The ageing-associated progressive loss of proper tissue homeostasis maintenance makes age a prevalent risk factor for many human pathologies, including cancer, neurodegenerative, and cardiovascular diseases (Campisi, 2013; Niccoli and Partridge, 2012; Wyss-Coray, 2016). A better comprehension of the molecular mechanisms of human ageing is thus required for the development and effective application of therapies targeting its associated morbidities.

Dynamic transcriptional alterations accompany most physiological processes occurring in human tissues (López-Otín et al., 2013). Transcriptomic analyses of tissue samples can thus provide snapshots of cellular states therein and insights into how their modifications over time impact tissue physiology. A small proportion of transcripts has indeed been shown to vary with age in tissue (Stegeman and Weake, 2017) and sex-specific (Tower, 2017; Austad and Fischer, 2016; Melé et al., 2015; Gershoni and Pietrokovski, 2017; Kassam et al., 2019; Mayne et al., 2016) manners. Such variations reflect dysregulations of gene expression that underlie cellular dysfunctions (Stegeman and Weake, 2017).

Many studies analysed the age-related changes in gene expression in rodent tissues (Zahn et al., 2007; Kimmel et al., 2019; Benayoun et al., 2019; Lui et al., 2010; Shavlakadze et al., 2019; Almanzar et al., 2020; Schaum et al., 2020), emphasising the role in ageing of genes related to inflammatory responses, cell cycle, or the electron transport chain. However, while it is possible to monitor the modifications in gene expression over time in those species by sequencing transcriptomes of organs of littermates at different ages, as a close surrogate of longitudinality, such studies cannot be conducted in humans for ethical reasons. Indeed, most studies aimed at profiling ageing-related gene expression changes in human tissues focused on a single tissue (e.g. muscle Zahn et al., 2005; Welle et al., 2003; Jozsi et al., 2000, kidney Rodwell et al., 2004, brain Galatro et al., 2017; Olah et al., 2018; Berchtold et al., 2008; Lu et al., 2004; Işıldak et al., 2020, skin Haustead et al., 2016; Holzscheck et al., 2020, blood Nakamura et al., 2012; Harries et al., 2011, liver Thomas et al., 2002, retina Yoshida et al., 2002) and/or are limited to a comparison between young and old individuals (Welle et al., 2003; Jozsi et al., 2000; Haustead et al., 2016; Thomas et al., 2002; Yoshida et al., 2002), failing to fully capture the changes of the tissue-specific gene expression landscape throughout ageing (Stegeman and Weake, 2017). A few studies were nonetheless led on more than one tissue in humans, from post-mortem samples (Gheorghe et al., 2014; Yang et al., 2015) and biopsies (Aramillo Irizar et al., 2018; Glass et al., 2013), and in mice (Schaum et al., 2020; Aramillo Irizar et al., 2018) and rats (Yu et al., 2014). The age-related transcriptional profiles derived therein, either from regression (Schaum et al., 2020; Gheorghe et al., 2014; Yang et al., 2015; Glass et al., 2013) or comparison between age groups (Aramillo Irizar et al., 2018; Yu et al., 2014), highlight an asynchronous ageing of tissues (discussed in Rando and Wyss-Coray, 2021), with some of them more affected by age-related gene expression changes associated with biological mechanisms known to be impacted by ageing such as mitochondrial activity or metabolic homeostasis. In particular, tissue-specific periods of major transcriptional changes in the fifth and eighth decades of the human lifespan have been revealed (Gheorghe et al., 2014), reflecting the so-called digital ageing (Rando and Wyss-Coray, 2021), consistent with what is observed in mice (Almanzar et al., 2020; Schaum et al., 2020). Furthermore, despite outlining the tissue specificity of the transcriptomic signatures of human ageing, some synchronisation was found between tissues like the lung, heart, and whole blood, which exhibit a co-ageing pattern (Yang et al., 2015). Nevertheless, as each study followed its own specific procedures, from sample collection to data processing, results from these analyses are hard to compare with one another.

Processed data from those studies have not been made easily accessible and interpretable to researchers lacking computational proficiency but aiming to use them to test their novel hypotheses. To fill this void, we have developed voyAGEr, a web application providing flexible visualisation of comprehensive functional analyses of gene expression alterations occurring in 49 human tissues with age in each biological sex. We leverage the large RNA-seq dataset from the Genotype-Tissue Expression (GTEx) project (Lonsdale et al., 2013), encompassing tissue samples from hundreds of donors aged from 20 to 70 years, with a pipeline for gene expression profiling with an optimised temporal resolution. voyAGEr allows us to investigate ageing from two perspectives: (i) gene-centric – how each gene’s tissue-specific expression progresses with age; and (ii) tissue-centric – how tissue-specific transcriptomes change with age. Additionally, voyAGEr enables the examination of modules of co-expressed genes altered with age in four tissues (brain cortex, skeletal muscle, left ventricle of the heart, whole blood), namely their enrichment in specific cell types, biological pathways, and association with diseases. We, therefore, expect voyAGEr to become a valuable support tool for researchers aiming to uncover the molecular mechanisms underlying human ageing. Moreover, being open-source, voyAGEr can be adapted by fellow developers to be used with alternative datasets (e.g. from other species) or to incorporate other specific functionalities.

voyAGEr is freely available at https://compbio.imm.medicina.ulisboa.pt/app/voyAGEr.

voyAGEr’s interactive exploration of tissue-specific gene expression landscapes in ageing is based on sequential fitting of linear models (v. Methods) to estimate, for each gene in each tissue:

1. the Age effect, i.e., how the age-associated changes in gene expression evolve with age itself;

2. the Sex effect, i.e., how the differences in gene expression between sexes evolve with age;

3. the Age&Sex interaction effect, i.e., how the differences between sexes of age-associated changes in gene expression evolve with age.

We named our approach Shifting Age Range Pipeline for Linear Modelling (ShARP-LM). Briefly, this method consists of performing differential gene expression (with gene expression as a function of the donors’ Age, Sex, and Age&Sex interaction) in moving age windows spanning 16 years. By considering the percentage of genes altered in each age range, we can highlight age periods of major tissue-specific transcriptomic alterations (Figure 1).

Figure 1 with 3 supplements see all

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Gene-centric analyses of human tissue-specific expression changes across age

The progression of tissue-specific expression of a particular gene across age can be examined in voyAGEr’s Gene tab. By entering its HGNC symbol in the Gene selector, the user has access to graphical summaries of the gene’s tissue-specific expression (sub-tab Profile) (Figure 2A) and the significance of age-related changes in its expression due to the Age, Sex, and Age&Sex effects (sub-tab Alteration) (Figure 2B) across age. Results can be displayed in a heatmap for all tissues or in a scatter plot for a chosen individual tissue (Figure 2C). When the gene is studied in a single tissue, the user can graphically and statistically profile the association of the donors’ sex and reported conditions (e.g. history of heart attack or pneumonia) with the gene’s expression profile. A table summarizing the donors’ metadata is also shown (Figure 2C). The user can interactively select donors of interest on the scatter plot and further examine their information in the automatically subsetted table.

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An example of a process whose molecular mechanisms are of particular interest to researchers in the ageing field is cellular senescence. Senescence is a stress-induced cell cycle arrest limiting the proliferation of potentially oncogenic cells but progressively creating an inflammatory environment in tissues as they age (van Deursen, 2014; Gorgoulis et al., 2019. CDKN2A, that encodes cell cycle regulatory protein p16^INK4A known to accumulate in senescent cells Gil and Peters, 2006; Erickson et al., 1998), has its expression increased with age in the vast majority of tissues profiled (Figure 2—figure supplement 1A ). Similarly, reduced levels of proliferation markers, such as PCNA (Narita et al., 2003) and MKI67 (Sun and Kaufman, 2018), can be studied as putative markers of ageing of certain tissues. These genes have their expression altered with age in the lung and display a similar expression profile (decreasing from 25 to 30 years old, constant between 35 and 50 years old and decreasing in older ages) (Figure 2C). However, these trends appear to vary according to the donor’s history of non-metastatic cancer (Figure 2—figure supplement 1B), illustrating voyAGEr’s use in helping to find associations between gene expression and age-related diseases.

On a different note, sex biases have been reported in the expression of SALL1 and DDX43 in adipose tissue and lung, respectively (Kassam et al., 2019). voyAGEr allows us to not only recapitulate those observations but also assess the temporal window where these changes occur (Figure 2—figure supplement 2).

