# Telomerase Depletion Accelerates Ageing of the Zebrafish Brain

Raquel Martins, Savandara Besse, Pam Ellis, Rabia Sevil, Naomi Hartopp, Catherine Purse, Georgia Everett-Brown, Owain Evans, Nadiyah Mughal, Mina Wahib, et al.

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Raquel Martins, Savandara Besse, Pam Ellis, Rabia Sevil, Naomi Hartopp, et al., Telomerase Depletion Accelerates Ageing of the Zebrafish Brain. Aging Cell, 2025, 10.1111/acel.70280. hal-05335533

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RESEARCH ARTICLE

OPEN ACCESS

# Telomerase Depletion Accelerates Ageing of the Zebrafish Brain

Raquel R. Martins<fnref n="1" />,² ID | Savandara Besse³ ID | Pam S. Ellis<fnref n="1" /> ID | Rabia Sevil⁴ ID | Naomi Hartopp² ID | Catherine Purse<fnref n="1" /> |
Georgia Everett-Brown<fnref n="1" /> | Owain Evans<fnref n="1" /> ID | Nadiyah Mughal<fnref n="1" /> | Mina H. F. Wahib<fnref n="1" /> | Zerkif Yazigan<fnref n="1" /> | Samir Morsli<fnref n="1" /> |
Ada Jimenez-Gonzalez² ID | Andrew Grierson² ID | Heather Mortiboy² ID | Chrissy Hammond⁴ ID | Michael Rera³ ID |
Catarina M. Henriques<fnref n="1" /> ID

**Correspondence:** Catarina M. Henriques (c.m.henriques@sheffield.ac.uk)

**Received:** 5 December 2024 | **Revised:** 1 October 2025 | **Accepted:** 14 October 2025

**Funding:** This work was supported by Wellcome Trust, 206224/Z/17/Z/WT, The University of Sheffield Vice Chancellors Fellowship, Conseil National de la Recherche Scientifique, ANR ADAGIO, ANR-20-CE44-0010, Vivensa, AISRPG2305\32, Biotechnology and Biological Sciences Research Council, BB/ Y002504/1, SwbioDTP.

**Keywords:** ageing | behaviour | blood-brain barrier (BBB) | brain | inflammation | senescence | telomerase | TERT | transcriptomics | zebrafish

## ABSTRACT

Decreased telomerase expression, telomere shortening, senescence-associated markers, and inflammation have all been independently observed in the ageing brain and associated with disease. However, causality between limited telomerase expression and brain senescence and neuro-inflammation in the natural ageing setting is yet to be established. Here, we address these questions using the zebrafish as an ageing model. Akin to humans, zebrafish display premature ageing and death in the absence of telomerase and telomere shortening is a driver of cellular senescence. Our work shows for the first time that telomerase deficiency (tert⁻<fnref n="1" />⁻³) accelerates key hallmarks of ageing identified in the Wild Type (WT) zebrafish brain at transcriptional, cellular, tissue and functional levels. We show that telomerase depletion accelerates ageing-associated transcriptomic changes associated with dysregulation of stress response and immune genes. These are accompanied by accelerated in situ accumulation of senescence-associated markers and inflammation in the aged brain. Importantly, in vivo, these changes correlate with increased blood-brain barrier permeability and increased anxiety-like behaviour. Of note, the acceleration of senescence-associated markers in the absence of tert occurs not only in the expected proliferative areas but also in non-proliferative ones, where it is unlikely due to telomere-dependent replicative exhaustion. This suggests that non-canonical roles of telomerase may be involved.

**Abbreviations:** 2,6-DCPIP, 2,6-dichlorobenzenone-indophenol sodium salt; 4-MU-chitotrioside, 4-methylumbelliferyl-β-D-N, N', N''-triacetylchitotriose; AD, Alzheimers disease; ALS, amyotrophic lateral sclerosis; ANOVA, analysis of variance; ATH, ageing transcriptional hallmarks; BBB, blood-brain barrier; BCA, bicinchoninic acid; bp, base pair; BSA, bovine serum albumin; Ce, cerebellum; Cent, centromere; CNS, central nervous system; DAPI, 4',6-diamidino-2-phenylindole; DAVID, database for annotation, visualisation, visualisation, and integrated discovery; DDR, DNA damage response; DEG, differential expression gene; dH2O, distilled water; Die, diencephalon; DNA, deoxyribonucleic acid; Dpf, day post fertilisation; dscDNA, double-stranded cDNA; EDTA, ethylenediaminetetraacetic acid; ENU, N-ethyl-N-nitrosourea; FAM, carboxyfluorescein; FISH, fluorescence in situ hybridisation; GEO, gene expression omnibus; GOBP, gene ontology biological pathway; h, hours; HBSS, Hanks buffered solution; IF, immunofluorescence; IP, intraperitoneal injection; IR, irradiation; KCN, potassium cyanide; KEEG, Kyoto encyclopaedia of genes and genomes; KH₂PO₄, potassium dihydrogen phosphate; KO, knock-out; LCP1, Lplastin; LMF, Wolfson light microscopy facility; LPS, lipopolysaccharide; MgCl₂, magnesium chloride; Min, minutes; MO, medulla oblongata; NaCl, sodium chloride; NADH, nicotinamide adenine dinucleotide reduced disodium salt; NFkβ, nuclear factor kappa β; nm, nanometres/nanometers; NO, novel object; NPCs, neural progenitor cells; NSCs, neural stem cells; O.C.T, mounting media; OT, optic tectum; PBS, phosphate buffered saline; PCA, principal component analysis; PCNA, proliferating cell nuclear antigen; PFA, paraformaldehyde; PNA, peptide nucleic acid; RIN, RNA integrity number; RNA, ribonucleic acid; RPM, revolutions per minute; RT, room temperature; SASP, senescence-associated secretory phenotype; sec, seconds; SITraN, Sheffield Institute for Translational Neuroscience; SSC, saline-sodium citrate; TE, Tris-EDTA; Tel, telencephalon; Telo, telomere; Tert, telomerase; TERT, telomerase reverse transcriptase; TNFα, tumour necrosis factor alpha; WT, wild-type ZIRC, wild-type ZIRC, Zebrafish international resource center.

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Aging Cell* published by Anatomical Society and John Wiley & Sons Ltd.

<footnote n="1">¹School of Medicine and Population Health, Bateson Centre for Disease Mechanisms and Healthy Lifespan Institute (HELSI), University of Sheffield, Sheffield, UK | ²Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK | ³Institut Jacques Monod (UMR7592), Paris, France | ⁴School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK</footnote>
Together, our work shows that telomerase has a protective role in the zebrafish brain against the accumulation of senescence and neuro-inflammation and is required for blood-brain barrier integrity.

# 1 Introduction

Brain ageing and related neurodegenerative diseases have dramatic consequences on quality of life and medical care costs. Dementia, one of the most prevalent manifestations of neurodegenerative disease, affects 23 million people in the UK alone at a cost of around £26 billion a year to the NHS (Collins et al. 2022; Panca et al. 2019; Wittenberg et al. 2019). Understanding the mechanisms underlying brain ageing holds the promise of uncovering therapeutic targets for multiple age-associated brain diseases, aiming at ameliorating quality of life throughout the life course.

There are well-known hallmarks of ageing which include telomere attrition, cellular senescence, stem cell exhaustion and chronic inflammation, amongst others (Lemoine 2021; Lopez-Otin et al. 2023). Telomeres are (TTAGGG)n DNA repeats that, together with a complex of proteins (known as Shelterin), create a ‘cap-like’ structure at the end of linear chromosomes (de Lange 2004), preventing them from being recognised as deleterious DNA double strand breaks (Ferreira et al. 2004). However, in humans, due to time- and cell-specific limited telomerase expression, telomeres shorten with each cell division, eventually leading to proliferative exhaustion and replicative senescence (Bodnar 1998; d'Adda di Fagagna et al. 2003). Even though senescent cells stop proliferating, they are metabolically active, releasing several inflammatory, tissue remodelling, and growth factors, known as Senescence Associated Secretory Phenotype (SASP) (Coppe et al. 2010). Consequently, accumulation of senescent cells can contribute to impairment of tissue homeostasis, via decreased proliferation and chronic low level inflammation, and has been linked to several age-associated diseases (Ovadya and Krizhanovsky 2014; Tuttle et al. 2021). Importantly, cellular senescence accumulates with ageing in humans, and this can be thought of as a general stress response (Dimri et al. 1995; Birch and Gil 2020; Fridlyanskaya et al. 2015).

Restricted telomerase expression and consequent telomere dysfunction are key hallmarks of natural ageing in humans, underpinning multiple age-related diseases (Blackburn et al. 2015). As tissues with high cellular turnover present accelerated telomere erosion (Bodnar 1998; Lee et al. 1998), it is reasonable to think that telomerase functions are likely to primarily affect highly proliferative tissues. Accordingly, premature accumulation of critically short telomeres has been identified in highly proliferative tissues such as the gut, in tert-deficient animal models, including first generation zebrafish *tert*⁻/⁻ mutants (and late generation TERT KO mice) (Lee et al. 1998; Henriques et al. 2013; Anchelin et al. 2013; Carneiro et al. 2016). Nevertheless, because senescence has been shown to ‘spread’, in a phenomenon described as paracrine senescence, telomere-dependent genome instability and replicative senescence may lead to a non-cell autonomous increase in senescence-associated markers and inflammation (Lex et al. 2020). This means that the role of telomerase and telomeres is not restricted to highly proliferative tissues. To complicate things further, growing evidence suggests that the Tert component of telomerase also has non-canonical activities, independent of its action

at telomeres (Goodman and Jain 2011; Romaniuk et al. 2018; Segal-Bendirdjian and Geli 2019; Sung et al. 2014). In the nucleus, these non-canonical functions include transcriptional regulation of genes involved in inflammation, namely of nuclear factor kappa β (NFkβ) and tumour necrosis factor alpha (TNFα) (Deacon and Knox 2018; Ghosh et al. 2012; Mattiussi et al. 2012), as well as genes involved in cell proliferation (Choi et al. 2008; Sarin et al. 2005) and survival (Cao et al. 2002; Rahman et al. 2005). Telomerase can also translocate to the mitochondria, where it has been shown to play a protective role against DNA damage and oxidative stress (Ahmed et al. 2008; Haendeler et al. 2009). In the brain, considered a predominantly post-mitotic tissue, telomerase has been shown to have a protective role against excitotoxicity (Eitan et al. 2012), oxidative stress (Spilsbury et al. 2015) and neuronal death (Lee et al. 2010), all involved in neurodegenerative diseases. Studies in late-generation telomerase-deficient mice have suggested that limited telomerase expression is associated with premature accumulation of senescence-associated markers in different cell populations in the CNS, including Purkinje neurons, cortical neurons, and microglia (De Felice et al. 2014; Jaskelioff et al. 2011; Jurk et al. 2012; Raj et al. 2015). However, it is still unclear whether this is due to telomere length maintenance or non-canonical functions of telomerase (Choi et al. 2008; Sarin et al. 2005).

