Editor's evaluation: Deep proteome profiling reveals signatures of age and sex differences in paw skin and sciatic nerve of naïve mice

Abstract

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Abstract The age and sex of studied animals profoundly impact experimental outcomes in biomedical research. However, most preclinical studies in mice use a wide-spanning age range from 4 to 20 weeks and do not assess male and female mice in parallel. This raises concerns regarding reproducibility and neglects potentially relevant age and sex differences, which are largely unknown at the molecular level in naïve mice. Here, we employed an optimized quantitative proteomics workflow in order to deeply profile mouse paw skin and sciatic nerves (SCN) – two tissues implicated in nociception and pain as well as diseases linked to inflammation, injury, and demyelination. Remarkably, we uncovered significant differences when comparing male and female mice at adolescent (4 weeks) and adult (14 weeks) age. Our analysis deciphered protein subsets and networks that were correlated with the age and/or sex of mice. Notably, among these were proteins/biological pathways with known (patho)physiological relevance, e.g., homeostasis and epidermal signaling in skin, and, in SCN, multiple myelin proteins and regulators of neuronal development. Extensive comparisons with available databases revealed that various proteins associated with distinct skin diseases and pain exhibited significant abundance changes in dependence on age and/or sex. Taken together, our study uncovers hitherto unknown sex and age differences at the level of proteins and protein networks. Overall, we provide a unique proteome resource that facilitates mechanistic insights into somatosensory and skin biology, and integrates age and sex as biological variables – a prerequisite for successful preclinical studies in mouse disease models. Editor's evaluation This study sheds light on the importance of appropriate experimental design for mouse disease models which has been overlooked so far. The authors provide solid evidence for dynamic changes of proteomes in mouse tissues according to age and sex. This type of work is extremely valuable to many biomedical scientists in the field for conducting reproducible research, especially in preclinical studies. https://doi.org/10.7554/eLife.81431.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The age and sex of mice are major confounders in preclinical studies, affecting experimental outcomes across scales: from molecular, morphological, and physiological to behavioral parameters (Flórez-Vargas et al., 2016; Flurkey et al., 2007; Fu et al., 2013; Jackson et al., 2017). In mice, the first 12 weeks of life are characterized by pronounced changes in terms of growth and development of all organs and systems. Therefore, the Jackson Laboratory (https://www.jax.org) considers the widely used mouse strain C57BL/6J of mature adult physiology only at 12 weeks of age (Flurkey et al., 2007). Similarly, the sex of mice needs to be considered when comparing experimental outcomes. Despite recently enforced policies by funding agencies to include animals of both sexes, most preclinical studies still do not perform experiments on male and female rodents in parallel, exhibit gaps in data analysis by sex, and often pool animals of both sexes and a wide range of ages (between 4 and 20 weeks) (Flórez-Vargas et al., 2016) given time and financial constraints (Garcia-Sifuentes and Maney, 2021; Woitowich et al., 2020). These practices may negatively impact reproducibility across studies, increase data variability, conceal differences or generate artifactual results, and, consequently, hamper translationally oriented preclinical research (Flórez-Vargas et al., 2016; Jackson et al., 2017; Oliva et al., 2020). A prominent example of the enormous diversity of age ranges in publications are studies on rodent (mainly mice and rats) skin and peripheral sensory neurons (e.g., the sciatic nerve [SCN]) in the context of somatosensation and pain. Here, it is particularly noteworthy that often different age ranges were used for in vivo versus in vitro investigations. Mouse behavior experiments assessing paw sensitivity