Tissue-specific assessment of gene expression changes across age

Peaks of gene expression alterations

The landscape of global tissue-specific gene expression alteration across age can be examined in voyAGEr’s Tissue main tab. A heatmap displaying, for all tissues, the statistical significance over age (v. Methods) of the proportion of genes altered with Age, Sex, or Age&Sex (depending on the user’s interest) is initially shown (Figure 3A), illustrating the aforementioned asynchronous ageing of tissues observed for humans and rodents (Schaum et al., 2020; Thomas et al., 2002; Yoshida et al., 2002; Gheorghe et al., 2014; Yang et al., 2015; Aramillo Irizar et al., 2018; Rando and Wyss-Coray, 2021).

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The user can then spot the age periods with the most significant gene expression alterations in a selected tissue (Figure 3B, a), and identify the associated altered genes (Figure 3b). The user can also plot the expression of a given gene of interest across ages together with the significance of its expression modification with Age, Sex, or Age&Sex (Figure 3B, c).

An example of voyAGEr’s capabilities is illustrated in Figure 1—figure supplement 1, showing substantial transcriptomic alterations in the uterus from the late forties to the early fifties, overlapping with the age distribution of menopausal onset (Kaczmarek, 2014), which could explain the observed molecular modifications.

It is also possible to visualise tissues with more than one period of transcriptomic changes, and to individually inspect these periods. As an example, the subcutaneous adipose tissue appears to go through two main periods of transcriptional changes with age: a major one at the late 20s (~13% of altered genes), and a minor one after 45 years (~5% of altered genes) (Figure 3B, A). The most altered genes in this first peak appear to have their expression modified only at this precise age period (e.g. PRELID1, RUNX1T1, TUBB4B, FGFRL1, and MALSU1). Similarly, mitochondrial genes (e.g. MT-CYB, MT-ND4, MT-ATP6, MT-ND2) (Figure 3B) appear to be the most altered genes in the second peak (Figure 3B, C). This particularity suggests that different sets of genes drive the periods of major transcriptional changes, which begs to assess if they reflect the activation of distinct biological processes.

Gene set enrichment

The user can explore the biological functions of the set of genes underlying each peak of transcriptomic changes by assessing their enrichment in curated pathways from the Reactome database (Croft et al., 2014). voyAGEr performs Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005) and the user can visualise heatmaps displaying the evolution over the age of the resulting normalised enrichment score (NES Subramanian et al., 2005, reflecting the degree to which a pathway is over- or under-represented in a subset of genes) for a given tissue, effect (Age, Sex, or Age&Sex) and Reactome pathway (all, or user-selected) (Figure 4A). To reduce redundancy and facilitate the understanding of their biological relevance, we clustered those pathways into families that also include Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Kanehisa and Goto, 2000) and Gene Ontology (GO) Biological Processes of level 3 (Gene Ontology Consortium, 2004). Briefly, we clustered gene sets from the three sources based on the overlap of their genes (v. Methods), thereby creating families of highly functionally related pathways. Taking advantage of the complementary and distinct terminology in Reactome, KEGG, and GO, the user can interpret each family’s broad biological function by looking at the word cloud of its most prevalent terms, and browsing the list of its associated pathways (Figure 4A). For example, although most pathways enriched in the two aforementioned peaks of altered genes in subcutaneous adipose tissue were different, there is an overlap of pathways related to metabolism, including various mitochondrial processes (Figure 4A). This highlights the importance of integrating individual gene data with pathway enrichment analysis to garner more comprehensive insights into the biological relevance of those changes.

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The peaks of transcriptomic changes can also be examined for enrichment in a user-provided gene set (Figure 4B). As expression of senescence-related CDKN2A is increased in the subcutaneous adipose tissue with age (Figure 2—figure supplement 1A), we hypothesised that other senescence-associated genes may be augmented too. Thus we used that voyAGEr functionality, using the Senequest (Gorgoulis et al., 2019) geneset (supported by at least four sources) to test it, observing no significant alterations (Figure 4B).

Modules of co-expressed genes

voyAGEr also allows functional analyses of modules of co-expressed genes i.e. genes with highly correlated expression across samples, defined by weighted correlation network analysis (Langfelder and Horvath, 2008). Genes in the same module are likely to be co-regulated and share biological functions or associations with phenotypical or pathological traits (van Dam et al., 2017). Those modules may also act as markers of core transcriptional features of cellular activity and identity (Kelley et al., 2018).

Concretely, voyAGEr enables the user to visualise how the expression of modules of genes that are associated with a specific cell type, biological pathway, or disease progresses over age in a specific tissue. After selecting one tissue of interest, the user has, for each module, access to four levels of information:

1. Expression: its eigengene expression progression over age (Figure 5C);

2. Cell types: its enrichment in specific cell types, based on cell type signatures found in the literature (Figure 5C);

3. Pathways: its enrichment in Reactome pathways;

4. Diseases: its enrichment in disease markers, based on gene-disease associations from DisGeNET (Piñero et al., 2019; Piñero et al., 2017), calculated with both the disgenet2r package (Piñero et al., 2019) and with Fisher’s tests (Figure 5B).

Figure 5

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By default, for each tissue, results are displayed in the form of heatmaps of expression (centered and scaled) illustrating how all modules evolve with age (Figure 5A). The user also has the possibility to select a module of interest and see its eigengene progression over age in a scatter plot (Figure 5C), lists of its association with diseases and pathways ordered by significance, and a TreeMap for its cell type enrichments (Figure 5C). In the example of Figure 5C, the ‘MEblue’ module, comprising 241 genes co-expressed in the brain cortex, shows significant enrichment in astrocyte markers. The apparent increase of this module’s expression with age may reflect the known age-related changes in astrocyte activation (Palmer and Ousman, 2018) and the relative weakening of neuronal activity (‘MEyellow’ and ‘MEpink’ modules).

As in the Gene tab, the user can separate donors based on their sex and associated medical conditions in the scatter plot of the eigengene expression progression. On the Pathways and Diseases-Manual tabs below the main plot, the user can also visualise the contingency table from that specific disease/pathway, on the corresponding column.

voyAGEr provides a framework to examine the progression of gene expression over age in several human tissues, serving as a valuable resource for the ageing research community. In particular, it helps to identify tissue-specific age periods of major transcriptomic alterations. The results of our analyses show the complexity of human biological ageing by stressing its tissue specificity (Gheorghe et al., 2014) and non-linear transcriptional progression throughout the lifetime, consistent with previous results from both proteomic (Lehallier et al., 2019) and transcriptomic (Haustead et al., 2016; Gheorghe et al., 2014) analyses. By revealing and annotating the age-specific transcriptional trends in each tissue, voyAGEr aims to assist researchers in deciphering the cellular and molecular mechanisms associated with the age-related physiological decline across the human body.

Due to the tissue-specific nature of the pre-processing steps (v. Read count data pre-processing in the Methods section), and given that most of the plotted gene expression distributions are centered and scaled by tissue, it is important to note that voyAGEr may not be always suited for direct comparisons between different tissues. For instance, it does not allow us to directly ascertain if a gene exhibits different expression levels in different tissues or if the expression of a particular gene in one tissue changes more drastically with age than in another tissue. Furthermore, we must emphasise that the majority of GTEx donors contributed samples to multiple tissues (Figure 1—figure supplement 1 ), potentially introducing biases and confounders when comparing gene expression patterns between tissues. Our analyses of variance (Figure 1—figure supplement 1B ) and downsampling to control for common donors (Figure 1—figure supplement 1C-E) suggest very limited global confounding between the impacts of donor and age on gene expression and that any potential cross-tissue bias not to depend much on the proportion of common donors (Figure 1—figure supplement 1E). However, this effect must be taken into account when comparing specific pairs of tissues (e.g. Colon – Transverse and Whole Blood, Figure 1—figure supplement 1D).

Additionally, voyAGEr allows us to scrutinise and visually display the tissue-specific differences in gene expression between biological sexes across ages. Biological sex is an important factor in the prevalence of ageing-associated diseases, as well as their age of onset, progression (Tower, 2017; Austad and Fischer, 2016; Khramtsova et al., 2019), and sex-related differences in gene expression (Melé et al., 2015; Gershoni and Pietrokovski, 2017; Kassam et al., 2019; Mayne et al., 2016). By profiling the age distribution of such differences, voyAGEr can lead to a better understanding of their influence in the aetiology of the sex specificities of human ageing. For instance, we were able to corroborate findings on the sex-differential transcriptome of adult humans by Gershoni and Pietrokovski, 2017, with voyAGEr emphasising its tissue-specificity and allowing to discriminate the ages at which sex-related biases appear to be more prevalent (Figure 3—figure supplement 2).