There are many different stimuli that can cause cellular senescence beyond telomere-dependent replicative senescence. These include epigenetic alterations, oxidative stress, mitochondrial dysfunction, inactivation of tumour suppressor genes, mechanical or shear stress, pathogens and activation of oncogenes (Hernandez-Segura et al. 2018; Tripathi et al. 2021). What causal role telomerase and telomere dysfunction play in CNS senescence in humans is, however, not clear, even if implicated (Yu et al. 2023). Telomere shortening has been associated with neurodegenerative diseases, including Parkinson's and AD (Papageorgakopoulou et al. 2024), including via glia activation and pathogenesis (Vellingiri et al. 2023). Of note, a recent study analysing human brain biobank data from over 30,000 patients identified a positive correlation between shorter leukocyte telomere length (used as a proxy for the patient's general telomere length) and neurodegenerative phenotypes (Topiwala et al. 2023). Additionally, cellular senescence-associated markers have been reported in human neuroinflammation and neurodegenerative diseases. Increased levels of DNA damage checkpoints such as p53, p21 and p16, as well as SASP factors, have been observed in the spinal cord and brain of patients with amyotrophic lateral sclerosis (ALS) (De Felice et al. 2014) and Alzheimer's disease (AD) (Bhat et al. 2012), respectively. Higher mRNA levels of p16INK4a in the substantia nigra pars compacta (SNpc) were also observed in post-mortem tissue of patients with Parkinson's disease (PD) compared to healthy controls (Chinta et al. 2018). Moreover, senescence, neuroinflammation and oligomeric tau have been associated with AD pathology, recently reviewed here (Gaikwad et al. 2024). Hence, growing evidence suggests that, despite being a low proliferative tissue, the CNS accumulates senescence or at least senescence-associated markers, and that this may be associated with brain diseases
of ageing. However, causality between limited telomerase expression and increased senescence and neuro-inflammation in the natural ageing setting is yet to be established. Nevertheless, reducing putative senescent cells leads to improvement of cognitive decline in aged mice, suggesting an active role for senescence in this phenotype (Bussian et al. 2018). It remains to be determined which cell populations undergo senescence in natural ageing, in the CNS, and what type of senescence this is: replicative (telomere-dependent) or stress-induced senescence. Understanding whether and how telomerase restriction may contribute to ageing-associated brain degenerative phenotypes would help understand the fundamental mechanisms of brain functional decline and potentially contribute to the uncovering of new therapeutic targets to delay or prevent disease.

The role of telomerase restriction and its consequences in natural ageing may benefit from additional and complementary models, beyond the mouse, where most lab strains have significantly longer telomeres than humans and show little evidence of replicative senescence (Forsyth et al. 2002; Gomes et al. 2011). The zebrafish may offer such a model since, like in humans, telomerase depletion leads to a significant decrease in health and lifespan (Henriques et al. 2013; Anchelin et al. 2013; Carneiro et al. 2016; Henriques and Ferreira 2012). As it was shown that the zebrafish brain presents low levels of *tert* in the adult brain and that *tert* expression decreases further with ageing (Sprungala, n.d.; Zambusi et al. 2020), we hypothesised that limited expression of *Tert* in the aged zebrafish brain contributes to brain degeneration with ageing. To our knowledge, this manuscript represents the first broad characterisation of zebrafish brain ageing in the presence and absence of telomerase (Tert).

To test our hypothesis, we performed RNA sequencing in whole brains of zebrafish throughout their life course in both the presence and absence of telomerase (tert). Specifically, we compared WT and *tert*⁻/⁻ siblings derived from *tert*⁺/⁻ in-crosses throughout their life course *tert*⁻/⁻ zebrafish, extensively characterised elsewhere (Henriques et al. 2013; Anchelin et al. 2013; Carneiro et al. 2016), display no telomerase activity and have significantly shorter telomeres from birth, consequently ageing and dying prematurely. Ageing is usually described as a time-dependent change in tissue homeostasis that increases the probability of disease and death (Hayflick 2007). However, whether the genes and pathways driving or accompanying these time-dependent changes are also consistently changing in a time-specific manner remains unresolved (Rando and Wyss-Coray 2021). We therefore decided to combine a time-series analysis (STEM), which allowed the identification of genes and pathways that are consistently up or down-regulated over time, with the more traditional differential gene expression (DEGs) analysis between young and old animals. This combined strategy allowed the identification of genes that change in a monotonic, time-dependent manner (STEM), versus genes that change at specific stages of life (DEGs). Both transcriptomic analyses, combined with enrichment strategies, highlighted increased immune response and decreased proliferation as the main signatures of ageing in both WT and *tert*⁻/⁻ zebrafish brains. We then asked which specific WT ageing-associated gene changes (STEM and standard DEG comparisons between time points) were accelerated, that occurred at an earlier age, in the absence of telomerase (i.e., *tert*). The more refined STEM-based analyses identified 6

specific gene changes accelerated from the age of 9–16 months in *tert*⁻/⁻ and 11 from the age of 18–24, contrasting to 30–36 months in WT. Broader DEG comparisons-based analyses were used to identify DEGs of ‘old age’ (WT young vs. old) and then compared with *tert*⁻/⁻ at the different ages to determine how telomerase depletion may accelerate transcriptomic changes of ‘old age’. This approach identified 4 DEGs accelerated from the early age of 2–6 months in *tert*⁻/⁻; 639 from the age of 9–16; and 109 from the age of 18–24. These transcriptomic changes were accompanied by an in situ acceleration of senescence-associated markers and inflammation. In vivo, telomerase depletion accelerated ageing-associated increases in blood–brain barrier permeability and increased anxiety-like behaviour. Together, our work shows that telomerase has a protective role in the zebrafish brain against the accumulation of senescence and neuro-inflammation and is required for BBB integrity.

## 2 | Results

### 2.1 | Telomerase (Tert) Depletion Accelerates Transcriptomic Changes of Ageing in the Zebrafish Brain

Tissue-specific transcriptomic analysis over the life course can offer important insights into the downstream molecular mechanisms underpinning age-associated pathologies. Significant research is being dedicated to these approaches in different animal models, including in mice (Schaum et al. 2020; Tabula Muris, C 2020; Zhang et al. 2021). To identify telomerase-dependent and -independent transcriptional signatures of ageing in the zebrafish brain, we performed RNA-Sequencing of whole brain tissue throughout the lifespan of WT and telomerase-deficient (*tert*⁻/⁻) fish (Figure 1 and supplementary source data files). While *tert*⁻/⁻ fish have a lifespan of c. 18–24 months, WT fish typically die between c. 36–42 months of age (Carneiro et al. 2016). The data shown here include 4 age groups of WT (2–6, 9–16, 18–24, and 30–36 months), corresponding to young, adult, median lifespan, and old, respectively. As the *tert*⁻/⁻ fish have a shorter lifespan compared with their WT siblings, the data include three age groups of telomerase-deficient fish (2–6, 9–16, and 18–24 months), which correspond to young, medium lifespan, and old (see Materials & Methods for further explanation of genotypes and ages chosen) (Figure 1A). The reads were aligned to the latest zebrafish genome build GRCz11 (Lawson et al. 2020) and resulted in uniquely mapped read percentages ranging from 92.1% to 94.4%, which is considered good quality (Lawson et al. 2020). All samples had at least 10 million uniquely mapped reads. To analyse the overall impact of the genotype and age on transcriptomic regulation, we performed a Principal Component Analysis (PCA) and observed that the samples cluster mainly *per* age (Figure S1A).

We performed a time-series analysis (STEM) (Figure 1 and supplementary source (SS) data), which allowed the identification of genes and pathways that are consistently up or down-regulated over time. In parallel, we performed a broader, more traditional differential gene expression (DEGs) analysis at the different time points, in WT and *tert*⁻/⁻ (Figure S1 and Data S1). For the time-series analysis, we grouped the gene expression data into temporal expression profiles using Short Time-series Expression
<figure><img src="image_3.png" /><figcaption>B) STEM profiles</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>B2) STEM profile hypergeometric enrichment analysis</figcaption></figure>

-log10(Adjusted P-value)

<figure><img src="image_5.png" /><figcaption>D) Which WT STEM profile DEGs are accelerated in the absence of telomerase (tert⁻⁰?)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**FIGURE 1** | Legend on next page.</figcaption></figure>
**FIGURE 1** | Transcriptomic signatures of ageing in the presence and absence of telomerase in the zebrafish brain. (A) Schematic figure of the study design for transcriptomics: RNA from whole brain tissue from young (2–6 months), young adult (9–16 months), middle aged (18–24 months) and old, (30–36 months) WT fish, and young (2–6 months), middle aged (9–16 months) and old (18–24 months) *tert*−/− fish was used for RNA sequencing. *N* = 3 per group. Transcriptomic alterations of ageing were analysed using two complementary approaches: STEM profiles and traditional DEG analysis between genotypes at different ages. Enrichment analysis and hypergeometric analysis were performed to identify the predominant pathways altered with ageing in the presence and absence of telomerase, and the DEGs were mapped to the ageing transcriptomic hallmarks (ATH). (B) Plots of the profiles identified by the STEM analysis (profiles containing down-regulated genes in blue and profiles containing up-regulated genes in orange) in both WT and *tert*−/− zebrafish brains, and (B1) top enrichment pathways (GOBP) identified in representative profiles. (B2) STEM profile hypergeometric enrichment analysis in WT and *tert*−/− brains identified which cell populations are mainly affected with ageing in the presence and absence of telomerase. (C) The genes identified by STEM analysis were then mapped to ATH. Plots show the proportion of genes mapped to each ATH, in both WT and *tert*−/− fish. (D) Venn diagrams highlight the genes identified in the STEM profiles of WT fish that are accelerated in the absence of telomerase (i.e., in *tert*−/− fish).

Miner (STEM) software (Ernst and Bar-Joseph 2006). To determine whether the temporal profiles were associated with specific biological processes and pathways, pathway over-representation analysis (ORA) was performed for the genes assigned to the significant STEM profiles. In the WT brain, we identified 6 temporal profiles, 3 of them including overall up-regulated genes (profiles 40, 18, and 29), and 3 including overall down-regulated genes (profiles 1, 26 and 15). In the *tert*−/− brain, time-series analysis revealed 5 different profiles. Profiles 8 and 15 contained overall up-regulated genes and profiles 0, 7 and 3 contained overall down-regulated genes (Figure 1B and Data S1). We then performed two types of enrichment analysis strategies: Gene Ontology Biological Pathway (GOBP) and hypergeometric. GOBP analysis of STEM profiles highlighted up-regulation of immune response and down-regulation of cell-cycle and related pathways with ageing in both WT and *tert*−/− zebrafish brains (Figure 1B1). The hypergeometric enrichment analysis allowed us to further investigate the cellular context of age-associated transcriptional changes. To do this, we mapped the gene sets from significant STEM profiles in WT and *tert*−/− mutant brains onto a published zebrafish brain single-cell dataset (Anneser et al. 2024). This dataset was chosen because it provides high-quality profiles (> 16,000 cells at ~8000 UMIs per cell), detailed annotations of unsorted cells capturing the full cell repertoire, and accessible raw and processed data. In WT brains, the major down-regulated trajectory (STEM profile 1) was most strongly enriched for genes expressed in cycling cells, with lesser enrichment in neural progenitor cells (NPCs) and oligodendrocytes, indicating a decline in proliferative and myelinating populations with age. Similarly, in *tert*−/− mutants, multiple down-regulated profiles (0, 3 and 7) showed enrichment for cycling cells, NPCs, oligodendrocytes, and additionally in differentiating oligodendrocyte progenitor cells (OPCs). In contrast, upward trajectories in both WTs (profiles 18, 29 and 40) and *tert*−/− mutants (profiles 8 and 15) were enriched for leukocytes and microglia, consistent with increased immune activity during ageing (Figure 1B2). These enrichment analyses were further supported when we contextualized pathway enrichments within the hallmarks of ageing, using a previously described strategy (Zane et al. 2023) (see additional details in Materials and Methods). Here, we mapped known GOBP terms to experimentally identified ageing transcriptomic hallmarks (ATH) (Frenk and Houseley 2018). ATH mapping identified dysregulation of immune genes (ATH1) in up-regulated STEM profiles and stress response (ATH2) in down-regulated STEM profiles in both WT and *tert*−/− zebrafish (Figure 1C and Data S1). The broader analysis of 'DEGs of

old age' (WT young vs. old) showed a GOBP enrichment for cell cycle, division, chromatin organization, and chromosome segregation, and down-regulation of DNA transcription as the top 5 enriched pathways. Notably, when mapped to ATH, this broader analysis identified dysregulation of gene expression, stress response, and dysregulation of immune genes as the top 3 hallmarks of old age (Frenk and Houseley 2018) (Figure S1 and Data S1), supporting the STEM analysis. We further validated selected DEGs of old age by RT-QPCR (*ctss2.1* and *parp3*) and/or in situ hybridization (*cdkn2a/b* 'p16-like' and *cdkn1a* 'p21-like') (Figure 2B3 and Figure S4).

