Skip to main content

Impact of aging on meningeal gene expression



The three-layered meninges cover and protect the central nervous system and form the interface between cerebrospinal fluid and the brain. They are host to a lymphatic system essential for maintaining fluid dynamics inside the cerebrospinal fluid-filled subarachnoid space and across the brain parenchyma via their connection to glymphatic structures. Meningeal fibroblasts lining and traversing the subarachnoid space have direct impact on the composition of the cerebrospinal fluid through endocytotic uptake as well as extensive protein secretion. In addition, the meninges are an active site for immunological processes and act as gatekeeper for immune cells entering the brain. During aging in mice, lymphatic drainage from the brain is less efficient contributing to neurodegenerative processes. Aging also affects the immunological status of the meninges, with increasing numbers of T cells, changing B cell make-up, and altered macrophage complement.


We employed RNASeq to measure gene expression and to identify differentially expressed genes in meninges isolated from young and aged mice. Using Ingenuity pathway, GO term, and MeSH analyses, we identified regulatory pathways and cellular functions in meninges affected by aging.


Aging had profound impact on meningeal gene expression. Pathways related to innate as well as adaptive immunity were affected. We found evidence for increasing numbers of T and B lymphocytes and altered activity profiles for macrophages and other myeloid cells. Furthermore, expression of pro-inflammatory cytokine and chemokine genes increased with aging. Similarly, the complement system seemed to be more active in meninges of aged mice. Altered expression of solute carrier genes pointed to age-dependent changes in cerebrospinal fluid composition. In addition, gene expression for secreted proteins showed age-dependent changes, in particular, genes related to extracellular matrix composition and organization were affected.


Aging has profound effects on meningeal gene expression; thereby affecting the multifaceted functions meninges perform to maintain the homeostasis of the central nervous system. Thus, age-dependent neurodegenerative processes and cognitive decline are potentially in part driven by altered meningeal function.


Consisting of three distinct layers-dura, arachnoid and pia mater—the meninges cover and protect the brain, the spinal cord and the optic nerve. Embedded between pia and arachnoid mater, the subarachnoid space (SAS) is filled with cerebrospinal fluid (CSF). Aside from physically enveloping the central nervous system (CNS), the meninges harbour stem cell niches [1], provide neurotrophic factors influencing brain development [2,3], perform immune surveillance of the CNS [4], and are critically important for CSF homeostasis [5,6].

The meninges are an important interface between the immunologically privileged CNS and the immune system. While pial vessels consist of endothelial cells forming tight cell–cell contacts, fenestrated vessels in the dura allow for the exchange of large molecules and plasma components [7].In addition, the meninges are host to a lymphatic system [8], which drains CSF to the deep cervical lymph nodes allowing for immunological surveillance of this fluid compartment [9].Interestingly, there are strong indications that age-dependent dysfunction of meningeal lymphatic drainage is linked to the development of neurodegenerative diseases [10,11].Furthermore, the meninges are populated by a host of immune cells including macrophages, dendritic cells, mast cells, neutrophils, and B and T lymphocytes, the latter comprising CD4 + , CD8 + , γδ and regulatory T (Treg) subpopulations [12,13,14].Whereas age-dependent increases in Tregnumbers are linked to age-associated cognitive decline [15], IL-17 secreting γδT cells, which start populating the meninges shortly after birth, are important for behavioural development [16].Moreover, early in life, meningeal B cells originating from the skull bone marrow form a subset distinct from systemic B cells [17].During aging, however, systemic B cells migrate into the meninges, thereby changing the meningeal adaptive immunity landscape [17].

Originating from yolk sac precursors early during development, meningeal macrophages (mMΦ) are closely related to parenchymal microglia. These cells can persist over long periods of time and have the potential for extensive self-renewal [18].Like microglia in the parenchyma, mMΦ are dynamic and constantly survey their surroundings. Various mMΦ subsets can be distinguished based on their marker expression, with pia mater enriched in Lyve-1 and MHC-II positive macrophages, and the dura containing mainly Lyve-1and MHC-II+macrophages [19].