have routinely been performed in mice aged between 6 and 20 weeks (Hanack et al., 2015; Moehring et al., 2018; Zheng et al., 2019). Studies in cultured peripheral sensory neurons or keratinocytes have used mice aged 4–6 weeks (Hanack et al., 2015; Poole et al., 2014), 4–8 weeks (Zheng et al., 2019), 7–10 weeks (Narayanan et al., 2018; Narayanan et al., 2016), or 8–16 weeks (Sadler et al., 2020). Similarly, myelination of the SCN has been studied biochemically in mice aged 3 weeks (Siems et al., 2020), 10 weeks, and up to several months (depending on disease severity) (Siems et al., 2021). In contrast, cultured Schwann cells are generally derived from newborn rats (Siems et al., 2020). We have recently discovered a previously unknown age dependence of tactile sensitivity in the back skin and hind paws of mice (Michel et al., 2020). In particular, 4-week-old adolescent mice were more sensitive to innocuous tactile stimulation than 12-week-old adult mice. Interestingly, these observations correlated with similar changes in the activity of the mechanically activated ion channel Piezo2 and age-dependent transcriptome changes in peripheral sensory neurons. Even so, to date, we still lack comprehensive knowledge about the differential molecular setup of the somatosensory system in dependence on age and sex, in particular on the level of the proteome. This is highly relevant as transcript levels only show limited correspondence with protein levels, which renders the functional interpretation of transcriptome results difficult, in particular under dynamic conditions such as development, maturation, and disease (Liu et al., 2016; Schwanhäusser et al., 2011; Wang et al., 2017). However, in contrast to well-established RNA-seq approaches, deep proteome profiling of complex tissues is still challenging, above all, for low abundant and transmembrane proteins. Latest technological advances in mass spectrometry (MS) and data analysis provide new solutions for these challenges (Demichev et al., 2020; Meier et al., 2020; Meier et al., 2018). Here, we thoroughly compared two MS-based quantitative proteomics approaches: commonly used data-dependent acquisition (DDA) paired with parallel accumulation serial fragmentation (DDA-PASEF) (Meier et al., 2018) compared to data-independent acquisition (DIA-PASEF) (Meier et al., 2020). The latter has been shown to offer superior performance for deep profiling (Meier et al., 2020), yet it has, thus far, only been applied by specialized laboratories given its high demands regarding technology and data analysis. The goal of this work was to comprehensively catalog the protein setup of mouse paw skin and SCN, changes upon age (comparing adolescents, 4 weeks of age, and adults, 14 weeks of age), and differences between male and female wild-type (WT) C57BL/6J mice. The SCN is affected by a wide variety of motor and sensory neuropathologies induced by inflammation, trauma, and demyelination. Similarly, the skin, as our interface to the outer world, can be impaired by several inflammatory diseases like atopic dermatitis, psoriasis, and lupus erythematodes. In addition, both the skin and SCN are involved in nociception and (chronic) pain. We therefore focused on the potential implication of our data for preclinical research on skin- and SCN-related pathologies, including pain. Our results decipher hitherto unknown age and sex dependency of assorted proteins and signaling pathways, including those with known disease relevance. Taken together, our dataset is unique as (1) it provides a quantitative protein catalog of skin and SCN and (2) it does so in dependence on the age and sex of naïve mice. Given the heterogeneity of mouse age ranges in biomedical studies and the impact of age and sex on experimental outcomes, our results represent a highly valuable resource to foster future investigations in the context of skin and peripheral nerve (patho)physiology by enhancing reproducibility and unmasking hitherto unknown differences. Results DIA-PASEF allows deep and reproducible proteome profiling of mouse paw skin and sciatic nerves (SCN) In this study, we analyzed 16 biological replicates of paw skin and SCN samples to compare the proteome between (1) two age