Finally, we asked which specific WT ageing-associated gene changes from the STEM and standard DEG comparisons between time points were accelerated in the absence of telomerase (*tert*−/−), that is, occurred at an earlier age than WT. The more refined STEM-based analyses identified 6 specific gene changes accelerated from the age of 9–16 months in *tert*−/− and a further 11 from the age of 18–24 months, contrasting to old WT (30–36 months). These included down-regulation of SI:DKEY-225F5.4 (an ortholog to ZWINT human gene) and ZGC:101100 (aka dmbx2 in zebrafish- diencephalon/mesencephalon homeobox 2) and upregulation of *klf9* (Kruppel like factor 9), *lyn* (LYN proto-oncogene, Src family tyrosine kinase), *mhcluka* (major histocompatibility complex class I UKA), and *mxc* (myxovirus (influenza virus) resistance C) accelerated from the ages of 9–16 months in *tert*−/−. We identified a further 11 from the age of 18–24, contrasting to 30–36 months in WT (Figure 1C and Data S1). We performed a similar analysis using the broader 'DEGs of old age' (Figure S1C and Data S1). To do this, we compared DEGs identified in *tert*−/− zebrafish brains over time (i.e., 2–6 vs. 9–16 months and 2–6 vs. 18–24 months) with the WT 'DEGs of old age'. We then selected only the ones that changed in *tert*−/− in the same direction as WT, that is, either up or down-regulated, and further compared these with the corresponding age in sibling WT DEGs, to check whether they were accelerated in *tert*−/−, i.e., changed at younger ages than the equivalent sibling WT. Using this strategy, we identified 1049 DEGs of old age accelerated in the absence of telomerase (*tert*−/−), which comprised c.36% of the total (2887) 'DEGs of old age' (Figure S2 and Data S1). Within these, 4 'DEGs of old age' were accelerated from the early age of 2–6 months in *tert*−/−; 639 from the age of 9–16 and 109 from the age of 18–24, contrasting with the age of 30–36 in WT. Notably, when mapped to the ageing transcriptomic hallmarks of ageing, the 'DEGs of old age' accelerated in the absence of telomerase identified dysregulation of gene
expression, stress response, and dysregulation of immune genes
as the top 3 significantly enriched transcriptional hallmarks
(Frenk and Houseley 2018), similarly to the overall DEGs of WT
old, and to similar extents (Figure S3 and Data S1).

## 2.2 | Telomerase (Tert) Depletion Accelerates Ageing-Associated Stress Response in the Zebrafish Brain

Given our transcriptomic analysis results in 2.1, we proceeded
to testing whether these changes were reflected at the cell and
tissue level, starting with markers associated with the ‘stress
response’ transcriptomic hallmark of ageing: cell cycle (also
highlighted in both the GOBP and the hypergeometric analysis);
senescence-associated markers; DNA damage response and rel-
ative telomere length, in brain sections of WT and *tert*⁻/⁻ zebraf-
ish over their life course.

There is not a single marker that can be used on its own to ir-
refutably identify a senescent cell, and markers often described
to be associated with senescence in proliferative tissues, such as
P16 and P21, are highly expressed in terminally differentiated
neurons (Huang et al. 2022; Vazquez-Villasenor et al. 2020).
This may confound the identification of putative senescent
cells. Nevertheless, markers such as P16 have previously been
identified as a good marker for predicting disease progression
in Parkinson's Disease (PD) patients (Martin-Ruiz et al. 2020)
and with ageing (Idda et al. 2020). Acknowledging these limita-
tions, we performed a broad characterisation of putative cellular
senescence as a stress response using the following five main
readouts (Hernandez-Segura et al. 2018) (Figure 2B): (1) accu-
mulation of Senescence-Associated β Galactosidase (SA-β-Gal),
a known biomarker of cellular senescence, at least in most of the
cell types tested (Dimri et al. 1995), and often present in differ-
ent types of senescence (Figure 2B1–B3); (2) levels of expression
of cell cycle kinase inhibitors *p21* and *p16* genes (Figure 2B–2,4
and Figure S4B); (3) proliferation, by Proliferating Cell Nuclear
Antigen (PCNA) expression and EdU pulse chase labelling
(Figure 2B2,B5,C); (4) DNA Damage Response (DDR) by iden-
tifying cells with strong signal reflecting multiple γH2AX
foci (Figure S5); and (5) relative telomere length (Figure 2D
Figure S5B).

Our initial whole brain SA-β-Gal staining in WT zebrafish
brains detected more intense blue staining in WT old (30–
36 months), when compared to young (2–6 months), which
motivated us to explore further (Figure 2A). To do this, we
dissected and sectioned a separate set of brains to determine
which regions had increased SA-β-Gal staining, comparing
WT and *tert*⁻/⁻ siblings at different ages. Our results showed
a significant increase in SA-β-Gal with ageing in specific
regions of the WT zebrafish brain, such as in the midline of
the telencephalon, diencephalon, cerebellum and medulla
oblongata (Figure 2B-1). Accumulation of SA-β-Gal in the
diencephalon, cerebellum and medulla oblongata was sig-
nificantly accelerated in the absence of telomerase (*tert*⁻/⁻),
where, at 2-6 months, the *tert*⁻/⁻ brain is already displaying
the pattern and levels of SA-β-Gal observed in old WT brains
of 30–36 months of age (diencephalon quantification shown as
a representative example in Figure 2B–2).

We then collected 2 further sets of zebrafish WT and *tert*⁻/⁻
sibling brains to assess (a) *cdkn2a/b* ‘*p16-like*’ (RNA in situ),
PCNA (IF), and SA-β-Gal (chromogenic) (Figure 2) and (b)
*cdkn1a* ‘*p21-like*’ and *cdkn2a/b* ‘*p16-like*’ genes (RNA in situ)
(alongside a repeat of chromogenic SA-β-Gal as control)
(Figure S4) in sequential sections of the respective animals.
Our data show an increase in *cdkn2a/b* ‘*p16-like*’ expression
levels with ageing in WT and *tert*⁻/⁻, this is not accelerated
by the absence of telomerase, either at 2–6 or 9–16 months
(Figure 2B,B4). Notably, the increase in *cdkn2a/b* ‘*p16-like*’
expression was not accompanied by *cdkn1a* ‘*p21-like*’ expres-
sion (Figure S4B). We detected very little proliferation in the
zebrafish brain—generally under 5% of PCNA⁺ cells per FOV
in any region we looked (Figure S5A). Even after a 3-day pulse
with EdU, the % of EdU⁺ cells in the diencephalon/Optic tec-
tum, where known proliferative niches reside, was under 1%/
FOV (Figure 2C). Nevertheless, both PCNA and EdU data
show a significant decrease in proliferating cells with age-
ing in both WT and *tert*⁻/⁻ in the diencephalon/optic tectum
(PCNA and EdU), but this is not significantly accelerated in
the absence of telomerase at the ages tested.

Another common marker of senescent cells is the accumulation
of DNA damage response (DDR) markers, such as γH2AX foci.
Our data show that very few cells accumulate γH2AX foci in
the aged brain, in all regions tested (never more than 2% of cells
(Figure S5A)). Nevertheless, we detected a significant increase
in γH2ax⁺ cells with ageing in both WT and *tert*⁻/⁻ in the cer-
ebellum (Figure S5A2) and when we combine diencephalon
with optic tectum regions (Figure 2B6). Notably, accumula-
tion of DDR with ageing is accelerated in the absence of telo-
merase in these brain regions from the age of 9–16 months in
*tert*⁻/⁻ zebrafish.

Of note, the senescence-associated markers tested (increase
in SA-β-Gal, *cdkn2a/b* ‘*p16-like*’ and γH2AX⁺ cells) occur
in both proliferative and non-proliferative areas of the brain
(Figure 2B3) (see example of regions 1 and 2 in a young WT
brain (Figure 2B3.1)).

To test whether increase in senescence-associated markers cor-
related with shorter telomeres (i.e., putative replicative senes-
cence), we measured relative telomere length in brain sections
of WT fish over time, alongside their *tert*⁻/⁻ counterparts as pre-
viously described by us, using a telomeric Peptide Nucleic Acid
(PNA) probe alongside a centromeric PNA probe in Fluorescence
in situ Hybridisation (FISH) (Ellis et al. 2022) (Figure 2D and
Figure S5B). We show that relative telomere length decreases
with ageing in WT brains and this is accelerated in the ab-
sence of telomerase, where telomeres are significantly shorter
in *tert*⁻/⁻ from the young age of 2–6 months, as has been shown
for multiple tissues (Carneiro et al. 2016). However, as expected,
relative telomere length in the brain is significantly longer than
in the highly proliferative gut epithelia of the same individuals,
both in WT and *tert*⁻/⁻ at the ages tested.