In addition to the extensive complement of immune cells [20], the meninges also house a large population of fibroblast-like meningothelial cells (MECs). Reflecting the multiple functions of the meninges, these cells represent a transcriptionally diverse spectrum of cell types [21].在硬脑膜内,外骨膜硬铝fibroblasts and inner dural border cells form a barrier towards the cranial bone and the arachnoid, respectively. Immediately adjacent to the inner dural border, arachnoid barrier cells form a tight layer separating the CSF filled subarachnoid space from the dura with its fenestrated vessels. The arachnoid extends across the subarachnoid space with arachnoid fibroblasts either as in the mouse by directly making contact with the pia underneath or by covering collagenous septae and pillars as in humans and rats. Finally, pial fibroblasts delimiting the subarachnoid space opposite of the arachnoid form a basement membrane with direct contact to the brain parenchyma [20,22].Along with their different location, these fibroblasts express various cell–cell contact proteins [23] explaining differences in barrier formation on the dural, arachnoid and pial level. One important function of MECs is maintaining the CSF and the microenvironment of the subarachnoid space. MECs are highly active facultative phagocytes capable of ingesting neurotoxic peptides such as Aβ and α-synuclein [24], gram-positive and gram-negative bacteria [25], as well as apoptotic bodies [26].During inflammation, MECs are also involved in T and B cell recruitment and activation [27].In addition, and further connecting these cells to the meningeal immune system, MECs react to pyrogenic signals by secreting pro-inflammatory cytokines [20,28,29].Furthermore, MECs are a major source of extracellular matrix components for maintaining meningeal integrity and function [30].

Using RNASeq, we analysed the effects of aging on this complex anatomical structure. We identified aging-induced changes to the meningeal transcriptional profile consistent with a pro-inflammatory phenotype that includes the accumulation of T and B lymphocytes as well as changes in the activation status of mMΦ. Furthermore, our data indicate that aging not only affects the complement system, but also impinges on chemokine and cytokine signalling. Finally, we also observed age-related changes in meningeal transport mechanisms involving solute carriers and endocytosis. As meninges are critically important for maintaining CNS function, these changes might play a role in the context of neurodegenerative processes associated with aging.


To understand the effect of aging on meninges, we performed transcriptome analysis of this tissue isolated from young (n = 9, 13.2 ± 2.7 weeks) and aged (n = 12, 87.8 ± 5.1 weeks) female C57BL/6JRj mice. Following perfusion of animals to remove blood, meninges were isolated from the calotte [31].No meninges were dissected from the surface of the brain to minimize sample contamination with neural tissue.

Following total RNA isolation, RNAseq was performed to obtain 300 bp long paired sequences, which were aligned to the mouse reference genome using subread (Table S1). Count data were filtered against lowest level expressed genes (counts per million < 1), normalized using edgeR and prepared for linear modeling using voom ([32]; Additional file1: Fig. S1). Multidimensional scaling clearly separated between the two age groups (Fig.1A). Differential gene expression analysis revealed that 177 out of 18240 identified genes were downregulated in aged compared to young animals, while 429 genes were upregulated Fig.1B and Additional file1: Fig. S1). To exclude that aging altered the physical properties of meninges to an extend interfering with unbiased sampling, we compared expression of known meningeal cell marker genes [21] between young and aged animals. We found no significant difference in expression of the dural markersFxyd5andMgp, the dural/arachnoid markerCrapb2, the arachnoid markerAldh1a2(Raldh2) andCdh1(E-cadherin), the arachonoid/pial markerS100a6, and the pial markerLama2. Similarly, the lymphatic markerLyve1was not differentially expressed between the age groups (Additional file1:图S2)。基于这些数据,衰老没有标出se bias in our sample preparation and our sample likely contains to some extent fibroblast from all three meningeal layers despite preparing sample only from the calvaria bone.

Fig. 1
figure 1

Comparing gene expression in meninges isolated from young and aged mice.AMeninges were isolated from mice at different ages, total RNA was isolated, and gene expression was determined by RNASeq. Shown is a multi-dimensional scaling analysis of 21 samples based on the top 250 differentially regulated genes.BVolcano-plot of differential gene expression plotted as log10fold change vs. statistical significance of observed differential gene expression expressed as -log2of the Benjamini–Hochberg adjusted p-value

Immune-related pathways are affected by aging

Using Ingenuity Pathway Analysis (IPA), we found several regulatory networks influenced by the aging process (Fig.2). This analysis revealed a clear impact of aging on meningeal gene expression related to immune response-related signaling. Among the affected pathways are T and B cell receptor signaling, pathways targeting dendritic cells, natural killer cells, macrophages and monocytes. In addition, aging affects inflammasome and interleukin signaling as well as signaling from pattern recognition receptors.