groups, that is, 4-week-old adolescent mice and 14-week-old adult mice, and (2) males and females (Figure 1—figure supplement 1). To enable and optimize deep proteome profiling, we compared two label-free quantification strategies of MS-based quantitative proteomics. In particular, DDA-PASEF and DIA-PASEF. For each sample, we analyzed technical duplicates using a timsTOF Pro mass spectrometer (Bruker Daltonik). DDA or DDA-PASEF have been the methods of choice for most proteomics studies published so far (Aballo et al., 2021; Aebersold and Mann, 2016; Meyer, 2021). However, recent advances highlighted the superior performance of DIA-PASEF methods (Brunner et al., 2022; Meier et al., 2021), which we tested in our study side-by-side. Given the long acquisition time of approximately 20 days for all samples and replicates, we constantly monitored the performance of our MS setup in DIA-PASEF mode by using pooled skin peptides and SCN peptides as quality controls. Pearson’s correlation coefficients were calculated for all quality control runs (Figure 1A and B). The average correlations of quality controls were 0.98 and 0.99 for pooled skin and SCN samples, respectively, indicating highly consistent stability of the instrument setup. Usually, DIA data is searched against a peptide library constructed from data obtained via DDA of the same sample; therefore, only proteins present in the library can be identified and quantified (Ludwig et al., 2018). In contrast, DIA-NN, a recently developed program based on deep neural networks, extensively advanced DIA workflows with a library-free database search mode (Demichev et al., 2020). Thus, we compared DDA-PASEF data subjected to a standard MaxQuant search (Cox et al., 2014) with DIA-PASEF data subjected to DIA-NN library-free search. As shown in Figure 1C and D, protein identifications from DDA-PASEF were highly covered by DIA-PASEF experiments, and DIA-PASEF detected additional 4135 and 3926 protein groups in skin and SCN, respectively (Figure 1—source data 1 and 2). Besides comparing protein identifications (protein IDs: for the remainder of this article, we will refer to protein groups as protein IDs for the sake of simplicity), we also compared both acquisition modes with respect to reproducibility at the quantitative level. Notably, we observed smaller coefficients of variation (CVs) across all DIA-PASEF runs (Figure 1E and F), indicating higher reproducibility compared to DDA-PASEF. Taken together, DIA-PASEF exhibited superior performance and was therefore chosen for further analysis of skin and SCN samples. Figure 1 with 1 supplement see all Download asset Open asset Data-independent acquisition paired with parallel accumulation serial fragmentation (DIA-PASEF) acquisition followed by DIA-NN analysis outperforms data-dependent acquisition paired with PASEF (DDA-PASEF) acquisition in deep proteome profiling of paw skin and sciatic nerve (SCN) of naïve mice. (A, B) Pearson’s correlations of technical controls of paw skin (blue) and SCN (green) acquired over 20 days on a timsTOF Pro. (C, D) Comparisons of identified protein groups (protein IDs) using DDA- and DIA-PASEF workflows in paw skin (C) and SCN (D). (E, F) Coefficient of variation (CV) distributions of quantitative proteomes using DDA- and DIA-PASEF in paw skin (E) and SCN (F) of 4-week and 14-week-old males (cyan) and females (magenta). Figure 1—source data 1 Quantitative proteome and differentially expressed protein (DEP) lists of paw skin. DEPs from age- and sex-dependent comparisons are listed in separated sheets. https://cdn.elifesciences.org/articles/81431/elife-81431-fig1-data1-v1.xlsx Download elife-81431-fig1-data1-v1.xlsx Figure 1—source data 2 Quantitative proteome and differentially expressed protein (DEP) lists of sciatic nerve (SCN). DEPs from age- and sex-dependent comparisons are listed in separated sheets. https://cdn.elifesciences.org/articles/81431/elife-81431-fig1-data2-v1.xlsx Download elife-81431-fig1-data2-v1.xlsx Age-dependent protein abundance changes in mouse paw skin and SCN In paw skin, we quantified > 8600 protein IDs across experimental groups (Figure 2A, Figure 1—source data 1). Comparing this proteome dataset with the most comprehensive (human) skin proteome dataset (Dyring-Andersen et al., 2020) published so far, our skin proteome covered approximately 70% (Figure 2B). Importantly, in our study we analyzed whole-skin lysates without preanalytical sample fractionation (e.g., separation of different skin layers) (Dyring-Andersen et al., 2020). Note that the previously published skin proteome was obtained from human hairy skin, while we analyzed mouse glabrous skin known to exhibit several differences in skin structure (Gudjonsson et al., 2007). Nonetheless, we identified all 50 known keratins and 19 collagens. In addition to structural proteins, we also quantified 13 members of the interleukin (IL) family and 11 of the S100 family (Figure 2E), known to play essential roles in the context of inflammation and infection (Kozlyuk et al., 2019; Velazquez-Salinas et al., 2019). Their detection across all skin samples with only a few missing values (note that we did not impute any data; see ‘Materials and methods’ for details) further validates the high performance and reproducibility of our optimized workflow. In SCN, approx. 8400 protein IDs were quantified across experimental groups (Figure 2C, Figure 1—source data 2). SCN harbor myelinated axons, which are closely associated with glia cells such as Schwann cells. Remarkably, the myelin proteome was nearly completely covered in our SCN data (94%); 1014/1077 described myelin proteins (Siems et al., 2020; Figure 2D), without a priori myelin enrichment as required in previous studies (Siems et al., 2020). Among the 63 proteins of the myelin proteome, which were not covered in our dataset, were ATP synthases, histones, and septins (Figure 2—source data 1). Another indication as to the depth and high performance of our workflow is the fact that we robustly quantified multiple ion channels across SCN samples (Figure 2F, Figure 1—source data 2), such as Trpv1 and several voltage-gated sodium channels (e.g., Scn8a, Scn9a, Scn11a) – again without requiring preanalytical membrane preparations. These ion channel identifications further corroborate the high quality of our approach as ion channels are usually expressed at low abundance and are notoriously difficult to be detected by MS given their pronounced hydrophobicity (Samways, 2014). Figure 2 with 1 supplement see all Download asset Open asset Age and sex differences in proteomes of paw skin and sciatic nerve (SCN). (A) Venn diagram shows unique and shared protein IDs across age and sex groups of paw skin. (B) Comparison of the quantified paw skin proteome with previously reported sub-proteomes of human skin (Dyring-Andersen et al., 2020) indicates high coverage in our proteome data. (C) Venn diagram shows unique and shared protein IDs across age and sex groups of SCN. (D) Our SCN proteome dataset harbors 1014 myelin proteins, i.e. 94% of the previously reported myelin proteome (Siems et al., 2020). (E) Heatmaps show the expression of interleukin and S100 protein families across all paw skin samples. (F) Heatmap shows the expression of ion channel proteins quantified across all SCN samples. Color legends are coded based on log2-transformed protein intensities. (G, H) Principal component analysis (PCA) reveals age as a prominent variable in paw skin and SCN tissues. Figure 2—source data 1 List of myelin proteins (Siems et al., 2020) not quantified in the sciatic nerve (SCN) proteome. https://cdn.elifesciences.org/articles/81431/elife-81431-fig2-data1-v1.xlsx Download elife-81431-fig2-data1-v1.xlsx We employed principal component analysis (PCA) to visualize proteome similarities and differences across age and sex groups. Importantly, we only considered those proteins that were robustly quantified in all samples (according to all our quality criteria; see ‘Materials and methods’ for details), resulting in 6086 protein IDs in the skin and 6065 protein IDs in SCN (Figure 2G and H, Figure 1—source data 1 and 2). Age groups were clearly segregated by the first and second components in skin and SCN samples, indicating that age is a prominent discriminator in our study and associated differences can be tackled by whole-proteome analysis. Furthermore, to elucidate changes in abundance profiles across all experimental groups, fuzzy