Different types of senescence stimuli have been identified to
express different SASP factors. We took advantage of available
public databases of genes changes associated with SASP fac-
tors in humans and mice (SASP atlas and Senmayo databases)
(Saul et al. 2022; Basisty et al. 2020) and compared them with
<figure><img src="image_7.png" /><figcaption>A) SA-β-Gal whole brain staining</figcaption></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>B3) OT Die Diencephalon/ Optic tectum examples</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>B) Whole body sagital or brain transverse sections</figcaption></figure>

SA-β-Gal
cdkn2a/b, “p16-like”
PCNA
γh2ax
TELO/ CENT-FISH

<figure><img src="image_11.png" /><figcaption>C) EdU pulse chase experiment</figcaption></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

**FIGURE 2** | Legend on next page.
**FIGURE 2** | Telomerase (tert) depletion accelerates ageing-associated stress response in the zebrafish brain. Brain tissue collected from 2–6 months, 9–16 months and 30–36 months WT fish, and 2–6 months and 9–16 months *tert*⁻/⁻ fish were used to assess stress response by SA-β-Gal staining, *cdkn2a/b* ('p16-like') RNA expression, proliferation (PCNA and EDU staining), telomere length and DNA damage. (A) Whole brain SA-β-Gal staining in young and old WT fish and respective quantification show an increased expression of SA-β-Gal in old WT brains compared to young ones. *N* = 5–6 per group. (B) Schematic figure showing the regions of the brain assessed for senescence-associated markers. (B1) Representative images of SA-β-Gal staining in coronal cryosections of WT and *tert*⁻/⁻ zebrafish show that there is an increased expression of SA-β-Gal in old WT zebrafish (30–36 months) and that a similar pattern is observed in *tert*⁻/⁻ at the young age of 2–6 months, particularly in the diencephalon and optic tectum for which (B2) quantifications are shown. *N* = 8–14 per group. (B3) Representative images from in situ hybridisation in coronal cryosections, show an increased expression of *cdkn2a/b* ('p16-like') in WT zebrafish > 30 months optic tectum and diencephalon, in areas where SA-β-Gal expression is observed. This increase in *p16* expression is already significant at 9–16 months of age in *tert*⁻/⁻. Quantifications shown in B4. *N* = 3–5 per group. (B3) Representative images of PCNA and γH2AX staining in adjacent sections from zebrafish brain, in coronal cryosections. (B5) Proliferation assessed by PCNA staining show a decrease in proliferation with ageing in both WT and *tert*⁻/⁻ fish but this is not accelerated in the absence of telomerase. *N* = 3–7 per group. (B6) DNA damage response assessed by γH2AX staining show increased DNA damage in WT fish 30–36 months, this increase is accelerated in the *tert*⁻/⁻ fish from 9–16 months age. *N* = 3–7 per group. (C) Similar to what was observed by PCNA staining, proliferation assessment by EdU staining after a 3-day pulse experiment show a decrease in proliferation with ageing in both WT and *tert*⁻/⁻ brains, but this is not accelerated in the absence of telomerase. *N* = 3–7 per group. (D) Relative telomere length, assessed by telo/cent-FISH, in longitudinal paraffin sections of zebrafish brain (white) and gut (yellow), show that telomere length decreases with ageing in WT fish (30–36 months) and that this is accelerated in young *tert*⁻/⁻ fish (2–6 months). *N* = 4–5 per group. (A, B2, B4, B5, B6) Each dot represents one animal. (D) Each dot represents one cell. Error bars: SEM. \*p < 0.05; \*\*p < 0.01; \*\*\*p < 0.001. Abbreviations: OB, olfactive bulb; Tel, telencephalon; Die, diencephalon; OT, optic tectum; Ce, cerebellum; MO, medulla oblongata.

our transcriptomic STEM and broad 'DEGs of old age'. We were particularly interested in determining whether any of these putative SASP-associated genes were accelerated in the absence of telomerase, at the ages where we see acceleration of other senescence-associated markers. The STEM-based SASP analysis identified 2 genes in common between WT and *tert*⁻/⁻ profiles (*tyms* and *psm2*) (Figure S6A), but none of these were accelerated in the absence of *tert* (Figure 1D).

However, when we performed the broader 'DEGs of old age' accelerated in the absence of telomerase, we identified a further 52 genes, including *psm2* (accelerated in the absence of telomerase from the age of 9–16 months), which was also detected in the STEM profiles (Figure S6B). Of note, *idh1*, *lancl1*, *masp1*, *ncl* and *tuba1a* are accelerated in the absence of telomerase from the age of 18–24 months, and the remaining ones from 18–24 months in *tert*⁻/⁻ (Data S1).

## 2.3 | Telomerase (Tert) Depletion Accelerates Ageing-Associated Inflammation in the Zebrafish Brain

We then proceeded to testing whether the changes associated with the 'dysregulation of immune response' ageing transcriptomic hallmark—also highlighted in both the GOBP and hypergeometric analysis—were reflected at the cell and tissue level. We started by quantifying the percentage of immune cells in brain sections of WT and *tert*⁻/⁻ zebrafish over their life course.

Because there is no agreed specific microglia marker in zebrafish, we combined the *tg(mpeg1.1-mCherry-caax)* transgenic zebrafish line (Ellett et al. 2011), which has been shown to identify macrophages and microglia in the brain (Oosterhof et al. 2017), with a well-described pan-immune cell marker (L-plastin or LCP-1) (Cvejic et al. 2008; Mathias et al. 2010; Redd et al. 2006; Van Houcke et al. 2017) and used immunofluorescence (IF) to quantify immune cell populations in the brain (Martins et al. 2019) as a readout for inflammation. Using these

two markers allowed us to identify macrophages/microglia as L-plastin⁺*mpeg*⁺ cells, and distinguish them from any other immune cells, which would be L-plastin⁺*mpeg*⁻ (Figure 3A). Our data show that there is an increased number of plastin⁺*mpeg*⁺ cells (Figure 3A1) but not of L-plastin⁺*mpeg*⁻ cells (Figure 3A2) with ageing, in most of the zebrafish brain (WT 2–6 months vs. WT 30–36 months) (Figure S7A), suggesting a macrophage/microglia specific increase. The macrophage/microglia increase in the brain with ageing (WT 2–6 months vs. WT 30–36 months) is suggestive of neuroinflammation and was accelerated in the absence of telomerase from the early age of 2–6 months (WT 2–6 months vs. *tert*⁻/⁻ 2–6 months), at which ages we do not see any significant increase in immune cell proliferation (Figure S7B).

To understand whether macrophage/microglia increase with ageing was associated with immune activation, we used a chitotriosidase activity assay. Chitotriosidase is an enzyme expressed by activated macrophages and other phagocytic cells and can therefore be used as a biomarker of microglia/macrophage activation, including in zebrafish (Keatinge et al. 2015). Our data show that there are increased levels of chitotriosidase in the naturally aged brain (WT 2–6 vs. 9–16 vs. 30–36 months), suggesting age-associated neuronal immune activation, which we show is accelerated in the absence of telomerase (*tert*⁻/⁻) from the age of 9–16 months (Figure 3B).

Accumulation of cellular senescence is thought to contribute to chronic inflammation in ageing, often termed 'inflammageing' (Deleidi et al. 2015; Franceschi and Campisi 2014; Figueira et al. 2016). On one hand, in some contexts, immune cells have been shown to be recruited to sites of cellular senescence (Demaria et al. 2014; Krizhanovsky et al. 2008). On the other hand, senescent cells have been shown to release pro-inflammatory factors (part of SASP), which can contribute to paracrine senescence (Acosta et al. 2013) as well as to tissue inflammation (Franceschi and Campisi 2014). We therefore set out to determine how the increased levels of senescence-associated markers we detected in the zebrafish
<figure><img src="image_18.png" /><figcaption>B) Chitotriosidase activity assay (marker for activated macrophages/microglia in inflammatory diseases)</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>C) Correlation between senescence and immune cells</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>**FIGURE 3** | Legend on next page.</figcaption></figure>
**FIGURE 3** | Telomerase depletion is associated with increased inflammation in the aged zebrafish brain. (A) Representative images of mpeg-mcherry and L-plastin staining in the diencephalon of adult zebrafish (schematic figure highlights the region imaged as diencephalon). The yellow arrows highlight Lplastin⁺; mpeg⁻ cells. (A1) Quantifications of L-plastin-positive; mpeg-positive cells (orange; from red and green co-localisation) and (A2) L-plastin-positive; mpeg-negative cells (red) in the whole zebrafish brain show an increased number of macrophages/microglia (L-plastin-positive; mpeg-positive cells) with natural ageing, and this is accelerated in the absence of telomerase (in *tert*⁻/⁻ at 2–6 months of age). However, we observed no differences in the number of T/B cells and neutrophils (L-plastin-positive; mpeg-negative cells) with ageing, in the WT or *tert*⁻/⁻ fish. *N* = −6 per group. (B) Schematic figure of the chitotriosidase assay (left). Quantifications (right) show that chitotriosidase activity increases with natural ageing at > 30 months of age, and that this is accelerated in the *tert*⁻/⁻ at the age of 9–16 months. (C) Representative images of co-staining with SA-β-Gal (blue) and L-plastin (white) in brain sections show that most of the L-plastin-positive cells do not co-localise with the SA-β-Gal staining (left). Staining quantification shows a significant correlation between increased number of immune cells and increased expression of SA-β-Gal with ageing in both WT and *tert*⁻/⁻ (right). (A1, A2, B) Each dot represents one animal. (A1, A2, B, C) Bar errors represent the SEM. \* *p* < 0.05; \*\* \* *p* < 0.01.

brain correlated with immune cell numbers. To do this, we
performed IF staining with the pan-immune cell marker L-
plastin on SA-β-Gal-stained brain tissue sections. Even
though we cannot exclude that some of these immune cells
may also be SA-β-Gal⁺ themselves, most of the immune cells
appear to surround, rather than overlap with SA-β-Gal⁺
areas (Figure 3C) and there is a statistically significant pos-
itive correlation between % of SA-β-Gal⁺ areas and % of im-
mune cells with ageing in the WT zebrafish brain (R² = 0.99),
*p*(two-tailed) = 0.0313.

**2.4** | Telomerase (Tert) Depletion Accelerates Ageing-Associated Blood-Brain Barrier Dysfunction

Because our data show that telomerase (tert) depletion accelerates ageing-associated neuroinflammation (Figure 3), not explained by increased immune proliferation (Figure S7B), we

hypothesised that there could be immune infiltration from the
periphery. Immune cells should not cross the blood-brain bar-
rier (BBB) and invade the brain in a healthy condition unless
the BBB is compromised (reviewed in (Muldoon et al. 2013)).
It has been described that zebrafish have a functional BBB
from the 10th day post fertilisation (dpf) (Fleming et al. 2013),
after which molecules such as horseradish peroxidase (HRP,
44,000 Da), evans blue (961 Da), and sodium fluorescein
(376 Da) do not cross this barrier (Fleming et al. 2013; Jeong
et al. 2008).

To test whether zebrafish BBB becomes leaky with ageing and whether telomerase plays a role in it, we set up a BBB permeability assay in WT and *tert*⁻/⁻ siblings at similar ages where increased immune activation (chitotriosidase activity) was identified in the brain. To do this, we performed intraperitoneal injections (IP) of an infrared fluorescent dye IRDye 680RD Dextran diluted in saline (Hanks buffered solution-HBSS). We

A) Blood Brain Barrier *in vivo* permeability assay

4Kda molecules should not cross the Blood Brain Barrier

<figure><img src="image_21.png" /></figure>

**FIGURE 4** | Telomerase depletion is associated with increased BBB permeability with ageing in the zebrafish brain. (A) Schematic figure of the BBB permeability assay. Dextran-FITC 4 kDa or vehicle control (HBSS) were injected by intraperitoneal injection. 3 h later the brains were dissected and imaged in the Odyssey CLx (Li-cor) to confirm the presence of Dextran-FITC. Quantification of the green fluorescence in the fish brains shows higher FITC expression in old WT (> 30 months) compared to middle aged ones (9–16 months). This increased presence of dextran-FITC in *tert*⁻/⁻ is already observed at 9–16 months, overall suggesting that there is increased brain permeability with natural ageing and that this is anticipated in the absence of telomerase. *N* = 9–20 per group in the tested animals; *N* = 3–6 per group in the vehicle control animals. Bar errors represent the SEM. Statistics: Unpaired *t*-tests. \* *p* < 0.05; \*\* \* *p* < 0.001.

<figure><img src="image_22.png" /></figure>
confirmed this dye's suitability for assessing BBB by perform-
ing a pilot study comparing the dispersion of conjugated mol-
ecules of different sizes throughout the body at different time
points and determined that 4kDa Dextran conjugated with flu-
orescent dye was optimal for assessment of tissue dispersal 3h
post-injection (not shown). Three hours after injection, fish were
culled, and dissected brain fluorescence was measured (using
a Li-cor Odyssey imaging system). Our data show that BBB
permeability increases with WT ageing, as measured by 4kDa
dextran-FITC-associated fluorescence in the brain, and is accel-
erated in the absence of telomerase (tert⁻/⁻) from the tested age
of 9–16 months (Figure 4).