Fig. 2
figure 2

Ingenuity analysis – Canonical pathways. Ingenuity canonical pathway analysis was performed on the young vs. aged meningeal genes data set. The bar graph shows canonical pathways differentially regulated in meninges between young and aged mice. Only pathways with log10Benjamini–Hochberg adjusted p-value > 2 and z-score >|2| are shown (** adjusted p-value < 0.01, *** adjusted p-value < 0.001; **** adjusted p-value < 0.0001)

Next, we performed cluster analysis on the results reported by the IPA to identify genes shared between the identified pathways allowing for a more detailed subanalysis (Additional file1: Fig. S3). We foundPik3r2(93/116 pathways) andPik3r5(89/116 pathways) coding for phosphoinositide-3-kinase regulatory subunits 2 and 5 as well as Pik3Cd (92/116 pathways, coding for phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit δ) which is central to most identified pathways. Phosphoinositide 3-kinases and their regulators are critical immune response signaling molecules, as are members of the integrin family Itgb2 (24/116 pathways), Itgal (20/116 pathways), Itgam (19/116 pathways), and Itgax (18/116 pathways) or members of the MHC class II complex (13-15/116 pathways). Other highly relevant genes includeRelb(56/116通路)编码的NF -κb监管factor andPlcg2(51/116 pathways) coding for phospholipase C gamma2 involved in immune receptor signal transduction.

最著名和激活得分最高,pathways related to T lymphocytes were affected by aging. Among the regulated genes most commonly connected to these pathways areCd3d/e/gandCd28coding for the T cell receptor and its co-stimulator,Pik3cd, Pik3r2, Pik3r5andVav1involved in T cell receptor signal transduction. In addition, genes coding for MHC class II complexes expressed on antigen presenting cells for T cell stimulation are also upregulated (Fig.3A).

Fig. 3
figure 3

Aging affects multiple pathways involving T and B lymphocytes and myeloid cells. Differentially expressed genes and pathways selected by Ingenuity analysis related to (A) T cells, or (B) B cells, or (C) myeloid cells were plotted in a circos diagram. Please note the involvement of certain genes in multiple pathways

Pathways related to B lymphocytes were less affected by aging. Prominent genes related to these four B cell-related pathways areBlnkinvolved in B cell activation,Sykimportant for B cell receptor signaling andRelbcritical for B cell maturation and survival (Fig.3B). Similarly, pathways related to myeloid cells such as macrophages and neutrophils were influenced by aging. Of note here areLyz2coding for lysozyme found in macrophages and granulocytes, andNcf1/2/4coding for neutrophil cytosolic factors (Fig.3C).

Since the analysis of canonical pathways suggested a strong impact of aging on cellular signaling pathways of the innate as well as the adaptive immune system, we also performed an Ingenuity-based diseases and function analysis. Interestingly, when categories of immune cells vs. reported functions were plotted, a clear divide in activation between functions associated with cells of the adaptive immune system versus cells of the innate immune system is evident (Fig.4)。请注意,而克丽T和B细胞功能rly influenced, functions related to myeloid cells show more widely ranging activation. When further comparing adaptive and innate immunity, the quantity of B and T lymphocytes but not of myeloid linage cells seemed to be increased. Nevertheless, several functions (such as adhesion, binding, movement, chemotaxis, homing, infiltration, migration and recruitment) seemed to be affected by the aging process in granulocytes and neutrophils. Moreover, functions associated with macrophages such as engulfment and response are also activated, although by a comparably lesser degree.