C-means clustering analysis was performed based on the average intensity of any protein ID quantified (Figure 2—figure supplement 1). Among the nine clusters generated, most of the proteins showed strong age patterns, such as clusters 2 and 5 in skin, and clusters 4, 6, and 7 in SCN. On the contrary, several proteins exhibited different expression trends in age/sex groups. For instance, most proteins in cluster 6 of the skin proteome showed minor age-dependent changes in females, while their abundance was notably increased in 14-week males compared to 4-week males (Figure 2—figure supplement 1). Similar sex-specific changes were also observed in SCN represented by cluster 3. Taken together, the clustering analysis of the paw skin and SCN proteome reveals thus far unknown expression patterns dependent on the biological variables age and sex, that is, sex-specific and -overlapping age dependency, which may affect mouse (patho)physiology. Diverse biological pathways exhibit age dependence intertwined with sex differences in paw skin To explore this age dependency further, we applied a fold change (FC) cut-off (absolute log2 FC ≥ 0.585, i.e., an absolute FC of 1.5) in addition to a significance cut-off (q-value ≤ 0.05) and found 234 and 94 differentially expressed proteins (DEPs) in female and male skin datasets, respectively (Figure 1—source data 1). As shown in Figure 3A, 46 DEPs were shared between sexes, while 188 and 48 DEPs were unique for female and male skin (Figure 1—source data 1). Gene Ontology Biological Process (GO-BP) analysis of 46 common DEPs resulted in three significantly enriched pathways (criteria: at least four DEPs/pathway, Bonferroni-adjusted p-value ≤ 0.05). DEPs annotated to enriched pathways were mapped back to quantitative proteomic data, and the agglomerated z-scores of the pathways are visualized in Figure 3B, revealing a marked age-dependent pattern. As expected, skin development-related pathways such as ‘protein hydroxylation’ and ‘collagen fibril organization’ were enriched in 4-week skin compared to 14 weeks. These pathways were reported to be implicated in skin stability during development (Rappu et al., 2019). Specifically, several proline/serine hydroxylases (e.g., P4ha2, P3h1, P4ha1) were highly expressed in 4-week skin together with members of collagens (Figure 3B). Performing GO-BP enrichment on DEPs from age-dependent comparisons in female versus male mice (Figure 3—figure supplement 1A and B) revealed interesting biological insights into sex-dependent differences. In male skin, pathways of ‘notch signaling’ and ‘extrinsic apoptotic signaling’ were significantly enriched at 4 weeks, while ‘actin-mediated cell contraction’ and ‘cellular component assembly involved in morphogenesis’ were enriched at 14 weeks (Figure 3C). In female skin, proteins annotated to multiple interconnected pathways were significantly enriched at 14 weeks compared to 4 weeks (Figure 3D, Figure 3—figure supplement 1A). Many of these have been shown to contribute to skin homeostasis (Hamanaka et al., 2013; Sreedhar et al., 2020) such as ‘plasma membrane organization’, ‘cotranslational protein targeting to membrane’, and ‘positive regulation of map kinase activity’ besides others like ‘positive regulation of axonogenesis’ and ‘glial cell development’ (Figure 3D, Figure 3—figure supplement 1A). In contrast, proteins annotated to ‘cellular response to heat’ showed a higher z-score in 4-week skin of females. Figure 3 with 1 supplement see all Download asset Open asset Differential expression analysis of paw skin samples reveals diverse age-dependent biological pathways in male and female mice. (A) Venn diagram illustrates unique and shared differentially expressed proteins (DEPs; criteria: q-value ≤ 0.05, absolute log2 fold change [FC] ≥ 0.585, i.e., an absolute FC of 1.5) from age-dependent comparisons in female (magenta) and male (cyan) paw skin. (B) 46 common DEPs (A) are annotated to pathways related to skin development. The agglomerated z-score of each pathway is visualized in the heatmap. Common DEPs are annotated to three interconnected pathways. All proteins annotated here were highly expressed in 4-week paw skin (red filled circle). (C, D) Enriched