2.5 | Telomerase (Tert) Depletion Accelerates Ageing-Associated Behavioural Changes in Zebrafish

It is known that inflammation can influence behaviour. For
example, lipopolysaccharide (LPS)-induced peripheral inflam-
mation has been shown to lead to behavioural alterations in
rodents, in a dose-dependent manner, including changes in
anxiety-like behaviour (Bassi et al. 2012; Tarr et al. 2012). Given
that we observed a telomerase-dependent increase in neuroin-
flammation with ageing, we set out to test whether it was asso-
ciated with any alterations in behaviour. To do this, we assessed
anxiety-like behaviour using the well-established novel tank
test (Wong et al. 2010; Kalueff 2017) in WT and tert⁻/⁻ zebrafish
at young and old ages (Figure 5A). The novel tank test showed
that, with ageing, WT fish tend to spend less time in the top part
of the tank, particularly at 30–36 months of age, indicative of
increased anxiety-like behaviour. Ageing-associated increase in
anxiety-like behaviour is accelerated in tert⁻/⁻ from the early age
of 2–6 months (Figure 5B). Importantly, no differences between
groups were observed in the total distance swam throughout the
test (Figure 5C), supporting that the differences observed in the

time spent swimming in the top area of the tank are not due to
locomotive defects.

3 | Discussion

Decreased telomerase expression, telomere shortening, senescence-associated markers, and inflammation have all been independently observed in the ageing brain and associated with human neurodegeneration. However, causality between limited telomerase expression and increased brain senescence and neuro-inflammation in a WT setting was yet to be established. Here, we addressed these questions using the zebrafish as an ageing model which, akin to humans, displays premature aging and death in the absence of telomerase and where telomere shortening is a driver of cellular senescence. We hypothesised that limited expression of Tert in the aged zebrafish brain contributes to brain degeneration with ageing and provide the first broad characterisation of zebrafish brain ageing in the presence and absence of telomerase (Tert).

Our transcriptomic analyses highlighted increased inflam-
mation and decreased cell cycle as key signatures of ageing
accelerated in the absence of telomerase (tert), which were re-
flected at the cell/tissue level and accompanied by behaviour
alterations. Interestingly, some ageing-associated phenotypes
were accelerated from as early as 2–6 months in the *tert*⁻/⁻,
whereas the majority were from 9 to 16 months, which led us to
our current working model (Figure 6). In specific, telomerase
depletion accelerates ageing-associated telomere shortening,
accumulation of SA-β-Gal, a lysosomal protein, which is gener-
ally known to accumulate with senescence; and microglia/mac-
rophages numbers from the young age of 2–6 months in *tert*⁻/⁻.
These tissue-level changes are accompanied by an increase of
ageing-associated increase in anxiety-like behaviour from that
same early age in *tert*⁻/⁻. Our data lead us to suggest a potential

<figure><img src="image_23.png" /></figure>

**FIGURE 5** | Telomerase depletion accelerates ageing-associated increase in anxiety-like behaviour. (A) Schematic figure of the setup and behaviour assessment during the novel tank test (NTT). (B) Quantifications show that old WT (30–36 months) spend less periods of time in the top part of the tank compared to younger siblings, and that this behaviour is accelerated in *tert*⁻/⁻ from the age of 2–6 months. (C) Quantifications of the total distance swam during the entirety of the test show no differences between groups, suggesting that the differences observed in the time spent in the top area of the tank are not explained by locomotive defects. *N* = 10–14. Each dot represents a fish. Bar errors represent the SEM. \* *p* < 0.05; \*\*\* *p* < 0.001.
model in which SA-βGAL accumulation and recruitment of immune cells occur. Whether this increased neuroinflammation is detrimental remains to be determined. Neuroinflammation is associated with neurodegeneration and disease in humans (Gaikwad et al. 2024; Righi et al. 2025; Samant et al. 2024), but causality is not clear. It is not clear from our data whether neuroinflammation in the aged zebrafish brain is contributing to the senescence phenotypes via paracrine senescence mechanisms (Acosta et al. 2013) or whether immune cells are ‘trying’ to clear senescent cells (Lujambio 2016). Nevertheless, even if initially recruited for clearance purposes, this function may be decreased with ageing and thus chronic inflammation may contribute to a senescence phenotype by increasing senescence-associated markers such as SASP and DDR. Our data specifically highlight *psme2* (proteasome activator subunit 2) as a particularly relevant putative SASP-associated factor since it was identified in both our STEM and broader DEGs of old age accelerated in the absence of telomerase (Figure S6). Loss of proteostasis is a known hallmark of ageing and *psme2* may

be involved in this crosstalk (Lopez-Otin et al. 2023). Our data show that telomerase depletion accelerates ageing-associated increase in immune activation (increased chitotriosidase activity) and BBB permeability, potentially as a consequence of increased inflammation and accumulation of cellular senescence. In turn, BBB leakiness is likely to lead to further recruitment of immune cells from the periphery, further exacerbating inflammation and BBB dysfunction in a positive feedback loop. Of note, our broad analysis of ‘DEGs of old age’ showed that *ctss2.1* is significantly upregulated with ageing, which has recently been implicated in macrophage-driven ageing-associated disruption of the blood-CSF barrier (Chen et al. 2025).

Together, our work shows that telomerase has a protective role in the zebrafish brain against the accumulation of senescence-associated markers and neuro-inflammation, and is required for BBB integrity. Importantly, the acceleration of senescence-associated markers in the absence of *tert* occurs not only in the expected proliferative areas but also in non-proliferative ones,

<figure><img src="image_24.png" /><figcaption>**FIGURE 6** | Working model: Timeline of ageing events accelerated by telomerase depletion in the zebrafish brain. (A) Survival curve showing the early to late stages of ageing in WT fish compared to *tert*⁻/⁻ fish, highlighting the changes we identify at cellular, tissue, and functional levels with ageing and that are accelerated in the absence of telomerase, at different ages throughout their life course. (B) Table summarising the sequence of events we observe in the ageing brain and that are accelerated by telomerase depletion: Accumulation of lysosomal proteins (such as SA-β-Gal), increased number of macrophages, and increased anxiety-like behaviour are already observed at the young age of 2–6 in *tert*⁻/⁻ fish (but only at later ages in WT fish). Dysfunction at lysosomes and recruitment of immune cells are likely to contribute to a senescence phenotype by increasing senescence-associated markers such as SASP and DDR, which are observed at 9–16 months in *tert*⁻/⁻. This is accompanied by increased immune activation (increased chitotriosidase activity) and BBB permeability at the same age, potentially as a consequence of increased inflammation and accumulation of cellular senescence. In turn, BBB leakiness is likely to lead to further recruitment of immune cells from the periphery, further exacerbating inflammation and BBB dysfunction in a positive feedback loop.</figcaption></figure>
where it is unlikely due to telomere-dependent replicative ex-
haustion, suggesting that non-canonical roles of telomerase may
be involved.

4 | Materials and Methods

**4.1 | Animal Husbandry, Ethics, Genotypes and Ages**

Zebrafish were maintained at the standard conditions of
27°C–28°C, in a 14:10 h light–dark cycle, and fed twice a day
with Artemia (live rotifers) and Sparus (dry food). All the
experiments were performed at the University of Sheffield.
All animal work was approved by local animal review
boards, including the Local Ethical Review Committee at
the University of Sheffield (performed according to the pro-
tocols of Project Licences 70/8681 and PP4455093). Three
strains of adult zebrafish (*Danio rerio*) were used for these
studies: wild-type (WT; AB strain), tert⁻/**(tertAB/hu3430)** and
*Tg(mpeg1:mCherryCAAX)sh378* (Source: University of
Sheffield). Wild-type fish were obtained from the Zebrafish
International Resource Center (ZIRC). The telomerase mu-
tant line tertAB/hu3430 was generated by N-ethyl-nitrosourea
mutagenesis (Utrecht University, Netherlands (Wienholds
et al. 2003)). It has a T → A point mutation in the tert gene
and is available at the ZFIN repository, ZFIN ID: ZDB-
GENO-100412–50, from ZIRC. The fish used in this study are
direct descendants of the ones used in the first studies describ-
ing this line (Carneiro et al. 2016; Henriques et al. 2013), by
which point it had been subsequently outcrossed five times
with WT AB for clearing of potential background mutations
derived from the random ENU mutagenesis from which this
line was originated.

The tert<sup>hu3430/hu3430</sup> homozygous mutant is referred to in the paper as *tert<sup>-/-</sup>* and was obtained by in-crossing our tert heterozygous AB/hu3430 strain, and WT and *tert<sup>-/-</sup>* fish compared in this study are siblings, derived from that F1 *tert<sup>+/-</sup>* in-cross, following gold standard guidelines to use this line, as recently reviewed here (Henriques and Ferreira 2024). Similarly, in order to produce WT and *tert<sup>-/-</sup>* siblings carrying the *Tg(mpeg1:mCherryCAAX) sh378* transgene, *tert<sup>+/-</sup>*, *mpeg1:mCherryCAAX* were incrossed.

In this study, we used only males, given that the kinetics
of telomerase-dependent ageing in zebrafish has been well
described for males only (Henriques et al. 2013; Carneiro
et al. 2016), which is a limitation of the model and this study.
Nevertheless, there is data showing ‘Limited sex-biased neural
gene expression patterns across strains in Zebrafish’ (Wong
et al. 2014), giving strength to our study.

Genotyping was performed by PCR of the tert gene as before. To study age-related phenotypes in the zebrafish brain, we used 30–36 months old fish for what we consider old in WT (in the last 25%–30% of their lifespan), and we considered the *tert*⁻/⁻ old fish at the equivalent age (18–24 months), which approximately corresponds to the last 25%–30% of their lifespan. In specific, ‘Old’ was defined as the age at which most of the fish present age-associated phenotypes, such as cachexia, loss of body mass, and curvature of

the spine. These phenotypes develop close to the time of death and
are observed at 30–36 months of age in WT and at > 18–24 months
in *tert*⁻/⁻ (Henriques et al. 2013; Carneiro et al. 2016).

Note on Zebrafish gene and protein nomenclature

This manuscript followed the agreed convention as described in:

https://zfin.atlassian.net/wiki/spaces/general/pages/1818394635/ZFIN+Zebrafish+Nomenclature+Conventions

In summary:

Gene nomenclature

Genes should be named after the mammalian orthologue when-
ever possible. When mammalian orthologues are known, the
same name and abbreviation should be used, except all letters
are italicized and lower case. Members of a gene family are se-
quentially numbered.

Examples:

Names—engrailed 1a, engrailed 2b.

Symbols—eng1a, eng2b.

Protein nomenclature

The protein symbol is the same as the gene symbol, but non-
italic and the first letter is uppercase.

Note the differences between zebrafish and mammalian nam-
ing conventions:

Species/gene/protein

zebrafish/shha/Shha

human/SHH/SHH

mouse/Shh/SHH

**4.2** | Tissue Fixation

4.2.1 | Paraffin-Embedded Sections

Fish were culled by immersion in concentrated tricaine (MS-222). After terminal anaesthesia, the animals were fixed by immersion in 50 mL of Neutral buffered formalin (NBT, VWR International, Radnor, PA, USA) at 4°C for 48 h–72 h and de-calcified in 50 mL of 0.5 M ethylenediaminetetraacetic acid (EDTA), pH 8, for an additional 48 h–72 h. Then, the whole fish were paraffin-embedded (in formalin I for 10 min, formalin II for 50 min, ethanol 50% for 1 h, ethanol 70% for 1 h, ethanol 95% for 1 h 30 min, ethanol 100% for 2 h, ethanol 100% for 2 h 30 min, 50%:50% of ethanol 100%: xylene for 1 h 30 min, xylene I for 3 h, xylene II for 3 h, paraffin I for 3 h, paraffin II for 4.30 h), longitudinally sliced (sagittal slices, 3 µm thick, using the Leica TP 1020) and stored at room temperature (RT). To do this, diluted
ethanol absolute (VWR International) and paraffin histosec
pastilles (Merck & Co, Kenilworth, NJ, USA) were used.