Nonetheless, it is essential to interpret sex chromosome-specific gene results in voyAGEr with caution. For instance, we observed elevated expression of Y-chromosome-specific DDX3Y in males, whilst its female expression (expected to be zero) is very low, in the range of what can be considered background noise (Figure 2—figure supplement 3). Its age-related alterations exhibit a distinctive peak around the age of 40 apparently driven by subtle changes in gene expression in female samples, illustrating the need for the abovementioned caution.

One of the limitations of voyAGEr is that most GTEx tissue donors had health conditions and their frequency increased with age, preventing us from defining a class of healthy individuals and identifying age-associated transcriptomic changes that could be more confidently proposed to happen independently of any disease progression. Whilst the large sample sizes and inherent biological variability among individuals, reflected in the diversity of condition combinations, are expected to mitigate significant confounding effects, voyAGEr also allows users to evaluate how tissue-specific gene expression trends vary according to the donors’ diverse conditions (see Figure 2—figure supplement 1B).

The development of voyAGEr was accompanied by that of a pipeline, ShARP-LM, that facilitates the holistic depiction of the transcriptional landscapes of adult human tissues throughout ageing with a yearly age resolution. We take advantage of the comprehensiveness of the transcriptome collection from human tissues from the GTEx project to make our analyses a valid surrogate of a currently undoable longitudinal study. It confers our method enough statistical robustness to mitigate the inter-individual differences and deal with the non-uniform distribution of the donors’ ages. Nevertheless, it is worth highlighting that the age distribution of donors does impact the statistical power for detecting transcriptional changes. Consequently, we are more likely to identify significant alterations (with p-value <0.05 in our gene-centric analyses) within age ranges that are more prevalent in our sample population, often characterised by older individuals (Figure 3—figure supplement 3). When downsampling to ensure a balanced age distribution, a loss of statistical power is apparent but a considerable positive correlation with the original results is maintained and a substantial number of significant alterations remain so (Figure 3—figure supplement 4). This limitation is likely to be overcome by the accumulation of transcriptomes of human tissues in public databases, promising a gradual increase in accuracy and age resolution with which human transcriptomic ageing can be profiled. Similarly, the expanding collection of single-cell transcriptomes in public databases is yielding improved gene markers for an increasing diversity of human cell types, enhancing the usefulness of leveraging bulk transcriptomes to study the impact of ageing on the cellular composition of human tissues, for which the co-expression module approach in voyAGEr provides a proof-of-concept.

Nonetheless, it is imperative to approach the module-based analysis with caution, as direct and literal interpretations may be misleading. For instance, it is not uncommon to observe an enrichment of ‘Rheumatoid Arthritis’ in modules associated with various immune cell types in anatomical locations, such as the brain cortex, where the disease does not directly manifest. If a specific module associated with a condition like ‘Liver Cirrhosis’ exhibits an age-related increase in the brain cortex, of course, this does not mean such disease ever occurs within older brains. Nevertheless, we consider that the module-based approach can serve as a valuable resource for generating hypotheses.

Given its open-source nature, voyAGEr is envisaged to be a continually evolving resource, able to accommodate new data and expand its functionalities, namely by incorporating additional tissues into the modules section and integrating perturbagen data for inference of molecular causes underlying observed gene expression alterations and small molecules to target them for therapeutic purposes (Subramanian et al., 2017; Saraiva-Agostinho and de Almeida, 2020).

As an in silico approach with no experimental validation for its results, voyAGEr is meant to be a discovery tool, supporting biologists in the exploration of a large transcriptomic dataset, thereby generating, refining, or preliminary testing hypotheses, laying the groundwork for subsequent experimental research. It can be an entry point for projects aiming at better understanding the tissue- and sex-specific transcriptional alterations underlying human ageing, to be followed by targeted studies focusing on the functional roles of the most promising markers identified therein in the physiology of ageing. Those marker genes can contribute to the development of more robust and cross-tissue gene signatures of ageing (de Magalhães et al., 2009) and the expansion of age-related gene databases (Tacutu et al., 2018; Craig et al., 2015).

Moreover, the observed diverse and asynchronous changes in gene expression between tissues over the human adult life provide potentially relevant information for the design of accurate diagnostic tools and personalised therapies. On one hand, identifying those changes’ association with specific disorders could have a prognostic value by enabling the identification of their onset before clinical symptoms manifest (Aramillo Irizar et al., 2018). On the other hand, computational screening of databases of genetic and pharmacologically induced human transcriptomic changes could help to infer their molecular causes and uncover candidate drugs to delay these effects (Subramanian et al., 2017; Saraiva-Agostinho and de Almeida, 2020; Dönertaş et al., 2018; Janssens et al., 2019).

No data has been generated for this manuscript. The data used for the analyses described in this manuscript were obtained from the GTEx Portal (https://www.gtexportal.org/) and dbGaP accession number phs000424.v9.p2 on 06/12/2023. The complete source code for data processing and Shiny interface is available on GitHub: https://github.com/DiseaseTranscriptomicsLab/voyAGEr.

1. 1. The GTEx Consortium

   (2022) NCBI Gene Expression Omnibus

   ID phs000424.v9.p2. Common Fund (CF) Genotype-Tissue Expression Project (GTEx).

   https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v9.p2

1. 1. Almanzar N
   2. Antony J
   3. Baghel AS
   4. Bakerman I
   5. Bansal I
   6. Barres BA
   7. Beachy PA
   8. Berdnik D
   9. Bilen B
   10. Brownfield D
   11. Cain C
   12. Chan CKF
   13. Chen MB
   14. Clarke MF
   15. Conley SD
   16. Darmanis S
   17. Demers A
   18. Demir K
   19. de Morree A
   20. Divita T
   21. du Bois H
   22. Ebadi H
   23. Espinoza FH
   24. Fish M
   25. Gan Q
   26. George BM
   27. Gillich A
   28. Gòmez-Sjöberg R
   29. Green F
   30. Genetiano G
   31. Gu X
   32. Gulati GS
   33. Hahn O
   34. Haney MS
   35. Hang Y
   36. Harris L
   37. He M
   38. Hosseinzadeh S
   39. Huang A
   40. Huang KC
   41. Iram T
   42. Isobe T
   43. Ives F
   44. Jones RC
   45. Kao KS
   46. Karkanias J
   47. Karnam G
   48. Keller A
   49. Kershner AM
   50. Khoury N
   51. Kim SK
   52. Kiss BM
   53. Kong W
   54. Krasnow MA
   55. Kumar ME
   56. Kuo CS
   57. Lam J
   58. Lee DP
   59. Lee SE
   60. Lehallier B
   61. Leventhal O
   62. Li G
   63. Li Q
   64. Liu L
   65. Lo A
   66. Lu WJ
   67. Lugo-Fagundo MF
   68. Manjunath A
   69. May AP
   70. Maynard A
   71. McGeever A
   72. McKay M
   73. McNerney MW
   74. Merrill B
   75. Metzger RJ
   76. Mignardi M
   77. Min D
   78. Nabhan AN
   79. Neff NF
   80. Ng KM
   81. Nguyen PK
   82. Noh J
   83. Nusse R
   84. Pálovics R
   85. Patkar R
   86. Peng WC
   87. Penland L
   88. Pisco AO
   89. Pollard K
   90. Puccinelli R
   91. Qi Z
   92. Quake SR
   93. Rando TA
   94. Rulifson EJ
   95. Schaum N
   96. Segal JM
   97. Sikandar SS
   98. Sinha R
   99. Sit RV
   100. Sonnenburg J
   101. Staehli D
   102. Szade K
   103. Tan M
   104. Tan W
   105. Tato C
   106. Tellez K
   107. Dulgeroff LBT
   108. Travaglini KJ
   109. Tropini C
   110. Tsui M
   111. Waldburger L
   112. Wang BM
   113. van Weele LJ
   114. Weinberg K
   115. Weissman IL
   116. Wosczyna MN
   117. Wu SM
   118. Wyss-Coray T
   119. Xiang J
   120. Xue S
   121. Yamauchi KA
   122. Yang AC
   123. Yerra LP
   124. Youngyunpipatkul J
   125. Yu B
   126. Zanini F
   127. Zardeneta ME
   128. Zee A
   129. Zhao C
   130. Zhang F
   131. Zhang H
   132. Zhang MJ
   133. Zhou L
   134. Zou J
   135. The Tabula Muris Consortium

   (2020) A single-cell transcriptomic atlas characterizes ageing tissues in the mouse