interconnected pathways from age-dependent comparison in male (cyan) and female (magenta) mice. Red: higher expression at 4 weeks; blue: lower expression at 4 weeks. (E) Ligands and neuronal receptors found in skin cells (Wangzhou et al., 2021) are significantly regulated by age. Significance levels are indicated as ns, q-value > 0.05, *q-value ≤ 0.05, **q-value ≤ 0.01, ***q-value ≤ 0.001, and ****q-value ≤ 0.0001. Figure 3—source data 1 List of ligands and neuronal receptors found in skin cell types (Wangzhou et al., 2021), which we quantified in the paw skin proteome. https://cdn.elifesciences.org/articles/81431/elife-81431-fig3-data1-v1.xlsx Download elife-81431-fig3-data1-v1.xlsx Keratinocytes are among the most abundant cell types in skin, followed by fibroblasts, endothelial cells, melanocytes, and subsets of resident innate and adaptive immune cells. In addition, sparsely distributed sensory nerve endings in the skin play significant roles for aspects of somatosensation, including the detection of different physical stimuli, whether they be innocuous or noxious. However, this cellular diversity cannot be separated on the experimental level when analyzing complex tissue lysates as in our study. Therefore, we assessed the depth of our profiling workflow across different cell types indirectly by applying a recently published resource on ligand–receptor interactions in 42 cell types, including sensory neurons of mouse dorsal root ganglia (DRG) (Wangzhou et al., 2021). We extracted ligand–receptor interactions found across skin cell types (Wangzhou et al., 2021) for comparison with our skin dataset. In total, 144 ligands and receptors of DRG were present in our skin dataset (Figure 3—source data 1), of which 12 were significantly regulated (q-value ≤ 0.05) when comparing 4-week to 14-week mice (Figure 3E). For example, the ligand Lamb1 was found to be more abundant in 4-week female skin. Lamb1 was reported to serve as an anchor point for end feet of radial glial cells and as a physical barrier to migrating neurons (Radmanesh et al., 2013). Three receptors of Lamb1, low-density lipoprotein receptor-related protein 1 (Lrp1), C-type mannose receptor 2 (Mrc2), and suppressor of tumorigenicity 14 protein homolog (St14), were also identified in our dataset (Figure 1—source data 1). Interestingly, Mrc2 showed age-dependent statistical significance with higher expression at 4 weeks of age. Taken together, our results generally raise awareness of pronounced age dependency of protein expression in naïve mice, which should be carefully considered when pooling wide-ranging age groups in mouse studies. Prominent age and sex dependency of immune pathways and myelin proteins in SCN In the SCN proteome, we observed similar age dependency as in paw skin. Differential expression analysis uncovered 929 DEPs and 1269 DEPs in age-dependent comparisons of female and male SCN (Figure 4A, Figure 1—source data 2), accounting for almost one-fifth of the here quantified SCN proteome. Pathway enrichment for 641 common DEPs (Figure 1—source data 2) and age-enriched DEPs in males versus females (for 4 weeks and 14 weeks, respectively) is given in Figure 4—figure supplement 1, spanning diverse categories from metabolic processes and translation to neuronal function and inflammatory/immune signaling. For example, among common DEPs, ‘vesicle localization’ had a higher z-score in 4-week SCN of both sexes, while pathways related to ‘neuron survival’, ‘neurotransmitter transport’, and ‘anterograde axonal transport’ were more pronounced in 14-week SCN of both sexes (Figure 4B). These processes appear to be interconnected via distinct DEPs (Figure 4B), suggesting crosstalk during development. For instance, superoxide dismutase (Sod1) was found to be less expressed in 4-week mice and represents a connecting hub of two pathways related to nervous system function (Figure 4B) in line with its implication in amyotrophic lateral sclerosis (ALS) (Pansarasa et al., 2018). Remarkably, 144 DEPs from age-dependent comparisons were associated with the ‘synapse’ as revealed by querying SynGO, a public reference for synapse research (Koopmans et al., 2019; Figure 4—source data 1). Figure 4 with 1 supplement see all