4.2.2 | Cryosections

Fish were culled by immersion in concentrated MS-222, followed
by immediate decapitation (Schedule 1 method). The brain was
dissected, transferred to, and rinsed in cold L15 (Thermo Fisher
Scientific, Waltham, MA, USA). Afterwards, the brain was fixed
in 4mL NBT overnight at 4°C, washed in 3mL cold Phosphate
Buffered Saline (PBS, 1×), and immersed in 4mL of 30% sucrose
in PBS overnight at 4°C for cryoprotection. Finally, the tissue
was mounted in a base mould (Electron Microscopy Sciences,
Hatfield, PA, USA), with O.C.T. (VWR), snap frozen, and stored
at -20°C. Prior to use, 13µm coronal tissue sections were cut
using a Leica CM1860 cryostat, attached to SuperfrostPlus slides
(Epedia), and air dried for a minimum of 2h at RT. Slides were
stored at -20°C for a maximum of 3months.

4.3 | RNA Probe Synthesis for In Situ Hybridisation (ISH)

A Zebrafish p16-like fragment was amplified using the fol-
lowing primers: FW cgttgaacATGAACGTCG and REV tcttta-
aacattTTAAACACGATTGAG and cloned into the pCR-Blunt
II-TOPO vector (Thermofisher Scientific Inc., USA). For anti-
sense probe generation, the plasmid was linearised with HindIII
(New England Biolabs), and synthesis was carried out with T7
RNA polymerase (New England Biolabs) and DIG-labelling mix
(Merck Life Science UK Limited).

For the sense probe, plasmid was linearised with XhoI (New
England Biolabs), and probe synthesised with SP6 RNA poly-
merase (New England Biolabs).

Probes were cleaned up using SigmaSpin post-reaction clean-up
columns (Sigma Aldrich) and used at a final concentration of
3 ng/µL for hybridisation.

4.4 | Chromogenic RNA Fluorescence In Situ Hybridisation (RNA-FISH)

Cryosections were fixed with NBT for 10 min at RT followed
by 3× washes with Diethyl pyrocarbonate (DEPC)-treated
PBS. Sections were then acetylated with 11.2 µL triethanol-
amine and 2.5 µL acetic anhydride per 1 mL H₂O for 10 min
at RT (both Sigma Aldrich). After 3× washes in DEPC-PBS,
sections were incubated in prehybridization solution (prehyb)
and coverslipped (prehyb: 50% formamide, 5× SSC pH 7, 2%
Blocking reagent, 0.1% TritonX100, 0.5% CHAPS, 1 mg/mL
yeast RNA, 50 µg/mL heparin sodium salt (all from Sigma
Aldrich)) for 1 h at 68°C in a humidified box. The solution was
replaced with 200 µL fresh prehyb containing 3 ng/µL DIG-
labelled RNA probe, coverslipped, and incubated overnight at
68°C in a humidified box.

Following this, coverslips were removed and slides were
washed for 1 h in prewarmed wash #1 (50% formamide, 5x

SSC pH 4.5, 1% SDS) at 68°C, then 1 h in prewarmed wash #2
(50% formamide, 2x SSC pH 4.5, 1% Tween 20) at 68°C in co-
plin jars.

Slides were washed in TBS-T (8 g NaCl, 0.2 g KCl, 25 mL 1 M Tris
pH 7.5, 10 mL Tween20 per L solution) at RT, blocked in TBS-
T/10% heat-inactivated goat serum for 40 min at RT, then incu-
bated in fresh block/alkaline phosphatase-conjugated anti-DIG
at 1:2000 for 80 min at RT.

Slides were washed extensively in TBS-T followed by 3× washes in freshly prepared NTMT (for 50 mL solution: 1 mL 5 M NaCl, 2.5 mL 2 M Tris pH 9.5, 2.5 mL 1 M MgCl2, 500 µL Tween20, 43.5 mL ddH2O). Development was started by incubation in filtered staining solution (4.5 µL NBT + 3.5 µL BCIP per 1 mL NTMT (both Merck Life Science UK Limited)) at RT in the dark. Development was monitored daily with replenishment of fresh filtered staining solution. Once the signal was clear, with minimal background staining, the reaction was stopped by washing in PBS. Slides were mounted in glycergel (Agilent, Santa Clara, CA, USA) or vectashield (2BScientific) before imaging on a Nikon Ti inverted microscope at 10×. Images were tiled using Nikon elements software. For analysis, the diencephalon and optic tectum (including PGZ) were delineated and thresholded using Fiji ImageJ to determine the area of positive staining within each macroarea. The percentage of positive tissue per section macroarea was then determined.

4.5 | Immunofluorescence (IF)

Paraffin sections with whole fish slices were dewaxed and hy-
drated through the following sequence of washes: histoclear
(Scientific Laboratory Supplies, Wilford, Nottingham, UK, 2 × 5
min), 100% ethanol (Thermo Fisher Scientific, 2 × 5 min), 90%
ethanol (5 min), 70% ethanol (5 min), and distilled water (2 × 5
min). Cryosections were hydrated in PBS for 10 min before pro-
ceeding. After antigen retrieval in 0.01 M citrate buffer at pH 6.0
(sub-boiling at 800 W in a microwave for 10 min), the tissue sec-
tions were permeabilized in PBS 0.5% Triton X-100 (for 10 min)
and blocked in 3% bovine serum albumin (BSA), 5% goat serum
in 0.3% Tween-20 in PBS (for 1 h). Then, primary antibody incu-
bation (overnight at 4°C) was followed by washes in PBS 0.1%
Tween-20 and secondary antibody incubation (1 h at RT or over-
night at 4°C). Finally, the tissues were incubated in DAPI for nu-
clear staining (Thermo Fisher Scientific), 1:10,000 dilution in PBS
from a 10 mg/mL stock, for 10 min; washed in PBS 1×, mounted
with vectashield (Vector Laboratories, Burlingame, CA, USA),
covered with a coverslip (Scientific Laboratory Supplies), and
sealed with clear nail varnish. For details on the primary and sec-
ondary antibodies refer to Table 1 and Table 2, respectively.

4.6 | Telomere-Fluorescence In Situ Hybridization (Telo-FISH)

Telo-FISH was performed after IF. Briefly, after incubation
with the secondary antibody and respective washes, the tis-
sues were fixed in 4% PFA (SIGMA-ALDRICH) for 20 min.
Washes in PBS, 3 × 10 min were followed by a dehydration step
consisting of washes with ice cold 70%, 90% and 100% ethanol
<table><caption>**TABLE 1** | Primary antibodies used for immunostaining.</caption><thead><tr><th>Antibody, species and type</th><th>Dilution factor</th><th>Catalogue number; company; country</th></tr></thead><tbody><tr><td>PCNA mouse monoclonal</td><td>1:500</td><td>NB500-106; Novus Biologicals, Littleton, CO, USA</td></tr><tr><td>γH2AX, rabbit polyclonal</td><td>1:500</td><td>GTX127342; GeneTex, Irvine, CA, USA</td></tr><tr><td>L-plastin (Lcp1), rabbit polyclonal</td><td>1:200</td><td>GTX124420; Novus Biologicals, Littleton, CO, USA</td></tr><tr><td>RFP, mouse monoclonal</td><td>1:500</td><td>GTX82561; GeneTex, Irvine, CA, USA</td></tr></tbody></table>

<table><caption>**TABLE 2** | Secondary antibodies used for immunostaining.</caption><thead><tr><th>Antibody, species and type</th><th>Dilution factor</th><th>Catalogue number; company; country</th></tr></thead><tbody><tr><td>Goat anti-rabbit IgG Alexa Fluor 488</td><td>1:500</td><td>A11008; Invitrogen, Carlsbad, CA, USA</td></tr><tr><td>Goat anti-rabbit IgG Alexa Fluor 568</td><td>1:500</td><td>10,032,302; Thermo Fisher Scientific, Waltham, MA, USA</td></tr><tr><td>Goat anti-mouse IgG Alexa Fluor 647</td><td>1:500</td><td>A21235; Thermo Fisher Scientific, Waltham, MA, USA</td></tr></tbody></table>

(Thermo Fisher Scientific, 3 min each). After this, the slides were left to dry for 1 h, 10 µL of hybridisation solution was pipetted on top of each tissue section and the slides were covered with a coverslip. Hybridisation solution (for a total volume of 100 µL): 8.56 µL MgCl2 buffer pH 7.0, 82 mM sodium hydrogen phosphate (SIGMA-ALDRICH), 70 µL of deionised formamide (Thermo Fisher Scientific), 1 µL of 1 mM Tris, pH 7.5 (SIGMA-ALDRICH), 5 µL of 1× blocking buffer (10× blocking reagent (Merck & Co.) diluted to 1× in maleic acid (SIGMA-ALDRICH)), 2 µL of PNA probe (Telo-Cy3, zebrafish, 568; Cambridge Research Biochemicals, Cleveland, UK), 2 µL of Cent-Fam probe (488; Eurogentec, Liège, Belgium), and 11.44 µL of dH2O.

The slides were incubated for 10 min at 80°C for DNA denaturation, followed by incubation for 2 h at RT, in the dark, to allow the probe to hybridise with the complementary DNA. After the incubation period, coverslips were gently removed and the slides were washed for 10 min in a solution made of 70% formamide (SIGMA-ALDRICH), 10% 2× saline-sodium citrate (SSC, SIGMA-ALDRICH), and 20% dH2O. This was followed by two more washes (10 min each) in 2× SSC. SSC was diluted from a 20× stock solution consisting of 88.3 g of trisodium citrate dihydrate (SIGMA-ALDRICH) and 175.3 g of NaCl (SIGMA-ALDRICH), diluted in dH2O to a total volume of 1 L, adjusted to a pH of 7.0, and autoclaved. Finally, the slides were washed in PBS, incubated in DAPI (Thermo Fisher Scientific) for 10 min, and mounted as previously described in Section 4.5.

## 4.7 | IF and Telo-FISH Imaging and Quantification

IF and telo-FISH staining was imaged using the DeltaVision microscope, Nikon Widefield inverted microscope, or a Nikon

Ti2 W1 spinning disc confocal at the Wolfson Light Microscopy Facility (LMF), using a 40× objective. An average of 20 images was acquired from the brain of each animal (paraffin sections) for longitudinal paraffin sections and 18 for transverse cryosections (minimum 6 images per macro-area). For images showing whole brain transverse cryosections, either 4× or 10× images were taken, with tiling in Nikon elements software for the latter. Imaging settings were kept consistent within experiments for accurate comparisons.