Fig. 4
figure 4

Aging impacts number and activation state of cells of the innate and adaptive immune system.ABased on Ingenuity “disease and function” analysis, functions related to cells of the immune system were selected and their activation z-score as well as the number of genes for each affected function were plotted.BUsing GO term analysis, a list of genes related to cytokines and chemokines was assembled and functionally annotated. Shown are genes differentially expressed between young and aged meninges

In line with the age-associated increase in meningeal immunological activity, IPA also suggested increased interleukin and chemokine signaling with aging. To further elucidate this, we generated a gene list of cytokines and chemokines as well as their cognitive receptors using GO term analysis (GO:0008009, GO:0004950, GO:0005125, and GO:0004896 for "chemokine activity", "chemokine receptor activity", "cytokine activity", "cytokine receptor activity"). In addition, we assigned labels for pro- and anti-inflammatory action to this gene list based on associated GO terms. Based on this list of 197 genes, we found 19 genes to be upregulated in aged meninges of which twelve were annotated with pro- and five with anti-inflammatory action, while two genes had both pro- and anti-inflammatory annotation. In young animals, only one of these genes with ambiguous annotation was expressed at higher levels as compared to aged animals (Fig.4B). Collectively, these data point to an age-dependent increase of pro-inflammatory signaling in the meningeal compartment. Together with the apparent increase in B and T cells, it seems that the meningeal innate and adaptive immune systems enter a heightened activity state during aging.

Meningeal transport mechanisms and aging

As part of the blood-CSF barrier, the meninges play important roles in maintaining CSF homeostasis. In addition to meningeal lymphatic vessels draining CSF, direct uptake but also secretion by MECs and resident immune cells contributes to CSF composition. Thus, we first analyzed the class of solute carriers, some of which were previously shown to be active in meninges. Out of 334 identified candidates, we found thirteen solute carrier genes to be differentially regulated between young and aged animals (Fig.5A). Among these, Slc13a5, mediating the cellular uptake of citrate is of special interest, as loss-of-function mutations are linked to seizures and epileptic encephalopathy [33].Other solute carriers found to be upregulated in an age-dependent manner are Slc11a1 and Slc28a2, linked to iron uptake by macrophages or nucleoside transport, respectively.

Fig. 5
figure 5

Impact of aging on meningeal transport mechanisms.AA gene list containing 334 genes coding for solute carrier proteins expressed in meninges was generated. The volcano plot highlights eight down- and five upregulated solute carrier genes.BUsing GO term analysis, a list of genes coding for proteins involved in endocytosis was assembled. The volcano plot highlights differentially expressed gene of this group of genes

Among the solute carriers found to be downregulated in aged mice (Fig.5A), Slc16a9, which transports lactate, pyruvate and ketone bodies, is of note.

Next, we assessed age-dependent regulation of genes associated with endocytosis. Out of 351 genes associated with GO terms containing “endocytosis”, 20 genes were upregulated in aged meninges, while two genes were downregulated Fig.5B). Of the upregulated genes, nine are linked to immunological processes, including in particular B cell specificIghm(logFC 2.94),Cd22(logFC 2.24) andCd209d(logFC 2.02). This data supports the increased presence of B cells in aged meninges, as also suggested by our analysis shown in Fig.4.

Aging influences meningeal protein secretion

In addition to cellular uptake, protein secretion by meningeal cells is important for CSF homeostasis by maintaining its proteome. Therefore, we evaluated the contribution of protein secretion by meningeal cells to CSF composition. Using the UniProt knowledge database, we assigned subcellular localization data to 12751 gene products expressed in meninges. Out of these 12751 genes, 1105 code for a protein annotated with cellular localization “secreted”. Interestingly, and supporting the notion that meningeal cells contribute to the CSF proteome, mean gene expression was about 3-times higher in the group of secreted proteins compared to the mean expression of all genes.

Figure6A secreted: 170.11 transcripts per million (TPM); all: 58.19 TPM). Among the genes coding for secreted proteins, 65 genes were differentially expressed with 50 genes upregulated in aged compared to young meninges (Fig.6B). Using Reactome pathway analysis (Fig.7A), affected pathways involving these differentially regulated genes coding for secreted proteins were centered on extracellular matrix (3/10), complement activation (3/10) and chemokine activity (1/10).

