & Wirtz, D. The Mechanobiology of Aging. Annu. Rev. Biomed. Eng. 17, 113–141 (2015).

37. Coppé, J. P. et al. Senescence-Associated Secretory Phenotypes Reveal Cell-Nonautonomous Functions of Oncogenic RAS and the p53 Tumor Suppressor. PLOS Biol. 6, e301 (2008).

38. Xu, M. et al. Transplanted Senescent Cells Induce an Osteoarthritis-Like Condition in Mice. J. Gerontol. A. Biol. Sci. Med. Sci. 72, 780–785 (2017).

39. Baker, D. J. et al. Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders. Nature479, 232–236 (2011).

40. Baker, D. J. et al. Naturally occurring p16(Ink4a)-positive cells shorten healthy lifespan. Nature 530, 184–189 (2016).

41. Bussian, T. J. et al. Clearance of senescent glial cells prevents tau-dependent pathology and cognitive decline. Nat. 2018 5627728 562, 578–582 (2018).

42. Waaijer, M. E. C. et al. The number of p16INK4a positive cells in human skin reflects biological age. Aging Cell11, 722 (2012).

43. Dufour, E. et al. Age-associated mosaic respiratory chain deficiency causes trans-neuronal degeneration. Hum. Mol. Genet. 17, 1418–1426 (2008).

44. Ruckh, J. M. et al. Rejuvenation of regeneration in the aging central nervous system. Cell Stem Cell 10, 96–103 (2012).

45. Horvath, S. et al. Reversing age: dual species measurement of epigenetic age with a single clock. bioRxiv2020.05.07.082917 (2020). doi:10.1101/2020.05.07.082917

46. Mehdipour, M. et al. Rejuvenation of three germ layers tissues by exchanging old blood plasma with saline-albumin. Aging (Albany NY) 12, 8790 (2020).

47. Schmauck-Medina, T. et al. New hallmarks of ageing: a 2022 Copenhagen ageing meeting summary. Aging (Albany. NY). 14, 6829–6839 (2022).

48. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: An expanding universe. Cell 186, 243–278 (2023).

49. Zhang, J. et al. A Human iPSC Model of Hutchinson Gilford Progeria Reveals Vascular Smooth Muscle and Mesenchymal Stem Cell Defects. Cell Stem Cell 8, 31–45 (2011).

50. Zhang, W. et al. Aging stem cells. A Werner syndrome stem cell model unveils heterochromatin alterations as a driver of human aging. Science 348, 1160–1163 (2015).

51. Whitton, H. et al. Changes at the nuclear lamina alter binding of pioneer factor Foxa2 in aged liver. Aging Cell17, e12742 (2018).

52. Noda, A. et al. Progerin, the protein responsible for the Hutchinson-Gilford progeria syndrome, increases the unrepaired DNA damages following exposure to ionizing radiation. Genes Environ. 37, 13 (2015).

53. McClintock, D. et al. The Mutant Form of Lamin A that Causes Hutchinson-Gilford Progeria Is a Biomarker of Cellular Aging in Human Skin. PLoS One 2, e1269 (2007).

54. Harrison, D. E. et al. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nat. 2009 4607253 460, 392–395 (2009).

55. Urfer, S. R. et al. A randomized controlled trial to establish effects of short-term rapamycin treatment in 24 middle-aged companion dogs. GeroScience 39, 117 (2017).

56. Cenni, V. et al. Autophagic degradation of farnesylated prelamin A as a therapeutic approach to lamin-linked progeria. Eur. J. Histochem. 55, 200–205 (2011).

57. Graziotto, J. J., Cao, K., Collins, F. S. & Krainc, D. Rapamycin activates autophagy in Hutchinson-Gilford progeria syndrome: implications for normal aging and age-dependent neurodegenerative disorders. Autophagy8, 147–151 (2012).

58. Horvath, S. et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging (Albany. NY). 10, 1758–1775 (2018).

59. Teschendorff, A. E. Epigenetic clocks galore: a new improved clock predicts age-acceleration in Hutchinson Gilford Progeria Syndrome patients. Aging (Albany NY) 10, 1799 (2018).

60. Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18, (2017).

61. Wang, M. & Lemos, B. Ribosomal DNA harbors an evolutionarily conserved clock of biological aging. Genome Res. 29, 325–333 (2019).

62. Horvath, S., Lu, A. T., Cohen, H. & Raj, K. Rapamycin retards epigenetic ageing of keratinocytes independently of its effects on replicative senescence, proliferation and differentiation. Aging (Albany NY) 11, 3238 (2019).

63. Lu, A. T. et al. DNA methylation-based estimator of telomere length. Aging (Albany. NY). 11, 5895–5923 (2019).

64. Lu, A. T. et al. Universal DNA methylation age across mammalian tissues. bioRxiv 100, 2021.01.18.426733 (2022).

65. Levine, M. E. et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 10, 573 (2018).

66. Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany. NY). 11, 303–327 (2019).

67. Fleischer, J. G. et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 19, 1–8 (2018).

68. Mamoshina, P. et al. Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification. Front. Genet. 9, 378508 (2018).

69. Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).

70. Lee, J. et al. Induced pluripotency and spontaneous reversal of cellular aging in supercentenarian donor cells. Biochem. Biophys. Res. Commun. 525, 563–569 (2020).

71. Liu, G. H. et al. Recapitulation of premature ageing with iPSCs from Hutchinson-Gilford progeria syndrome. Nature 472, 221–227 (2011).