   Nature 583:590–595.

   https://doi.org/10.1038/s41586-020-2496-1

   • PubMed
   • Google Scholar
2. 1. Aramillo Irizar P
   2. Schäuble S
   3. Esser D
   4. Groth M
   5. Frahm C
   6. Priebe S
   7. Baumgart M
   8. Hartmann N
   9. Marthandan S
   10. Menzel U
   11. Müller J
   12. Schmidt S
   13. Ast V
   14. Caliebe A
   15. König R
   16. Krawczak M
   17. Ristow M
   18. Schuster S
   19. Cellerino A
   20. Diekmann S
   21. Englert C
   22. Hemmerich P
   23. Sühnel J
   24. Guthke R
   25. Witte OW
   26. Platzer M
   27. Ruppin E
   28. Kaleta C

   (2018) Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly

   Nature Communications 9:327.

   https://doi.org/10.1038/s41467-017-02395-2

   • PubMed
   • Google Scholar
3. 1. Austad SN
   2. Fischer KE

   (2016) Sex Differences in Lifespan

   Cell Metabolism 23:1022–1033.

   https://doi.org/10.1016/j.cmet.2016.05.019

   • PubMed
   • Google Scholar
4. 1. Benayoun BA
   2. Pollina EA
   3. Singh PP
   4. Mahmoudi S
   5. Harel I
   6. Casey KM
   7. Dulken BW
   8. Kundaje A
   9. Brunet A

   (2019) Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses

   Genome Research 29:697–709.

   https://doi.org/10.1101/gr.240093.118

   • PubMed
   • Google Scholar
5. 1. Berchtold NC
   2. Cribbs DH
   3. Coleman PD
   4. Rogers J
   5. Head E
   6. Kim R
   7. Beach T
   8. Miller C
   9. Troncoso J
   10. Trojanowski JQ
   11. Zielke HR
   12. Cotman CW

   (2008) Gene expression changes in the course of normal brain aging are sexually dimorphic

   PNAS 105:15605–15610.

   https://doi.org/10.1073/pnas.0806883105

   • Google Scholar
6. 1. Buuren SV
   2. Groothuis-Oudshoorn K

   (2011) mice: multivariate imputation by chained equations in R

   Journal of Statistical Software 45:i03.

   https://doi.org/10.18637/jss.v045.i03

   • Google Scholar
7. 1. Campisi JA

   (2013) Aging, cellular senescence, and cancer

   Annual Review of Physiology 75:685–705.

   https://doi.org/10.1146/annurev-physiol-030212-183653

   • PubMed
   • Google Scholar
8. 1. Cao J
   2. O’Day DR
   3. Pliner HA
   4. Kingsley PD
   5. Deng M
   6. Daza RM
   7. Zager MA
   8. Aldinger KA
   9. Blecher-Gonen R
   10. Zhang F
   11. Spielmann M
   12. Palis J
   13. Doherty D
   14. Steemers FJ
   15. Glass IA
   16. Trapnell C
   17. Shendure J

   (2020) A human cell atlas of fetal gene expression

   Science 370:eaba7721.

   https://doi.org/10.1126/science.aba7721

   • PubMed
   • Google Scholar
9. Software

   1. Chang W
   2. Cheng J
   3. Allaire J
   4. Sievert C
   5. Schloerke B
   6. Xie Y
   7. Allen J
   8. McPherson J
   9. Dipert A
   10. Borges B

   (2024) Shiny: web application framework for R, version R package version 1.8.1

   GitHub.

   https://github.com/rstudio/shiny
10. 1. Chen Y-A
    2. Tripathi LP
    3. Dessailly BH
    4. Nyström-Persson J
    5. Ahmad S
    6. Mizuguchi K

    (2014) Integrated pathway clusters with coherent biological themes for target prioritisation

    PLOS ONE 9:e99030.

    https://doi.org/10.1371/journal.pone.0099030

    • PubMed
    • Google Scholar
11. Book

    1. Chen B
    2. Khodadoust MS
    3. Liu CL
    4. Newman AM
    5. Alizadeh AA

    (2018) Profiling tumor infiltrating immune cells with CIBERSORT

    In: Von L, editors. Cancer Systems Biology. Springer. pp. 243–259.

    https://doi.org/10.1007/978-1-4939-7493-1

    • Google Scholar
12. 1. Craig T
    2. Smelick C
    3. Tacutu R
    4. Wuttke D
    5. Wood SH
    6. Stanley H
    7. Janssens G
    8. Savitskaya E
    9. Moskalev A
    10. Arking R
    11. de Magalhães JP

    (2015) The Digital Ageing Atlas: integrating the diversity of age-related changes into a unified resource

    Nucleic Acids Research 43:D873–D878.

    https://doi.org/10.1093/nar/gku843

    • Google Scholar
13. 1. Croft D
    2. Mundo AF
    3. Haw R
    4. Milacic M
    5. Weiser J
    6. Wu G
    7. Caudy M
    8. Garapati P
    9. Gillespie M
    10. Kamdar MR
    11. Jassal B
    12. Jupe S
    13. Matthews L
    14. May B
    15. Palatnik S
    16. Rothfels K
    17. Shamovsky V
    18. Song H
    19. Williams M
    20. Birney E
    21. Hermjakob H
    22. Stein L
    23. D’Eustachio P

    (2014) The Reactome pathway knowledgebase

    Nucleic Acids Research 42:D472–D477.

    https://doi.org/10.1093/nar/gkt1102

    • PubMed
    • Google Scholar
14. 1. de Magalhães JP
    2. Curado J
    3. Church GM

    (2009) Meta-analysis of age-related gene expression profiles identifies common signatures of aging

    Bioinformatics 25:875–881.

    https://doi.org/10.1093/bioinformatics/btp073

    • PubMed
    • Google Scholar
15. 1. Dönertaş HM
    2. Fuentealba Valenzuela M
    3. Partridge L
    4. Thornton JM

    (2018) Gene expression‐based drug repurposing to target aging

    Aging Cell 17:e12819.

    https://doi.org/10.1111/acel.12819

    • Google Scholar
16. 1. Erickson S
    2. Sangfelt O
    3. Heyman M
    4. Castro J
    5. Einhorn S
    6. Grandér D

    (1998) Involvement of the Ink4 proteins p16 and p15 in T-lymphocyte senescence

    Oncogene 17:595–602.

    https://doi.org/10.1038/sj.onc.1201965

    • PubMed
    • Google Scholar
17. 1. Fan X
    2. Dong J
    3. Zhong S
    4. Wei Y
    5. Wu Q
    6. Yan L
    7. Yong J
    8. Sun L
    9. Wang X
    10. Zhao Y
    11. Wang W
    12. Yan J
    13. Wang X
    14. Qiao J
    15. Tang F

    (2018) Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis

    Cell Research 28:730–745.

    https://doi.org/10.1038/s41422-018-0053-3

    • PubMed
    • Google Scholar
18. 1. Galatro TF
    2. Holtman IR
    3. Lerario AM
    4. Vainchtein ID
    5. Brouwer N
    6. Sola PR
    7. Veras MM
    8. Pereira TF
    9. Leite REP
    10. Möller T
    11. Wes PD
    12. Sogayar MC
    13. Laman JD
    14. den Dunnen W
    15. Pasqualucci CA
    16. Oba-Shinjo SM
    17. Boddeke EWGM
    18. Marie SKN
    19. Eggen BJL

    (2017) Transcriptomic analysis of purified human cortical microglia reveals age-associated changes

    Nature Neuroscience 20:1162–1171.

    https://doi.org/10.1038/nn.4597

    • PubMed
    • Google Scholar
19. 1. García-Pérez R
    2. Ramirez JM
    3. Ripoll-Cladellas A
    4. Chazarra-Gil R
    5. Oliveros W
    6. Soldatkina O
    7. Bosio M
    8. Rognon PJ
    9. Capella-Gutierrez S
    10. Calvo M
    11. Reverter F
    12. Guigó R
    13. Aguet F
    14. Ferreira PG
    15. Ardlie KG
    16. Melé M

    (2023) The landscape of expression and alternative splicing variation across human traits

    Cell Genomics 3:100244.

    https://doi.org/10.1016/j.xgen.2022.100244

    • PubMed
    • Google Scholar
20. 1. Gene Ontology Consortium

    (2004) The Gene Ontology (GO) database and informatics resource

    Nucleic Acids Research 32:258D–261.

    https://doi.org/10.1093/nar/gkh036

    • Google Scholar
21. 1. Gershoni M
    2. Pietrokovski S

    (2017) The landscape of sex-differential transcriptome and its consequent selection in human adults