Fig. 6
figure 6

Aging alters the transcriptional profile of secreted proteins.ABased on average transcription of all samples irrespective of age, transcripts per million (TPM) were calculated and shown as violin plot for all identified genes (left panel) and for a list of genes coding for “secreted proteins” (right panel). The black line marks the mean gene expression in TPM.BA volcano plot highlighting differentially expressed genes from above gene list “secreted proteins”

Fig. 7
figure 7

Reactome analysis of differentially regulated genes coding for secreted proteins.AUsing the gene list “secreted proteins” from Fig.6, reactome pathway analysis was performed using the Bioconductor ReactomePA package [55].BA volcano plot of genes expressed in meninges and annotated with “extracellular matrix”.CA volcano plot of genes expressed in meninges and annotated with GO terms containing “complement”

Extracellular matrix and aging

As the meninges form the physical barrier surrounding and protecting the CNS, extracellular matrix composition is critical as it provides the structural basis for this function—reflected by the name dura (latin for hard, thick) for the outermost, collagenous layer of the meninges. Among the 542 genes associated with “extracellular matrix”, ten were upregulated while 13 were downregulated in aged compared to young meninges. Among these, genes coding for structural ECM components are affected with downregulation of collagen-coding genesCol3a1andCol8a1, suggesting an altered collagen composition of the extracellular matrix (Fig.7B). In fact, we found 44 collagen genes expressed in meninges with levels averaged across all samples ranging from 1584 TPM forCol1ato 0.4 TPM forCol6a6(附加文件1: Fig. S4A). Interestingly, expression of most collagen genes seemed to be affected by aging with borderline significance, further suggesting an impact on the collagenous ECM of aged meninges. With upregulation ofLamb3(coding for laminin β3), another structural component of ECM and in particular of the basal membrane is affected by aging. The impact of increasedLamb3expression, however, is unclear. The major laminin β-subunit, laminin β2, is more abundantly expressed in meninges by a factor of 250 (Additional file1: Fig. S4B). However, as laminins are determining factors for cell invasion and migration, increased laminin β3 levels might contribute to the above described potential increase in B cell population in aged meninges. ECM composition is not only determined by structural proteins such as collagens, but also by ECM remodeling enzymes. The observed upregulation ofMmp12(coding for the matrix metallopeptidase Mmp12) suggests age-related changes to the ECM of meninges. In addition, Mmp12 is also known as macrophage elastase, indicating that the observedMmp12upregulation might also reflect age-dependent changes to the meningeal macrophage population. Downregulation ofAdamsts5andAdamsts17coding for proteases acting on proteoglycans and fibrillin, respectively, further supports age-dependent meningeal ECM remodeling. The upregulation ofHpsecoding for heparanase active on proteoglycans, andMfap2coding for Microfibril-Associated Protein 2, both connected to increased cell migration and invasion, might also link age-dependent ECM remodeling with changes to the meningeal immune cell population.

Complement activation is impacted by aging

In addition to altered extracellular matrix, Reactome analysis suggested altered complement activation. Among 106 genes connected to complement activation, expression of 25 genes was increased in aged compared to young meninges (Fig.7C). WithC1qa,C1qb,C1qc,C2,C4b,C6andCfb, seven core components of the classical and alternative complement system are upregulated. In addition to genes coding for complement components, the genes with the highest upregulation in this group are immune globulin genes linked to B cells and important for triggering complement activation.

Alzheimer’s disease related changes to meningeal transcription

We previously reported on the neuroprotective function of MECs and their role in amyloid beta (Aβ) uptake linking these cells to Alzheimer’s disease (AD). Using Medical Subject Headings (MeSH) term analysis (Fig.8), we analyzed our data set for age-related changes linked to AD. Based on this, we identified 1237 genes connected to AD. Of these genes, 12 were downregulated, while 57 genes were upregulated in aged compared to young animals (Fig.8A, B). Among the highly regulated genes areCd79A(logFC = 1.85) part of the B-cell antigen receptor complex,Cst7(logFC = 1.77),Il1b(logFC = 1.74), andTrem2(logFc = 1.16). Of particular interest are Cst7 and Trem2, known microglial markers associated with AD. Unlike the brain parenchyma, where microglia represent the predominant phagocytic cell type, the meninges are home to a specific subgroup of macrophages. We therefore used MeSH term analysis and found 61 out of 69 differentially regulated genes to be annotated with “microglia” and/or macrophages (Fig.8C). This suggests an altered activation status of macrophages in the meninges during aging potentially connected to protective mechanisms involved in neurodegenerative processes.