After acquisition, the images were analysed by manual counting: (1) the number of cells with more than 5 foci of γH2AX (considered γH2AX+ cells) PCNA-, (2) the number of PCNA+ cells, (3) the number of L-plastin+, (4) the number of mpeg+ cells and (5) the total number of cells. Telo-FISH staining was quantified by the intensity of the mean of the nucleus. The ratio ‘telomeres/centromeres’ was calculated by dividing the intensity of the mean of telomeres (568 channel) by the intensity of the mean of centromeres (488 channel), which normalises the telomeric probe signal by the centromeric probe signal within individual cells. The brain commonly exhibits cytoplasmic autofluorescence from vascular and other cell types. These were excluded from our analyses by comparison to neighbouring control sections stained only with secondary antibodies. Nuclear markers such as γH2AX and PCNA were only classed as positive when they showed signal in the nucleus as determined by co-labelling with DAPI. For telomeric and centromeric labelling analysis, individual nuclei were circumscribed based on DAPI labelling and mean grey fluorescence determined. Mpeg+ and Lplastin+ cells were manually counted and could be easily distinguished from autofluorescent cells based on the strength of signal and morphology.

The analysis was performed on raw images, using the ImageJ Fiji (v. 1.51). Images were then processed with Adobe Illustrator 21.0.2 for display purposes.

## 4.8 | SA-β-Gal

The Senescence-Associated β-Galactosidase Staining Kit (Cell Signalling Technology, Danvers, MA, USA) was used to perform the SA-β-Gal staining, following the manufacturer's instructions.

### 4.8.1 | Whole Brain SA-β-Gal Staining

Fish were culled with neat Tricaine and decapitated. Whole heads were fixed in 4ml PFA overnight at 4°C with shaking.
The following day, whole heads were washed 3 × for 1 h in PBS at RT with slow shaking. Whole brains were then dissected in fresh PBS and immersed in 2 mL of SA-β-Gal staining solution mix, consisting of 1× staining solution (pH 5.9), 1× solution A, 1× Solution B, 1 mg/mL Xgal (SA-β-Gal staining kit #9860 by Cell Signalling Technology), and incubated at 37°C overnight with slow shaking in the dark. The following day, brains were washed 3 × for 1 h in PBS at RT with slow shaking and imaged using a Nikon SMZ1500 Stereomicroscope (2000).

### 4.8.2 | SA-β-Gal Staining on Cryosections

Briefly, after rinsing the cryosections in 1× PBS for 5 min, 200 µL of β-Gal staining solution was added per slide as above. Then, the slides were coverslipped and incubated at 37°C overnight in a humidified chamber box. The following day, cryosections were rinsed in 1× PBS (3 × 1 min) and mounted in glycergel (Agilent, Santa Clara, CA, USA). Slides were then imaged using an Olympus BX60 microscope or Nikon Ti inverted microscope at 10× with tiling using Nikon elements software.

Image analysis was done using ImageJ Fiji as described for RNA-FISH. Images were then processed with Adobe Illustrator 21.0.2 for display purposes.

For SA-β-Gal combined with IF, sections were first labelled with SA-β-Gal as described above, then fixed for 10 min with NBT at RT, rinsed and processed for IF as described in methods Section 4.5.

## 4.9 | RNA Extraction

Fish were culled by Schedule 1 method (as described before in Section 2.2). Zebrafish brains were dissected, transferred to a microcentrifuge tube with 100 µL of Trizol (Thermo Fisher Scientific), snap frozen in dry ice, and stored at -80°C until RNA extraction.

For RNA extraction, the tissue was homogenised with a mechanical homogeniser (VWR International) and a 1.5 mL pestle (Kimble Chase, Vineland, NJ, USA), and an extra 50 µL of fresh Trizol (Thermo Fisher Scientific) was added. After 5 min of incubation at RT, 30 µL of chloroform (1:5, VWR International) was added. The samples were then incubated for a further 3 min at RT and centrifuged (at 13,000 g for 30 min at 4°C). The resulting upper aqueous phase was transferred to a new microcentrifuge tube, and isopropanol (Thermo Fisher Scientific) was added (approximately 83% of the amount of the aqueous phase). After an incubation of 10 min at RT, the samples were centrifuged (13,000 g for 15 min at 4°C) and the supernatant was discarded. The pellets were washed in 250 µL of 75% ethanol twice and then left to air dry. In the end, the pellets were resuspended in 14 µL of nuclease-free water (VWR International) and stored at -80°C. All the samples were analysed by a Nanodrop 1000 Spectrophotometer to measure the concentration of RNA. Some samples (randomly chosen) were also analysed in the Bioanalyzer to assess RNA integrity. All the samples included in this study had an RNA integrity number (RIN) of > 9.

## 4.10 | RNA Sequencing

RNA-Sequencing was performed at the Genomics and Sequencing facility of Sheffield Institute for Translational Neuroscience (SITraN). Mature RNA was isolated and fragmented using the NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs Inc), following the manufacturer's instructions. The libraries were prepared using the NEBNext Ultra Directional RNA Library Prep Kit (Illumina), following the manufacturer's instructions. The NEBNext Multiplex Oligos from Illumina (Index Primers Set 1, New England Biolabs Inc) were used for the library preparation using the following PCR program: 98°C for 30 s (1 cycle), 98°C for 10 s followed by 65°C for 75 s (8 cycles), and 65°C for 5 min. The PCR product (≈50 µL) was then purified using 45 µL of Oligo dT Beads (Agencourt AMPure XP), followed by three washes with ethanol 80%. After air-drying, the dscDNA was eluted by adding 23 µL of 0.1× TE, and the resulting 20 µL of the library were collected. Sequencing was run on an Illumina HiScan SQ sequencer, using a high output run and sequencing by synthesis V3 chemistry on a single 100 base pair (bp) run. The sequencing process was monitored in real-time using Sequencing Analysis Viewer v1.8.37 Software, and therefore quality control was performed during and after sequencing. Then, the data was imported into a FASTA file format to perform the analysis.

## 4.11 | RNA Sequencing Analysis

### 4.11.1 | Data Processing

Featurecounts was run to obtain per-gene read counts. We calculated the transcriptome coverage for each sample, defined as the number of unique sequencing reads aligning to annotated transcriptomic regions. Coverage was estimated by multiplying the number of assigned reads (as determined by featureCounts) by the read length and dividing by the estimated total transcriptome length of approximately 76 Mb (from the Ensembl GRCz11_109 GTF file). On average, we obtained a coverage of 12X across all samples, corresponding to an average assignment rate of approximately 65%.

### 4.11.2 | Differential Expression

To identify signatures of ageing, WT brain samples were subjected separately to DESeq2 analysis, comparing the time points 9, 22 and > 30 months with the time-point of 2 months. Then, *tert*⁻/⁺ samples at 2, 9 and 22 months were compared with WT at 2 months, to identify telomerase-dependent ageing processes.

### 4.11.3 | Enrichment Analysis

Pathway enrichment analysis was performed using the Functional Annotation Bioinformatics Microarray Analysis programme (https://david.ncifcrf.gov/). Pathways identified in both Gene Ontology Biological Process (GO-BP) and KEGG databases were included in the analysis. The top 10 GO-BP and KEGG terms were then plotted into a heatmap using GraphPad Prism.
### 4.11.4 | Hypergeometric Enrichment Analysis

To assess whether the genes identified through the STEM analysis were over-represented in specific brain cell types, we performed over-representation analysis using Fisher's Exact Test under the hypergeometric distribution. For each cell type from the previously published adult zebrafish brain single-cell RNA-seq dataset (Anneser et al. 2024), we used the authors' defined gene programmes, consisting of marker genes with adjusted *p* < 0.05. These marker genes were compared to the list of STEM analysis genes to test whether the overlap was greater than expected by chance, relative to the total number of protein-coding genes in the zebrafish genome. A Fisher's Exact Test was applied for each cell type, and resulting *p*-values were adjusted for multiple comparisons using the Bonferroni correction method.

### 4.11.5 | Mapping to the Hallmarks of Ageing

We analyzed the recent Frenk & Houseley's paper (Frenk and Houseley 2018), which describes the different transcriptional hallmarks of ageing. In this paper, the different hallmarks are defined within specific text sections. For each hallmark, we collected groups of keywords that could be within GO term names. The complete list of GO term names was extracted from the file `go-basicobo` from the Gene Ontology website (https://geneology.org/). We then established lists of GO term names for each transcriptional hallmark of ageing. The mapping table between the hallmarks, the keywords, and the lists of GO term names is available in the table `GO_to_HA-THA_V1.csv`.

Zebrafish genes and their associated GO term names were retrieved from the mapping function `Gene2GoReader()` from the Python library `goatools`. We built three distinct dictionaries for each GO category (Biological Process, Molecular Function, and Cellular Component), where the entrezgene IDs are associated with their GO term names. From the differential analyses, we obtained different lists of gene symbols that must first be mapped to entrezgene ID using the Python library `mygene`. Each gene ID is associated with a list of GO term names. To assess a particular transcriptional hallmark of ageing for a given gene, we parsed their associated list of GO term names and mapped them to their corresponding hallmark of ageing.

Depending on the situation, the attribution of the hallmark of ageing is done as follows: (1) all the GO term names are mapped to the same transcriptional hallmark of ageing, (2) the GO term names are mapped to several transcriptional hallmarks of ageing; the hallmark with the highest number of occurrences is chosen. Otherwise, the gene is assigned an 'ambiguous' hallmark of ageing if (3) none of the GO term names are mapped to any transcriptional hallmarks of ageing; then the gene is not annotated with a transcriptional hallmark of ageing.

### 4.12 | cDNA Synthesis for Real-Time qPCR

cDNA synthesis was performed using the SuperScript II RT Kit (Thermo Fisher Scientific, USA) kit, following the manufacturer's

instructions. Approximately 300 ng of each RNA sample was mixed with 0.5 µL of 500 µg/mL random primers, 1 µL 10 mM dNTPs, and sterile, PCR-grade water to a final volume of 12 µL. Samples were heated to 65°C for 5 min, followed by cooling at 4°C and placement on ice. To each sample, 4 µL First-Strand Buffer, 2 µL 0.1 M DTT, and 1 µL NxGen RNase inhibitor (LGC Biosearch Technologies, Hoddesdon, UK) were added and gently mixed. Samples were heated to 25°C for 2 min, followed by the addition of 1 µL SuperScript II RT. Samples were then incubated at 25°C for 10 min, followed by incubation at 42°C for 50 min, before the reaction was inactivated by heating at 70°C for 15 min.

### 4.12.1 | Real-Time qPCR

cDNA samples from zebrafish brains were analysed by real-time qPCR using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad), forward and reverse primers at a final concentration of 200 nM (Table 1), and cDNA diluted 1:40. Samples were analysed in triplicate. The CFX384 Touch Real-Time PCR Detection System (Bio-Rad) was used to run each real-time qPCR. The program settings were: 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 58.4°C for 15 s. A melt curve analysis was included in each experiment by increasing the temperature from 65°C to 95°C at a 0.5 C/5 s increment. To ensure that the Cq values of the sample cDNA fell within a linear range in which the primer efficiency was between 90% and 110%, a two-fold serial dilution of pooled cDNA from randomly selected samples was included in each run, with curves containing at least 5 data points. No template (water) and no reverse transcriptase controls were also included in each run.

During analysis, the standard error of each triplicate was calculated and samples with triplicates presenting a standard deviation ≥0.5 were excluded. The fold change expression for each gene was calculated using the ΔΔCt method (Collins et al. 2022). Expression of each target gene was normalised to the housekeeper gene (*beta-actin*), and the average fold-change in values for each group compared to the young WT group was calculated.

Graphs and respective statistical analysis were performed using GraphPad Prism 10.4.2. Normality was assessed using the Shapiro-Wilk test and statistical comparisons were carried out using a two-way ANOVA, followed by Dunnett's test for multiple comparisons. A *p*-value of < 0.05 was considered significant.