    BMC Biology 15:7.

    https://doi.org/10.1186/s12915-017-0352-z

    • PubMed
    • Google Scholar
22. 1. Gheorghe M
    2. Snoeck M
    3. Emmerich M
    4. Bäck T
    5. Goeman JJ
    6. Raz V

    (2014) Major aging-associated RNA expressions change at two distinct age-positions

    BMC Genomics 15:132.

    https://doi.org/10.1186/1471-2164-15-132

    • Google Scholar
23. 1. Gil J
    2. Peters G

    (2006) Regulation of the INK4b-ARF-INK4a tumour suppressor locus: all for one or one for all

    Nature Reviews. Molecular Cell Biology 7:667–677.

    https://doi.org/10.1038/nrm1987

    • PubMed
    • Google Scholar
24. 1. Glass D
    2. Viñuela A
    3. Davies MN
    4. Ramasamy A
    5. Parts L
    6. Knowles D
    7. Brown AA
    8. Hedman AK
    9. Small KS
    10. Buil A
    11. Grundberg E
    12. Nica AC
    13. Di Meglio P
    14. Nestle FO
    15. Ryten M
    16. UK Brain Expression consortium
    17. MuTHER consortium
    18. Durbin R
    19. McCarthy MI
    20. Deloukas P
    21. Dermitzakis ET
    22. Weale ME
    23. Bataille V
    24. Spector TD

    (2013) Gene expression changes with age in skin, adipose tissue, blood and brain

    Genome Biology 14:R75.

    https://doi.org/10.1186/gb-2013-14-7-r75

    • PubMed
    • Google Scholar
25. 1. Gorgoulis V
    2. Adams PD
    3. Alimonti A
    4. Bennett DC
    5. Bischof O
    6. Bishop C
    7. Campisi J
    8. Collado M
    9. Evangelou K
    10. Ferbeyre G
    11. Gil J
    12. Hara E
    13. Krizhanovsky V
    14. Jurk D
    15. Maier AB
    16. Narita M
    17. Niedernhofer L
    18. Passos JF
    19. Robbins PD
    20. Schmitt CA
    21. Sedivy J
    22. Vougas K
    23. von Zglinicki T
    24. Zhou D
    25. Serrano M
    26. Demaria M

    (2019) Cellular senescence: defining a path forward

    Cell 179:813–827.

    https://doi.org/10.1016/j.cell.2019.10.005

    • PubMed
    • Google Scholar
26. Book

    1. GSEA-MSIGDB

    (2023)

    Gene Set Enrichment Analysis (GSEA) User Guide

    Broad institute.

    • Google Scholar
27. 1. Harries LW
    2. Hernandez D
    3. Henley W
    4. Wood AR
    5. Holly AC
    6. Bradley-Smith RM
    7. Yaghootkar H
    8. Dutta A
    9. Murray A
    10. Frayling TM
    11. Guralnik JM
    12. Bandinelli S
    13. Singleton A
    14. Ferrucci L
    15. Melzer D

    (2011) Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing

    Aging Cell 10:868–878.

    https://doi.org/10.1111/j.1474-9726.2011.00726.x

    • PubMed
    • Google Scholar
28. 1. Haustead DJ
    2. Stevenson A
    3. Saxena V
    4. Marriage F
    5. Firth M
    6. Silla R
    7. Martin L
    8. Adcroft KF
    9. Rea S
    10. Day PJ
    11. Melton P
    12. Wood FM
    13. Fear MW

    (2016) Transcriptome analysis of human ageing in male skin shows mid-life period of variability and central role of NF-κB

    Scientific Reports 6:26846.

    https://doi.org/10.1038/srep26846

    • PubMed
    • Google Scholar
29. 1. Holzscheck N
    2. Söhle J
    3. Kristof B
    4. Grönniger E
    5. Gallinat S
    6. Wenck H
    7. Winnefeld M
    8. Falckenhayn C
    9. Kaderali L

    (2020) Multi-omics network analysis reveals distinct stages in the human aging progression in epidermal tissue

    Aging 12:12393–12409.

    https://doi.org/10.18632/aging.103499

    • PubMed
    • Google Scholar
30. 1. Işıldak U
    2. Somel M
    3. Thornton JM
    4. Dönertaş HM

    (2020) Temporal changes in the gene expression heterogeneity during brain development and aging

    Scientific Reports 10:4080.

    https://doi.org/10.1038/s41598-020-60998-0

    • PubMed
    • Google Scholar
31. 1. Janssens GE
    2. Lin X-X
    3. Millan-Ariño L
    4. Kavšek A
    5. Sen I
    6. Seinstra RI
    7. Stroustrup N
    8. Nollen EAA
    9. Riedel CG

    (2019) Transcriptomics-based screening identifies pharmacological inhibition of Hsp90 as a means to defer aging

    Cell Reports 27:467–480.

    https://doi.org/10.1016/j.celrep.2019.03.044

    • PubMed
    • Google Scholar
32. 1. Jozsi AC
    2. Dupont-Versteegden EE
    3. Taylor-Jones JM
    4. Evans WJ
    5. Trappe TA
    6. Campbell WW
    7. Peterson CA

    (2000) Aged human muscle demonstrates an altered gene expression profile consistent with an impaired response to exercise

    Mechanisms of Ageing and Development 120:45–56.

    https://doi.org/10.1016/s0047-6374(00)00178-0

    • PubMed
    • Google Scholar
33. Book

    1. Kaczmarek M

    (2014) Variation in age at natural menopause among polish women in relation to biological and social factors

    Plantin Publ. & Press Ltd.

    https://doi.org/10.13140/2.1.2610.1449

    • Google Scholar
34. 1. Kanehisa M
    2. Goto S

    (2000) KEGG: kyoto encyclopedia of genes and genomes

    Nucleic Acids Research 28:27–30.

    https://doi.org/10.1093/nar/28.1.27

    • PubMed
    • Google Scholar
35. 1. Kassam I
    2. Wu Y
    3. Yang J
    4. Visscher PM
    5. McRae AF

    (2019) Tissue-specific sex differences in human gene expression

    Human Molecular Genetics 28:2976–2986.

    https://doi.org/10.1093/hmg/ddz090

    • PubMed
    • Google Scholar
36. 1. Kelley KW
    2. Nakao-Inoue H
    3. Molofsky AV
    4. Oldham MC

    (2018) Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

    Nature Neuroscience 21:1171–1184.

    https://doi.org/10.1038/s41593-018-0216-z

    • Google Scholar
37. 1. Khramtsova EA
    2. Davis LK
    3. Stranger BE

    (2019) The role of sex in the genomics of human complex traits

    Nature Reviews. Genetics 20:173–190.

    https://doi.org/10.1038/s41576-018-0083-1

    • PubMed
    • Google Scholar
38. 1. Kimmel JC
    2. Penland L
    3. Rubinstein ND
    4. Hendrickson DG
    5. Kelley DR
    6. Rosenthal AZ

    (2019) Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging

    Genome Research 29:2088–2103.

    https://doi.org/10.1101/gr.253880.119

    • PubMed
    • Google Scholar
39. Preprint

    1. Korotkevich G
    2. Sukhov V
    3. Budin N
    4. Shpak B
    5. Artyomov MN
    6. Sergushichev A

    (2021) Fast gene set enrichment analysis

    bioRxiv.

    https://doi.org/10.1101/060012

    • Google Scholar
40. Software

    1. Kunst J

    (2022) Hightchater: A wrapper for the ‘Highcharts’ library, version 0.9

    Rpackage.

    https://jkunst.com/highcharter/
41. 1. Langfelder P
    2. Horvath S

    (2008) WGCNA: an R package for weighted correlation network analysis

    BMC Bioinformatics 9:559.

    https://doi.org/10.1186/1471-2105-9-559

    • PubMed
    • Google Scholar
42. Software

    1. Langfelder P
    2. Horvath S

    (2024)

    WGCNA frequently asked questions

    Metwarebio.
43. 1. Law CW
    2. Chen Y
    3. Shi W
    4. Smyth GK

    (2014) voom: Precision weights unlock linear model analysis tools for RNA-seq read counts

    Genome Biology 15:R29.

    https://doi.org/10.1186/gb-2014-15-2-r29

    • PubMed
    • Google Scholar
44. 1. Lehallier B
    2. Gate D
    3. Schaum N
    4. Nanasi T
    5. Lee SE
    6. Yousef H
    7. Moran Losada P
    8. Berdnik D
    9. Keller A
    10. Verghese J
    11. Sathyan S
    12. Franceschi C
    13. Milman S
    14. Barzilai N
    15. Wyss-Coray T