Fig. 8
figure 8

Age-dependent regulation of genes related to Alzheimer disease.AA list of 1237 genes detected in meninges and annotated with the MeSH term D000544/Alzheimer Disease was generated. The violin plot displays the distribution of expression level of these genes with gene expression level of differentially expressed genes individually marked.BA volcano plot of gene list from A.CDifferentially expressed genes from gene list described in A were annotated using MeSH terms for D008264/macrophages and D017628/microglia and grouped. Shown is the log2 fold change for the three groups genes annotated with macrophages, macrophages and microglia and neither macrophages nor microglia


老化是伴随着深刻的cha生理nges. With its very limited regenerative capacity, the brain is especially affected by the effects of aging. The meninges, of minuscule volume compared to the large amount of neuronal and glial tissue of the CNS, are critical to maintaining brain function. In particular, the meninges are crucial to the immune-privileged status of the CNS and are clearly a first line of defense for the brain [36].Connected to this, meninges are essential for maintaining CSF homeostasis through lymphatic drainage [37], but also through protein and small molecule secretion into as well as endocytotic uptake from the CSF [24].

To understand better the effect of aging on meningeal function, we compared meningeal transcriptional profiles between young and aged mice. We found prominent age-related changes in immunological landscape, structural integrity, and processes modulating CSF composition. Our data point to a clear shift towards a pro-inflammatory meningeal immune response as aging progresses. We find clear indication for increased numbers of T and B lymphocytes in the meninges of aged mice. This is in line with the single cell-resolution immune cell atlas of the dural sinus of young and aged mice [38].There, aging was accompanied by increased numbers of CD4 and CD8 T cells. Furthermore, our observations of increased T and B lymphocytes are also in line with reports showing an age-dependent increase in meningeal T cells and in particular Foxp3+Tregwhile at the same time levels of T cells positive for the chemokine receptor Ccr7 were decreased [15,19].Interestingly, diminishedCcr7expression was linked to an observed increase of the Tregpopulation, and exacerbated disease phenotype in 5xFAD Alzheimer mice [15].In our data set, neitherCcr7norFoxp3expression were found to be significantly altered. However, we found the several genes encoding chemokines connected to T cell attraction and homing includingCcl3,Ccl5,Ccl7, andCcl839] to be upregulated in aged animals. These findings support an age-dependent impact on T cell homing and retention in the meninges.

Similar to T cells, we found increased expression of B cell-related pathways in line with Brioschi et al. [17),最近发现脑膜B细胞population to originate from the calvaria bone marrow, consistent with a specialized B cell subset tuned to the needs of the CNS. During aging, peripheral B cells and plasma cells were shown to invade the meninges potentially threatening the CNS immune privilege. In our data set, we indeed detected expression of plasma cell markersCxcr4,Cd93,Il6RandCd4440] supporting the presence of plasma cells in aged meninges.

While our analysis supports altered meningeal populations of T and B lymphocytes, our data does not hint towards a marked increase in meningeal macrophages. Consistent with this, we do not find increased expression of the meningeal macrophage markerMrc1/Cd20641].However, in IPA a clear trend towards altered activity of these cells is apparent. Interestingly, genes with age-dependent regulation related to AD can mostly be attributed to macrophages and microglia. In this context, the highly significant increase ofTrem2expression during aging is of special interest. Trem2 is a transmembrane receptor found on cells of myeloid origin. Rare partial loss-of-function variants ofTrem2are linked to increased risk to develop AD. Engagement of Trem2 induces a signaling cascade leading to increased cellular activity via Syk and PI3 kinase [42]—also upregulated in aged meninges. Thus, our data supports a heightened state of activity for meningeal macrophages in response to aging. In line with this, we also find upregulation of genes related to endocytosis/phagocytosis, further supporting increased toxic metabolite clearance with age. Similarly, the observed upregulation of complement components in aged meninges might be connected to increased macrophage activity. Complement activation is linked to various age-related neurodegenerative disorders and is known to regulate microglial activation [43].Among others, the complement receptorVsig4is upregulated in aged meninges about sixfold compared to young meninges. As Vsig4 is mainly found on monocytes/macrophages, this further supports a more active meningeal macrophage population in aged mice.