Download asset Open asset Age-dependent differential expression analysis in sciatic nerve (SCN) samples. (A) Venn diagram illustrates unique and shared differentially expressed proteins (DEPs) (criteria: q-value ≤ 0.05, absolute log2 fold change [FC] ≥ 0.585, i.e., an absolute FC of 1.5) from age-dependent comparisons in female (magenta) and male (cyan) SCN. (B) Common DEPs are annotated to pathways related to neuronal function and inflammation. The agglomerated z-score of each pathway is visualized in the heatmap. Red: proteins more abundant at 4 weeks; blue: proteins less expressed at 4 weeks. (C) Eighteen ligands of neuronal receptors found in immune cells (Wangzhou et al., 2021) are significantly regulated by age. Significance levels are indicated as: ns, q-value > 0.05, *q-value ≤ 0.05, **q-value ≤ 0.01, ***q-value ≤ 0.001, and ****q-value ≤ 0.0001. (D) Log2 FC of previously reported myelin proteins (Siems et al., 2020) in our age-dependent SCN datasets. Red: higher expression at 4 weeks; blue: lower expression at 4 weeks; white: not significantly regulated. Figure 4—source data 1 Synaptic proteins among differentially expressed proteins (DEPs) from age-dependent comparisons in sciatic nerve (SCN). https://cdn.elifesciences.org/articles/81431/elife-81431-fig4-data1-v1.xlsx Download elife-81431-fig4-data1-v1.xlsx Figure 4—source data 2 Ligand list of neuronal receptors found in immune cells (Wangzhou et al., 2021), which we quantified in the sciatic nerve (SCN) proteome. https://cdn.elifesciences.org/articles/81431/elife-81431-fig4-data2-v1.xlsx Download elife-81431-fig4-data2-v1.xlsx Figure 4—source data 3 Neuronal ligands of glial receptors (Wangzhou et al., 2021), which we quantified in the sciatic nerve (SCN) proteome. https://cdn.elifesciences.org/articles/81431/elife-81431-fig4-data3-v1.xlsx Download elife-81431-fig4-data3-v1.xlsx Figure 4—source data 4 Glial ligands of neuronal receptors (Wangzhou et al., 2021), which we quantified in the sciatic nerve (SCN) proteome. https://cdn.elifesciences.org/articles/81431/elife-81431-fig4-data4-v1.xlsx Download elife-81431-fig4-data4-v1.xlsx Miscellaneous immune cell types are known to be present in the SCN, where they contribute to nerve health, damage, and repair, as well as to sensory phenomena of pain (Kalinski et al., 2020). Thus, we cross-referenced our data to the aforementioned ligand–receptor database (Wangzhou et al., 2021) and searched our SCN dataset for ligands of neuronal receptors known to be expressed in immune cells. Among the 56 immune cell ligands of neuronal receptors quantified in the SCN proteome, 19 showed age-dependent abundance changes in both sexes such as Agrin (Agrn), Thy-1 membrane glycoprotein (Thy1), and several collagens (Figure 4C, Figure 4—source data 2). Given their age-dependent abundance differences already in naïve mice, our results caution to adequately pool age groups when assessing immune signaling in mouse disease models as data might get skewed by underlying – and thus far unknown – age differences. We also assessed ligands of neuron receptors Wangzhou et al., 2021 found in glial cells and vice versa given their utmost importance for SCN (patho)physiology. We found 85 glial cell ligands of neuron receptors and 70 neuron ligands of glial cell receptors in our SCN proteome, and, more interestingly, about one-third of ligand-receptor pairs showed strong age dependency (Figure 4—source data 3 and 4), e.g., limbic system-associated membrane protein (Lsamp), a glial cell ligand mediating selective neuronal growth and axon targeting (Sanz et al., 2017), and two of its neuronal receptors, netrin-G1 (Ntng1) and thy-1 membrane glycoprotein (Thy1). While Lsamp exhibited higher expression in 4-week SCN than in 14-week SCN, its two receptors showed the opposite (Figure 4—source data 4, datasheet 2). Similar expression trends were also found in neuron ligands of glial cell receptors, for example, the ligand disintegrin and metalloproteinase domain-containing protein 23 (Adam23) were less abundant in 4-weeks SCN, but the receptor integrin beta-3 (Itgb3) was more abundant in 4-week SCN (Figure 4—source data 3, datasheet 2). These data may suggest a homeostatic mechanism specifically in young SCN