    (2019) Undulating changes in human plasma proteome profiles across the lifespan

    Nature Medicine 25:1843–1850.

    https://doi.org/10.1038/s41591-019-0673-2

    • PubMed
    • Google Scholar
45. 1. Lonsdale J
    2. Thomas J
    3. Salvatore M
    4. Phillips R
    5. Lo E
    6. Shad S
    7. Hasz R
    8. Walters G
    9. Garcia F
    10. Young N
    11. Foster B
    12. Moser M
    13. Karasik E
    14. Gillard B
    15. Ramsey K
    16. Sullivan S
    17. Bridge J
    18. Magazine H
    19. Syron J
    20. Fleming J
    21. Siminoff L
    22. Traino H
    23. Mosavel M
    24. Barker L
    25. Jewell S
    26. Rohrer D
    27. Maxim D
    28. Filkins D
    29. Harbach P
    30. Cortadillo E
    31. Berghuis B
    32. Turner L
    33. Hudson E
    34. Feenstra K
    35. Sobin L
    36. Robb J
    37. Branton P
    38. Korzeniewski G
    39. Shive C
    40. Tabor D
    41. Qi L
    42. Groch K
    43. Nampally S
    44. Buia S
    45. Zimmerman A
    46. Smith A
    47. Burges R
    48. Robinson K
    49. Valentino K
    50. Bradbury D
    51. Cosentino M
    52. Diaz-Mayoral N
    53. Kennedy M
    54. Engel T
    55. Williams P
    56. Erickson K
    57. Ardlie K
    58. Winckler W
    59. Getz G
    60. DeLuca D
    61. MacArthur D
    62. Kellis M
    63. Thomson A
    64. Young T
    65. Gelfand E
    66. Donovan M
    67. Meng Y
    68. Grant G
    69. Mash D
    70. Marcus Y
    71. Basile M
    72. Liu J
    73. Zhu J
    74. Tu Z
    75. Cox NJ
    76. Nicolae DL
    77. Gamazon ER
    78. Im HK
    79. Konkashbaev A
    80. Pritchard J
    81. Stevens M
    82. Flutre T
    83. Wen X
    84. Dermitzakis ET
    85. Lappalainen T
    86. Guigo R
    87. Monlong J
    88. Sammeth M
    89. Koller D
    90. Battle A
    91. Mostafavi S
    92. McCarthy M
    93. Rivas M
    94. Maller J
    95. Rusyn I
    96. Nobel A
    97. Wright F
    98. Shabalin A
    99. Feolo M
    100. Sharopova N
    101. Sturcke A
    102. Paschal J
    103. Anderson JM
    104. Wilder EL
    105. Derr LK
    106. Green ED
    107. Struewing JP
    108. Temple G
    109. Volpi S
    110. Boyer JT
    111. Thomson EJ
    112. Guyer MS
    113. Ng C
    114. Abdallah A
    115. Colantuoni D
    116. Insel TR
    117. Koester SE
    118. Little AR
    119. Bender PK
    120. Lehner T
    121. Yao Y
    122. Compton CC
    123. Vaught JB
    124. Sawyer S
    125. Lockhart NC
    126. Demchok J
    127. Moore HF

    (2013) The Genotype-Tissue Expression (GTEx) project

    Nature Genetics 45:580–585.

    https://doi.org/10.1038/ng.2653

    • PubMed
    • Google Scholar
46. 1. López-Otín C
    2. Blasco MA
    3. Partridge L
    4. Serrano M
    5. Kroemer G

    (2013) The hallmarks of aging

    Cell 153:1194–1217.

    https://doi.org/10.1016/j.cell.2013.05.039

    • PubMed
    • Google Scholar
47. 1. Lu T
    2. Pan Y
    3. Kao S-Y
    4. Li C
    5. Kohane I
    6. Chan J
    7. Yankner BA

    (2004) Gene regulation and DNA damage in the ageing human brain

    Nature 429:883–891.

    https://doi.org/10.1038/nature02661

    • PubMed
    • Google Scholar
48. 1. Lui JC
    2. Chen W
    3. Barnes KM
    4. Baron J

    (2010) Changes in gene expression associated with aging commonly originate during juvenile growth

    Mechanisms of Ageing and Development 131:641–649.

    https://doi.org/10.1016/j.mad.2010.08.010

    • PubMed
    • Google Scholar
49. 1. Mayne BT
    2. Bianco-Miotto T
    3. Buckberry S
    4. Breen J
    5. Clifton V
    6. Shoubridge C
    7. Roberts CT

    (2016) Large scale gene expression meta-analysis reveals tissue-specific, sex-biased gene expression in humans

    Frontiers in Genetics 7:183.

    https://doi.org/10.3389/fgene.2016.00183

    • PubMed
    • Google Scholar
50. 1. McCall MN
    2. Illei PB
    3. Halushka MK

    (2016) Complex sources of variation in tissue expression data: analysis of the gtex lung transcriptome

    American Journal of Human Genetics 99:624–635.

    https://doi.org/10.1016/j.ajhg.2016.07.007

    • PubMed
    • Google Scholar
51. 1. Melé M
    2. Ferreira PG
    3. Reverter F
    4. DeLuca DS
    5. Monlong J
    6. Sammeth M
    7. Young TR
    8. Goldmann JM
    9. Pervouchine DD
    10. Sullivan TJ
    11. Johnson R
    12. Segrè AV
    13. Djebali S
    14. Niarchou A
    15. GTEx Consortium
    16. Wright FA
    17. Lappalainen T
    18. Calvo M
    19. Getz G
    20. Dermitzakis ET
    21. Ardlie KG
    22. Guigó R

    (2015) Human genomics: the human transcriptome across tissues and individuals

    Science 348:660–665.

    https://doi.org/10.1126/science.aaa0355

    • PubMed
    • Google Scholar
52. 1. Nakamura S
    2. Kawai K
    3. Takeshita Y
    4. Honda M
    5. Takamura T
    6. Kaneko S
    7. Matoba R
    8. Matsubara K

    (2012) Identification of blood biomarkers of aging by transcript profiling of whole blood

    Biochemical and Biophysical Research Communications 418:313–318.

    https://doi.org/10.1016/j.bbrc.2012.01.018

    • PubMed
    • Google Scholar
53. 1. Narita M
    2. Nũnez S
    3. Heard E
    4. Narita M
    5. Lin AW
    6. Hearn SA
    7. Spector DL
    8. Hannon GJ
    9. Lowe SW

    (2003) Rb-mediated heterochromatin formation and silencing of E2F target genes during cellular senescence

    Cell 113:703–716.

    https://doi.org/10.1016/s0092-8674(03)00401-x

    • PubMed
    • Google Scholar
54. 1. Niccoli T
    2. Partridge L

    (2012) Ageing as a risk factor for disease

    Current Biology 22:R741–R752.

    https://doi.org/10.1016/j.cub.2012.07.024

    • PubMed
    • Google Scholar
55. 1. Olah M
    2. Patrick E
    3. Villani A-C
    4. Xu J
    5. White CC
    6. Ryan KJ
    7. Piehowski P
    8. Kapasi A
    9. Nejad P
    10. Cimpean M
    11. Connor S
    12. Yung CJ
    13. Frangieh M
    14. McHenry A
    15. Elyaman W
    16. Petyuk V
    17. Schneider JA
    18. Bennett DA
    19. De Jager PL
    20. Bradshaw EM

    (2018) A transcriptomic atlas of aged human microglia

    Nature Communications 9:539.

    https://doi.org/10.1038/s41467-018-02926-5

    • Google Scholar
56. 1. Palmer AL
    2. Ousman SS

    (2018) Astrocytes and Aging

    Frontiers in Aging Neuroscience 10:337.

    https://doi.org/10.3389/fnagi.2018.00337

    • PubMed
    • Google Scholar
57. 1. Piñero J
    2. Bravo À
    3. Queralt-Rosinach N
    4. Gutiérrez-Sacristán A
    5. Deu-Pons J
    6. Centeno E
    7. García-García J
    8. Sanz F
    9. Furlong LI

    (2017) DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

    Nucleic Acids Research 45:D833–D839.

    https://doi.org/10.1093/nar/gkw943

    • PubMed
    • Google Scholar
58. 1. Piñero J
    2. Ramírez-Anguita JM
    3. Saüch-Pitarch J
    4. Ronzano F
    5. Centeno E
    6. Sanz F
    7. Furlong LI