The complex meningeal architecture suggests an equally complex meningeal immune system. Indeed, meningeal immune cells show distinct sub-meningeal distribution. For example, barrier macrophages are mainly found in high densities in the dura and the pia mater, but less so in the subarachnoid space [44].Similarly, γδ-T cells are more prevalent in the dura than the arachnoid [16].While our data set provides insight into potential age-dependent changes to the meningeal immune system, it remains unclear whether these changes affect all meningeal layers or are limited to individual meningeal layers. To answer these questions, single cell sequencing with sub-meningeal resolution would be necessary.

Meningeal extracellular matrix remodeling seems to be a consequence of aging. We found differences in the expression of various ECM components including laminins and collagens, but also proteases responsible for ECM proteolysis. An altered ECM composition would not only impact the mechanical properties of the meninges, but could potentially contribute to differences in immune cell populations associated with age. Also, the observed eightfold increased expression ofMmp12,also known as macrophage metalloelastase, likely reflects age-related changes to the meningeal macrophage activity status. In addition, decreases in collagen expression points to a MEC-driven ECM remodeling. Based on the high expression level of various collagens, only MECs can conceivably produce these large amounts of ECM. Whether this attenuation of collagen expression extends also to other MEC functions remains unclear. In fact, we did not find indications for a substantial loss of secretory capacity of these cells. When looking at the top ten genes coding for secreted proteins includingApoe, B2m, Chgb, Cst3, Ctsd, Gpx3, Igfbp5, Mgp, Enpp2,andTtr—responsible for 6.5% of total transcription—we did not find significant differences between young and aged mice.

Our analysis of solute carrier gene expression revealed among others differential expression ofSlc13a5andSlc16a9involved in the transport of citrate or lactate, pyruvate and ketone bodies, respectively. These metabolites are critical for energy metabolism and differential expression of their transporters might be linked to age-related changes metabolomic changes in the CSF [45,46,47].Produced by astrocytes, citrate is released into the CSF at a concentration of up to 0.4 mM [34] and seems to be involved in the chelation of divalent ions such as Ca2+and Zn2+. Slc13a5 is significantly upregulated in aged compared to young mice (by 1.8 fold) suggesting increased citrate clearance from the CSF by meningeal cells. In addition, lactate is important in neuronal energy metabolism [35].At the level of the astrocyte–neuron lactate shuttle, astrocytes release lactate to surrounding neurons, enabling efficient neuronal ATP generation. However, whether MECs or other meninges-resident cells are directly involved in contributing lactate for neuronal consumption is unknown.

We were able to identify several regulatory pathways affected by aging. And in particular our findings regarding age-related changes to meningeal immune responses corroborate previous reports [15,16,38].However, we also want to point out that bulk RNASeq is not able to discriminate between different cell types present in the analyzed tissue or prove altered function of these cells. And although the main cellular component of our analyzed samples are MECs, we cannot rule out that certain observations attributed here to changes in MEC function are potentially confounded by the presence of other cell types in our samples. Also, it remains unclear which MEC subtype undergoes these age-dependent changes as our sample contains based on marker expression not only dural, but also arachnoid and likely to some extent pial meningeal fibroblasts (Additional file1:图S2)。Another limitation of our study is the reliance on transcriptional profiling. While gene expression studies paired with knowledge-based bioinformatics analyses can point towards affected cellular pathways such studies cannot provide functional insight. Additional experimentation would be necessary to draw functional conclusions about age-related changes to the meninges.

In our study, we compared the transcriptional profile of young and aged meninges. Our results indicate that the meninges play an important role in the immunological defense of the CNS. In particular, we find evidence for an age-related switch to a pro-inflammatory meningeal phenotype that involves the adaptive as well as the innate immune system. As immunological processes in the brain are intimately linked to the maintenance of cognitive function and as mis-directed immune responses are connected to neurodegenerative processes, our results highlight the importance of the oftentimes overlooked meninges for CNS function.

Materials and methods

Animal experimentation

Animal experiments were approved by the Cantonal Veterinary Office of Basel

城市(许可证3023)。C57BL / 6 jrj老鼠繁殖d kept under specific pathogen-free conditions. Young mice were between 9 and 15 weeks, aged mice were between 80 and 97 weeks at the time of the experiment. All mice used in this experiment were female.