72. Sarkar, T. J. et al. Transient non-integrative expression of nuclear reprogramming factors promotes multifaceted amelioration of aging in human cells. Nat. Commun. 11, 1545 (2020).

73. David Sinclair reddit. Available at: https://www.reddit.com/r/IAmA/comments/eorio3/hi_im_david_sinclair_professor_of_genetics_at/.

74. Olova, N., Simpson, D. J., Marioni, R. E. & Chandra, T. Partial reprogramming induces a steady decline in epigenetic age before loss of somatic identity. Aging Cell 18, (2019).

75. Gill, D. et al. Multi-omic rejuvenation of human cells by maturation phase transient reprogramming. bioRxiv2021.01.15.426786 (2021). doi:10.1101/2021.01.15.426786

76. Conboy, I. M. et al. Rejuvenation of aged progenitor cells by exposure to a young systemic environment. Nat. 2004 4337027 433, 760–764 (2005).

77. Villeda, S. A. et al. The ageing systemic milieu negatively regulates neurogenesis and cognitive function. Nat. 2011 4777362 477, 90–94 (2011).

78. Villeda, S. A. et al. Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice. Nat. Med. 2014 206 20, 659–663 (2014).

79. Loffredo, F. S. et al. Growth differentiation factor 11 is a circulating factor that reverses age-related cardiac hypertrophy. Cell 153, 828–839 (2013).

80. Rebo, J. et al. A single heterochronic blood exchange reveals rapid inhibition of multiple tissues by old blood. Nat. Commun. 2016 71 7, 1–11 (2016).

81. Horowitz, A. M. et al. Blood factors transfer beneficial effects of exercise on neurogenesis and cognition to the aged brain. Science (80-. ). 369, 167–173 (2020).

82. Schaum, N. et al. Aging hallmarks exhibit organ-specific temporal signatures. Nature 583, 596 (2020).

83. Lehallier, B. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat. Med. 2019 2512 25, 1843–1850 (2019).

84. Xu, M. et al. Senolytics improve physical function and increase lifespan in old age. Nat. Med. 2018 248 24, 1246–1256 (2018).

85. Yang, J.-H. et al. Erosion of the Epigenetic Landscape and Loss of Cellular Identity as a Cause of Aging in Mammals. bioRxiv 808642 (2019). doi:10.1101/808642

86. Almanzar, N. et al. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nat. 2020 5837817 583, 590–595 (2020).

87. Berkel, C. & Cacan, E. A collective analysis of lifespan-extending compounds in diverse model organisms, and of species whose lifespan can be extended the most by the application of compounds. Biogerontology 2021 226 22, 639–653 (2021).

88. Ando, S. et al. mTOR regulates T cell exhaustion and PD-1–targeted immunotherapy response during chronic viral infection. J. Clin. Invest. 133, (2023).

89. Chung, C. L. et al. Topical rapamycin reduces markers of senescence and aging in human skin: an exploratory, prospective, randomized trial. GeroScience 41, 861–869 (2019).

90. Kligman, A. M., Grove, G. L., Hirose, R. & Leyden, J. J. Topical tretinoin for photoaged skin. J. Am. Acad. Dermatol. 15, 836–859 (1986).

91. US4603146A - Methods for retarding the effects of aging of the skin - Google Patents. Available at: https://patents.google.com/patent/US4603146A/en.

92. CA2090104C - Stabilized retinoid-containing skin care compositions - Google Patents. Available at: https://patents.google.com/patent/CA2090104C/en?oq=CA2090104C.

93. Hore, T. A. et al. Retinol and ascorbate drive erasure of epigenetic memory and enhance reprogramming to naïve pluripotency by complementary mechanisms. Proc. Natl. Acad. Sci. U. S. A. 113, 12202–12207 (2016).

94. Mukherjee, S. et al. Retinoids in the treatment of skin aging: an overview of clinical efficacy and safety. Clin. Interv. Aging 1, 327 (2006).

95. Fahy, G. M. et al. Reversal of epigenetic aging and immunosenescent trends in humans. Aging Cell 18, (2019).

96. Kerepesi, C., Zhang, B., Lee, S. G., Trapp, A. & Gladyshev, V. N. Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging. Sci. Adv. 7, 6082–6107 (2021).

97. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 2017 14314, 297–301 (2017).

98. Adamson, B. et al. A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response. Cell 167, 1867-1882.e21 (2016).

99. Jaitin, D. A. et al. Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. Cell 167, 1883-1896.e15 (2016).

100. Wiley, C. D. et al. Analysis of individual cells identifies cell-to-cell variability following induction of cellular senescence. Aging Cell 16, 1043–1050 (2017).

101. Wang, F. et al. Robust measurement of telomere length in single cells. Proc. Natl. Acad. Sci. U. S. A. 110, (2013).

102. Picard, M. et al. Progressive increase in mtDNA 3243A>G heteroplasmy causes abrupt transcriptional reprogramming. Proc. Natl. Acad. Sci. 111, E4033–E4042 (2014).

[*] This observation forms part of the foundational IP of clock.bio. Our IP covers all possible read outs of rejuvenation biology in pluripotent stem cells. In addition, we are protecting all target genes that are involved in the repair processes that are able to reverse the ageing hallmarks.

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Mark Kotter

Clinician, scientist & entrepreneur transitioning biology to engineering for the benefit of patients.