    (2019) The DisGeNET knowledge platform for disease genomics: 2019 update

    Nucleic Acids Research 01:gkz1021.

    https://doi.org/10.1093/nar/gkz1021

    • Google Scholar
59. 1. Rando TA
    2. Wyss-Coray T

    (2021) Asynchronous, contagious and digital aging

    Nature Aging 1:29–35.

    https://doi.org/10.1038/s43587-020-00015-1

    • PubMed
    • Google Scholar
60. 1. Regev A
    2. Teichmann SA
    3. Lander ES
    4. Amit I
    5. Benoist C
    6. Birney E
    7. Bodenmiller B
    8. Campbell P
    9. Carninci P
    10. Clatworthy M
    11. Clevers H
    12. Deplancke B
    13. Dunham I
    14. Eberwine J
    15. Eils R
    16. Enard W
    17. Farmer A
    18. Fugger L
    19. Göttgens B
    20. Hacohen N
    21. Haniffa M
    22. Hemberg M
    23. Kim S
    24. Klenerman P
    25. Kriegstein A
    26. Lein E
    27. Linnarsson S
    28. Lundberg E
    29. Lundeberg J
    30. Majumder P
    31. Marioni JC
    32. Merad M
    33. Mhlanga M
    34. Nawijn M
    35. Netea M
    36. Nolan G
    37. Pe’er D
    38. Phillipakis A
    39. Ponting CP
    40. Quake S
    41. Reik W
    42. Rozenblatt-Rosen O
    43. Sanes J
    44. Satija R
    45. Schumacher TN
    46. Shalek A
    47. Shapiro E
    48. Sharma P
    49. Shin JW
    50. Stegle O
    51. Stratton M
    52. Stubbington MJT
    53. Theis FJ
    54. Uhlen M
    55. van Oudenaarden A
    56. Wagner A
    57. Watt F
    58. Weissman J
    59. Wold B
    60. Xavier R
    61. Yosef N
    62. Human Cell Atlas Meeting Participants

    (2017) The Human Cell Atlas

    eLife 6:e27041.

    https://doi.org/10.7554/eLife.27041

    • PubMed
    • Google Scholar
61. 1. Ritchie ME
    2. Phipson B
    3. Wu D
    4. Hu Y
    5. Law CW
    6. Shi W
    7. Smyth GK

    (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies

    Nucleic Acids Research 43:e47.

    https://doi.org/10.1093/nar/gkv007

    • Google Scholar
62. 1. Robertson MJ
    2. Barnes JC
    3. Drew GM
    4. Clark KL
    5. Marshall FH
    6. Michel A
    7. Middlemiss D
    8. Ross BC
    9. Scopes D
    10. Dowle MD

    (1992) Pharmacological profile of GR117289 in vitro: a novel, potent and specific non-peptide angiotensin AT1 receptor antagonist

    British Journal of Pharmacology 107:1173–1180.

    https://doi.org/10.1111/j.1476-5381.1992.tb13425.x

    • PubMed
    • Google Scholar
63. 1. Robinson MD
    2. McCarthy DJ
    3. Smyth GK

    (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

    Bioinformatics 26:139–140.

    https://doi.org/10.1093/bioinformatics/btp616

    • PubMed
    • Google Scholar
64. 1. Robinson MD
    2. Oshlack A

    (2010) A scaling normalization method for differential expression analysis of RNA-seq data

    Genome Biology 11:R25.

    https://doi.org/10.1186/gb-2010-11-3-r25

    • Google Scholar
65. 1. Rodwell GEJ
    2. Sonu R
    3. Zahn JM
    4. Lund J
    5. Wilhelmy J
    6. Wang L
    7. Xiao W
    8. Mindrinos M
    9. Crane E
    10. Segal E
    11. Myers BD
    12. Brooks JD
    13. Davis RW
    14. Higgins J
    15. Owen AB
    16. Kim SK

    (2004) A transcriptional profile of aging in the human kidney

    PLOS Biology 2:e427.

    https://doi.org/10.1371/journal.pbio.0020427

    • PubMed
    • Google Scholar
66. Software

    1. Saraiva-Agostinho N
    2. de Almeida BP

    (2020) cTRAP: identification of candidate causal perturbations from differential gene expression data, version 0.1

    R Pacakge.

    https://rdrr.io/bioc/cTRAP/
67. 1. Schaum N
    2. Lehallier B
    3. Hahn O
    4. Pálovics R
    5. Hosseinzadeh S
    6. Lee SE
    7. Sit R
    8. Lee DP
    9. Losada PM
    10. Zardeneta ME
    11. Fehlmann T
    12. Webber JT
    13. McGeever A
    14. Calcuttawala K
    15. Zhang H
    16. Berdnik D
    17. Mathur V
    18. Tan W
    19. Zee A
    20. Tan M
    21. Tabula Muris Consortium
    22. Pisco AO
    23. Karkanias J
    24. Neff NF
    25. Keller A
    26. Darmanis S
    27. Quake SR
    28. Wyss-Coray T

    (2020) Ageing hallmarks exhibit organ-specific temporal signatures

    Nature 583:596–602.

    https://doi.org/10.1038/s41586-020-2499-y

    • PubMed
    • Google Scholar
68. 1. Shavlakadze T
    2. Morris M
    3. Fang J
    4. Wang SX
    5. Zhu J
    6. Zhou W
    7. Tse HW
    8. Mondragon-Gonzalez R
    9. Roma G
    10. Glass DJ

    (2019) Age-related gene expression signature in rats demonstrate early, late, and linear transcriptional changes from multiple tissues

    Cell Reports 28:3263–3273.

    https://doi.org/10.1016/j.celrep.2019.08.043

    • PubMed
    • Google Scholar
69. 1. Stegeman R
    2. Weake VM

    (2017) Transcriptional Signatures of Aging

    Journal of Molecular Biology 429:2427–2437.

    https://doi.org/10.1016/j.jmb.2017.06.019

    • PubMed
    • Google Scholar
70. 1. Subramanian A
    2. Tamayo P
    3. Mootha VK
    4. Mukherjee S
    5. Ebert BL
    6. Gillette MA
    7. Paulovich A
    8. Pomeroy SL
    9. Golub TR
    10. Lander ES
    11. Mesirov JP

    (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

    PNAS 102:15545–15550.

    https://doi.org/10.1073/pnas.0506580102

    • PubMed
    • Google Scholar
71. 1. Subramanian A
    2. Narayan R
    3. Corsello SM
    4. Peck DD
    5. Natoli TE
    6. Lu X
    7. Gould J
    8. Davis JF
    9. Tubelli AA
    10. Asiedu JK
    11. Lahr DL
    12. Hirschman JE
    13. Liu Z
    14. Donahue M
    15. Julian B
    16. Khan M
    17. Wadden D
    18. Smith IC
    19. Lam D
    20. Liberzon A
    21. Toder C
    22. Bagul M
    23. Orzechowski M
    24. Enache OM
    25. Piccioni F
    26. Johnson SA
    27. Lyons NJ
    28. Berger AH
    29. Shamji AF
    30. Brooks AN
    31. Vrcic A
    32. Flynn C
    33. Rosains J
    34. Takeda DY
    35. Hu R
    36. Davison D
    37. Lamb J
    38. Ardlie K
    39. Hogstrom L
    40. Greenside P
    41. Gray NS
    42. Clemons PA
    43. Silver S
    44. Wu X
    45. Zhao W-N
    46. Read-Button W
    47. Wu X
    48. Haggarty SJ
    49. Ronco LV
    50. Boehm JS
    51. Schreiber SL
    52. Doench JG
    53. Bittker JA
    54. Root DE
    55. Wong B
    56. Golub TR

    (2017) A Next generation connectivity map: L1000 platform and the first 1,000,000 Profiles

    Cell 171:1437–1452.

    https://doi.org/10.1016/j.cell.2017.10.049

    • PubMed
    • Google Scholar
72. 1. Sun X
    2. Kaufman PD

    (2018) Ki-67: more than a proliferation marker

    Chromosoma 127:175–186.

    https://doi.org/10.1007/s00412-018-0659-8

    • PubMed
    • Google Scholar
73. 1. Tacutu R
    2. Thornton D
    3. Johnson E
    4. Budovsky A
    5. Barardo D
    6. Craig T
    7. Diana E
    8. Lehmann G
    9. Toren D
    10. Wang J
    11. Fraifeld VE
    12. de Magalhães JP

    (2018) Human Ageing Genomic Resources: new and updated databases

    Nucleic Acids Research 46:D1083–D1090.