### 4.13 | Protein Extraction and Quantification

Fish were culled by the Schedule 1 method as previously described. Brain tissue was dissected, transferred to a microcentrifuge tube, and immediately snap-frozen in liquid nitrogen. The samples were stored at -80°C until protein extraction.

The tissue was homogenised in 100 µL of ultra-pure water using a mechanical homogeniser (VWR International) and RNase-free disposable pestles (Thermo Fisher Scientific) for approximately 30 s, on ice. Then, the samples were spun down at 4°C, at 2000 rpm, for 5 min. After transferring the supernatant to a new tube, samples were stored at -80°C until further use.
Protein extracts were quantified by the bicinchoninic acid (BCA) method, using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific), following the manufacturer's instructions. Briefly, a standard curve was prepared with a range of 0.2–2 µm of BSA, along with a blank control (containing ultra-pure water only). A total of 5 µL of each protein extract was mixed with 1 mL of BCA in microcentrifugation tubes and incubated at 37°C for 10 min in a water bath. After adding an additional 20 µL of copper sulphate, the solution was mixed and incubated at 37°C for 20 min. Then, 200 µL of each sample (or BSA standard curve) was transferred to a Corning Costar Brand 96-Well EIA/RIA Plate (Thermo Fisher Scientific) in duplicates, and absorbances were read in a Varioskan Plate Reader at a wavelength of 562 nm.

### 4.14 | Chitotriosidase Activity Assay

Chitotriosidase activity was measured using the protocol previously described by (Keatinge et al. 2015). This method allows assessment of the hydrolysis capacity of 4-methylumbelliferyl-β-D-N, N', N''-triacetylchitotriose (4-MU-chitotrioside) by chitotriosidase enzyme.

Briefly, a total of 10 µL of protein extract per sample was mixed with 100 µL of substrate solution and incubated at 28°C for 6 min. A blank control containing 10 µL of ultra-pure water was prepared alongside the samples. Substrate solution consisted of 4-MU-chitotrioside (SIGMA-ALDRICH) diluted in McIlvaine solution at pH 5.2 (1.73 mg of 4-MU-chitotrioside per 100 mL of McIlvaine solution). To make the McIlvaine solution, stock solutions of 0.1 M citric acid (SIGMA-ALDRICH) and 0.2 M di-sodium hydrogen phosphate (SIGMA-ALDRICH) were mixed at a ratio of 45% and 55%, respectively, obtaining a final pH of 5.2. After incubation, 1 mL of stop solution (0.25 M glycine buffer, pH 10.4, containing 0.01% Triton X-100 (SIGMA-ALDRICH)) was added to each sample (and blank control), and 200 µL of the final solution was transferred to a Corning Costar Brand 96-Well EIA/RIA Plate (Thermo Fisher Scientific). Duplicates of each sample were used. An extra microcentrifuge tube was prepared with 200 µL of standard solution (5 µM 1,4-Methylumbelliferone, SIGMA-ALDRICH) and 900 µL of stop solution. A volume of 200 µL of the final solution was transferred to the 96-well plate, in duplicates. The fluorescence was then read in a Varioskan Plate Reader (excitation: 365 nm; emission: 450 nm). Finally, the chitotriosidase activity was calculated in nM/h/mL, using Formula 4.

$$ \frac{(T - B)}{\text{std}} \times \frac{60}{\text{incubation time [min]}} \times \frac{1000}{\text{volume [µL]}} $$

T, fluorescence of test samples; B, fluorescence of blank; Std, standard.

### 4.15 | Blood Brain Barrier (BBB) Permeability Assay

To test BBB permeability, a total of 5 µL of 0.1% IRDye 680RD Dextran (LI-COR Biosciences, Lincoln, NE, USA) diluted in

HBSS (Thermo Fisher Scientific) was injected by intraperitoneal injection (IP). Two different sizes of the compound were tested, with the following molecular weights: 4 kDa and 70 kDa. A group of fish injected with 5 µL of HBSS alone was used as a vehicle control group. The fish were culled 3 h after IP, and the brains were collected and imaged using a LI-COR Odyssey CLx machine (LI-COR). The brain fluorescence was then quantified using the Image Studio 4.0 Software for Odyssey CLx (LI-COR).

### 4.16 | Mitochondrial Complex Assays

Homogenised brain tissue was diluted to approximately 0.7 mg protein/mL and subjected to 3 cycles of rapid freeze-thawing in liquid nitrogen. All spectrophotometric assays were performed at 28°C in 200 µL final volume using a Fluostar Omega plate reader. For assessing complex I activity, the assay buffer was 25 mM KH₂PO₄, 5 mM MgCl₂, (pH 7.2), 3 mM KCN, 2.5 mg/mL BSA, 50 µM ubiquinone, and 2 µg/mL antimycin A. Complex I activity was determined by measuring the oxidation of nicotinamide adenine dinucleotide reduced disodium salt (NADH) at 340 nm with a reference wavelength of 425 nm (ε = 6.22 mM⁻¹ cm⁻¹). 125 µM NADH started the reaction, and 3 µg/mL rotenone was used to inhibit the reaction. For assessing complex II activity, the brain homogenate was incubated for 10 min in assay buffer containing 25 mM KH₂PO₄, 5 mM MgCl₂, (pH 7.2), 3 mM KCN, and 20 mM succinate. 50 µM 2,6-DCPIP, 2 µg/mL antimycin A, and 3 µg/mL rotenone were then added, and baseline measurements recorded. The reaction was started with the addition of 50 µM ubiquinone Q1, and complex II activity was determined by measuring the reduction of 2,6-dichlorobenzenone-indophenol sodium salt (2,6-DCPIP) at 600 nm (ε = 19.2 mM⁻¹ cm⁻¹). Complex I and complex II activity rates are normalised to citrate synthase activity.

### 4.17 | Novel Tank Diving Test (NT)

The novel tank diving test (NT) was performed as previously reported by (Wong et al. 2010) and (Kalueff 2017). Briefly, 6 T-75 transparent flasks filled with aquarium water were inserted in the Zantiks LT behavioural unit (Zantiks Ltd, Cambridge, UK). After a 1 h acclimatisation period in the behaviour room, the fish were individually placed into the flasks and allowed to free swim for 5 min. A side camera was used to record the behaviour of the fish for the duration of the experiment. After the test, the fish were returned to their home tanks. The fish tracking was used to calculate the time spent and the total distance swam in the top area of the tank (top third of the flask), which were used as measures of anxiety-like behaviour. The more time spent and the longer distance swam in the top part of the tank indicate lower anxiety levels. Moreover, the total distance swam throughout the entire tank was used as a measure of general motor capacity. This was used as a control to test whether differences in distance swam in the top part of the tank identified between groups were not due to a potential locomotive defect but likely associated with an anxiety-like phenotype.
## 4.18 | EDU Pulse Chase Experiment and Analysis

EdU pulse chase was performed as described (Ellis et al. 2022; Martins et al. 2022). In brief, fish were fasted for 18 h, then anaesthetised in 4% MS-222 (Covetrus, pharmaceutical grade) injected intraperitoneally (IP) with 5 µL 10 mM EdU (Click it EdU Alexa Fluor 647 Imaging Kit (Invitrogen, C10340)) or control solution (Hanks' Balanced Salt Solution (HBSS) (Gibco, 14,175-053)) using a 30 G 3 mL insulin syringe (BD Micro-Fine U-100 insulin, REF 324,826). Fish were injected once every day for 3 consecutive days at the same time of day (3-day pulse) and culled on Day 4 (1 day chase), for paraffin embedding, sectioning, and IF (N=4 for each genotype/age).

Immunofluorescent images were processed using ImageJ Fiji (v1.54p) and QuPath (v0.6.0). Maximum intensity Z-projections were generated, with a minimum of three projections analysed per animal. Total cell counts were obtained from Channel 1 (DAPI) using QuPath's automated cell detection. EdU+ cells (Channel 4) were manually counted, and results were expressed as the percentage of EdU+ cells per field of view.

## 4.19 | Statistical Analysis

All the graphs and respective statistical analysis were performed with GraphPad Prism 7.0. Normality was assessed by comparing group variances using either the Brown-Forsythe test or the Shapiro-Wilk normality test. Unpaired t-test or Mann Whitney's unpaired t-test were used to compare 2 groups, depending on whether the samples presented a Gaussian distribution or not, respectively. For groups with normal distribution, comparisons over-time were performed using ANOVA, followed by Turkey test for multiple comparisons. For groups that failed the normality tests, non-parametric tests were performed, followed by Dunnett's test for multiple comparisons. A critical value for significance of *p* < 0.05 was used in all the analysis.

### Author Contributions

**Raquel R. Martins:** conceptualisation, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, validation, visualisation, writing. **Savandara Besse:** data curation, formal analysis, investigation, methodology, software, validation, visualisation, writing. **Pam S. Ellis:** data curation, formal analysis, investigation, methodology, validation, writing. **Rabia Sevil:** data curation, formal analysis, investigation, methodology, writing. **Naomi Hartopp:** data curation, formal analysis, investigation, methodology. **Catherine Purse:** data curation, formal analysis, investigation, methodology, writing. **Georgia Everett-Brown:** formal analysis, methodology, software. **Owain Evans:** investigation. **Nadiyah Mughal:** investigation. **Mina H. F. Wahib:** investigation. **Zerkif Yazigan:** investigation. **Samir Morsli:** methodology. **Ada Jimenez-Gonzalez:** methodology. **Andrew Grierson:** methodology. **Heather Mortiboyes:** data curation, formal analysis, funding acquisition, investigation, methodology. **Chrissy Hammond:** data curation, formal analysis, funding acquisition, investigation, methodology. **Michael Rera:** data curation, formal analysis, funding acquisition, investigation, methodology, project administration, software, validation, visualization, writing. **Catarina M. Henriques:** conceptualisation, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, writing.

### Acknowledgements

We would like to acknowledge and thank Dr. Marcus Keatinge for helping with the chitotriosidase activity assay. We would like to acknowledge and thank the University of Sheffield Aquarium staff for the great care they provide to our fish and help with protocols, fish selection, and breeding. We would like to acknowledge and thank the Wolfson Light Microscopy Facility staff for their support throughout the years with all our imaging needs and challenges. Finally, we would like to acknowledge John Kemp for critical discussion about transcriptional data and hypergeometric analysis and statistics. This work was generously funded by a University of Sheffield PhD studentship to R.R.M. This research was funded in whole, or in part, by Wellcome [206224/Z/17/Z/WT/Wellcome Trust/United Kingdom]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. C.M.H. was also funded by a Vice Chancellor's Fellowship, The University of Sheffield, and a VIVENSA grant AISRPG2305\32, which cover P.S.E. and C.P. salaries. R.S. is funded by the BBSRC SwbioDTP and C.H. by BBSRC (BB/Y002504/1). M.R. is funded by the CNRS and the ANR ADAGIO (ANR-20-CE44-0010). Thanks to the Bettencourt Schueller Foundation long-term partnership, this work was partly supported by the CRI Core Research Fellowship to Michael Rera for S.B. salary.

### Conflicts of Interest

The authors declare no conflicts of interest.

### Data Availability Statement

The dataset and source data generated during and/or analysed during the current study is either shared as supplementary and source data or is available from the corresponding author upon reasonable request. The RNA sequencing data from this experiment were deposited in Gene Expression Omnibus (GEO), accession GSE205601.

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**Supporting Information**

Additional supporting information can be found online in the Supporting Information section. **Appendix S1: Supporting Information.**