    https://doi.org/10.1093/nar/gkx1042

    • PubMed
    • Google Scholar
74. 1. Thomas RP
    2. Guigneaux M
    3. Wood T
    4. Evers BM

    (2002) Age-associated changes in gene expression patterns in the liver

    Journal of Gastrointestinal Surgery 6:445–453.

    https://doi.org/10.1016/s1091-255x(01)00010-5

    • PubMed
    • Google Scholar
75. 1. Tower J

    (2017) Sex-specific gene expression and life span regulation

    Trends in Endocrinology and Metabolism 28:735–747.

    https://doi.org/10.1016/j.tem.2017.07.002

    • PubMed
    • Google Scholar
76. 1. van Dam S
    2. Võsa U
    3. van der Graaf A
    4. Franke L
    5. de Magalhães JP

    (2017) Gene co-expression analysis for functional classification and gene–disease predictions

    Briefings in Bioinformatics bbw139:bbw139.

    https://doi.org/10.1093/bib/bbw139

    • Google Scholar
77. 1. van Deursen JM

    (2014) The role of senescent cells in ageing

    Nature 509:439–446.

    https://doi.org/10.1038/nature13193

    • PubMed
    • Google Scholar
78. 1. Welle S
    2. Brooks AI
    3. Delehanty JM
    4. Needler N
    5. Thornton CA

    (2003) Gene expression profile of aging in human muscle

    Physiological Genomics 14:149–159.

    https://doi.org/10.1152/physiolgenomics.00049.2003

    • PubMed
    • Google Scholar
79. 1. Wyss-Coray T

    (2016) Ageing, neurodegeneration and brain rejuvenation

    Nature 539:180–186.

    https://doi.org/10.1038/nature20411

    • PubMed
    • Google Scholar
80. 1. Yang J
    2. Huang T
    3. Petralia F
    4. Long Q
    5. Zhang B
    6. Argmann C
    7. Zhao Y
    8. Mobbs CV
    9. Schadt EE
    10. Zhu J
    11. Tu Z
    12. Ardlie KG
    13. Deluca DS
    14. Segrè AV
    15. Sullivan TJ
    16. Young TR
    17. Gelfand ET
    18. Trowbridge CA
    19. Maller JB
    20. Tukiainen T
    21. Lek M
    22. Ward LD
    23. Kheradpour P
    24. Iriarte B
    25. Meng Y
    26. Palmer CD
    27. Winckler W
    28. Hirschhorn J
    29. Kellis M
    30. MacArthur DG
    31. Getz G
    32. Shablin AA
    33. Li G
    34. Zhou Y-H
    35. Nobel AB
    36. Rusyn I
    37. Wright FA
    38. Lappalainen T
    39. Ferreira PG
    40. Ongen H
    41. Rivas MA
    42. Battle A
    43. Mostafavi S
    44. Monlong J
    45. Sammeth M
    46. Mele M
    47. Reverter F
    48. Goldmann J
    49. Koller D
    50. Guigo R
    51. McCarthy MI
    52. Dermitzakis ET
    53. Gamazon ER
    54. Konkashbaev A
    55. Nicolae DL
    56. Cox NJ
    57. Flutre T
    58. Wen X
    59. Stephens M
    60. Pritchard JK
    61. Lin L
    62. Liu J
    63. Brown A
    64. Mestichelli B
    65. Tidwell D
    66. Lo E
    67. Salvatore M
    68. Shad S
    69. Thomas JA
    70. Lonsdale JT
    71. Choi C
    72. Karasik E
    73. Ramsey K
    74. Moser MT
    75. Foster BA
    76. Gillard BM
    77. Syron J
    78. Fleming J
    79. Magazine H
    80. Hasz R
    81. Walters GD
    82. Bridge JP
    83. Miklos M
    84. Sullivan S
    85. Barker LK
    86. Traino H
    87. Mosavel M
    88. Siminoff LA
    89. Valley DR
    90. Rohrer DC
    91. Jewel S
    92. Branton P
    93. Sobin LH
    94. Qi L
    95. Hariharan P
    96. Wu S
    97. Tabor D
    98. Shive C
    99. Smith AM
    100. Buia SA
    101. Undale AH
    102. Robinson KL
    103. Roche N
    104. Valentino KM
    105. Britton A
    106. Burges R
    107. Bradbury D
    108. Hambright KW
    109. Seleski J
    110. Korzeniewski GE
    111. Erickson K
    112. Marcus Y
    113. Tejada J
    114. Taherian M
    115. Lu C
    116. Robles BE
    117. Basile M
    118. Mash DC
    119. Volpi S
    120. Struewing J
    121. Temple GF
    122. Boyer J
    123. Colantuoni D
    124. Little R
    125. Koester S
    126. Carithers NLJ
    127. Moore HM
    128. Guan P
    129. Compton C
    130. Sawyer SJ
    131. Demchok JP
    132. Vaught JB
    133. Rabiner CA
    134. Lockhart NC
    135. The GTEx Consortium

    (2015) Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases

    Scientific Reports 5:15145.

    https://doi.org/10.1038/srep15145

    • Google Scholar
81. Software

    1. Yihui X
    2. Joe C
    3. Xianying T

    (2024) DT: A wrapper of the Javascript library ‘Datatables, version 0.32

    Rpackage.

    https://github.com/rstudio/DT
82. 1. Yoshida S
    2. Yashar BM
    3. Hiriyanna S
    4. Swaroop A

    (2002)

    Microarray analysis of gene expression in the aging human retina

    Investigative Ophthalmology & Visual Science 43:2554–2560.

    • PubMed
    • Google Scholar
83. 1. Yu Y
    2. Fuscoe JC
    3. Zhao C
    4. Guo C
    5. Jia M
    6. Qing T
    7. Bannon DI
    8. Lancashire L
    9. Bao W
    10. Du T
    11. Luo H
    12. Su Z
    13. Jones WD
    14. Moland CL
    15. Branham WS
    16. Qian F
    17. Ning B
    18. Li Y
    19. Hong H
    20. Guo L
    21. Mei N
    22. Shi T
    23. Wang KY
    24. Wolfinger RD
    25. Nikolsky Y
    26. Walker SJ
    27. Duerksen-Hughes P
    28. Mason CE
    29. Tong W
    30. Thierry-Mieg J
    31. Thierry-Mieg D
    32. Shi L
    33. Wang C

    (2014) A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages

    Nature Communications 5:3230.

    https://doi.org/10.1038/ncomms4230

    • PubMed
    • Google Scholar
84. 1. Zahn JM
    2. Sonu R
    3. Vogel H
    4. Crane E
    5. Mazan-Mamczarz K
    6. Rabkin R
    7. Davis RW
    8. Becker K
    9. Owen AB
    10. Kim SK

    (2005) Transcriptional profiling of aging in human muscle reveals a common aging signature

    PLOS Genetics preprint:e115.

    https://doi.org/10.1371/journal.pgen.0020115.eor

    • Google Scholar
85. 1. Zahn JM
    2. Poosala S
    3. Owen AB
    4. Ingram DK
    5. Lustig A
    6. Carter A
    7. Weeraratna AT
    8. Taub DD
    9. Gorospe M
    10. Mazan-Mamczarz K
    11. Lakatta EG
    12. Boheler KR
    13. Xu X
    14. Mattson MP
    15. Falco G
    16. Ko MSH
    17. Schlessinger D
    18. Firman J
    19. Kummerfeld SK
    20. Wood WH
    21. Zonderman AB
    22. Kim SK
    23. Becker KG

    (2007) AGEMAP: a gene expression database for aging in mice

    PLOS Genetics 3:e201.

    https://doi.org/10.1371/journal.pgen.0030201

    • PubMed
    • Google Scholar

Author details

1. Arthur L Schneider

   Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

   Present address

   Sia Partners, 4 Rue Voltaire, 44000 Nantes, Nantes, France

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   Contributed equally with

   Rita Martins-Silva and Alexandre Kaizeler

   Competing interests

   No competing interests declared

2. Rita Martins-Silva

   Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

   Contribution

   Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Arthur L Schneider and Alexandre Kaizeler

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1067-7993

3. Alexandre Kaizeler

   Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

   Contribution

   Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Arthur L Schneider and Rita Martins-Silva

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9117-6073

4. Nuno Saraiva-Agostinho

   Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

   Present address

   European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom

   Contribution

   Resources, Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5549-105X

5. Nuno L Barbosa-Morais

   Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing - review and editing

   For correspondence

   nmorais@fm.ul.pt

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1215-0538

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