Isolation of meninges

Meninges were isolated according to Louveau et. al [31].简而言之,小鼠麻醉和100毫克/公斤ketamine and 20 mg/kg xylazine in 0.9% saline. After reaching deep anesthesia, animals were transcardially perfused with PBS/heparin (5 U/ml, Sigma-Aldrich H3393) using a syringe pump (PHD Ultra, Harvard Apparatus) at a rate of 2 ml/min for at least 5 min until the liver showed clear signs of exsanguination. Next, the head was severed above the shoulders, skin and flesh were removed from the skull and the optic nerves were severed. After removing the lower jaw and the nasal bone, the skull was opened and the brain was carefully removed. Meningeal tissue was dissected from the skull bone using forceps; no material was obtained by dissecting mengines from the brain itself. Meninges were immediately transferred into buffer RLT buffer for RNA isolation (see below).

RNA isolation and NGS sequencing

使用RNeasy微RNA分离设备(试剂盒,74004). Isolated total RNA was processed and sequenced at Novogen (Oxford) using their low input protocol to obtain paired sequences of 300 bp.

Bioinformatic analysis

Sequences were aligned using subread v2.0.0 [48] against a full-index of the GRCm38 (mm10) mouse reference genome. Features were counted using featureCounts v2.0.0 against the built-in annotations [49].The compute platform was a four node Raspberry Pi 4B 8 GB cluster running slurm 19.05.5 scheduler under aarch64 Ubuntu 20.04 LTS.

Differential gene expression was assessed using R 4.1.0 and the Bioconductor packages limma [50] and edgeR [51].Following feature counting, transcripts with less than 10 counts were filtered as described [52].Pathway analysis was performed using Ingenuity (Qiagen [53]) and the Bioconductor [54] package ReactomePA [55].GO and MeSH term analysis were performed using Bioconductor packages biomaRt [56] and [57], respectively. Plotting was done using R packages ggplot2 [58] and circlize [59].To account for multiple testing, p-value adjustment was performed according to Benjamini-Hochberg.

Availability of data and materials

Data is available at


  1. Bifari F, Decimo I, Pino A, Llorens-Bobadilla E, Zhao S, Lange C, et al. Neurogenic radial glia-like cells in meninges migrate and differentiate into functionally integrated neurons in the neonatal cortex. Cell Stem Cell. 2017;20(3):360–737.

    ArticleCASGoogle Scholar

  2. Reiss K, Mentlein R, Sievers J, Hartmann D. Stromal cell-derived factor 1 is secreted by meningeal cells and acts as chemotactic factor on neuronal stem cells of the cerebellar external granular layer. Neuroscience. 2002;115(1):295–305.

    ArticleCASGoogle Scholar

  3. Choe Y, Siegenthaler JA, Pleasure SJ. A cascade of morphogenic signaling initiated by the meninges controls corpus callosum formation. Neuron. 2012;73(4):698–712.

    ArticleCASGoogle Scholar

  4. Hu X, Deng Q, Ma L, Li Q, Chen Y, Liao Y, et al. Meningeal lymphatic vessels regulate brain tumor drainage and immunity. Cell Res. 2020;30(3):229–43.

    ArticleCASGoogle Scholar

  5. Louveau A, Plog BA, Antila S, Alitalo K, Nedergaard M, Kipnis J. Understanding the functions and relationships of the glymphatic system and meningeal lymphatics. J Clin Invest. 2017;127(9):3210–9.

    ArticleGoogle Scholar

  6. Decimo I, Dolci S, Panuccio G, Riva M, Fumagalli G, Bifari F. Meninges: a Widespread niche of neural progenitors for the brain. Neuroscientist. 2021;27(5):506–28.

    ArticleGoogle Scholar

  7. Nakada T, Kwee IL. Fluid dynamics inside the brain barrier: current concept of interstitial flow, glymphatic flow, and cerebrospinal fluid circulation in the brain. Neuroscientist. 2019;25(2):155–66.

    ArticleGoogle Scholar

  8. Killer HE, Laeng HR, Groscurth P. Lymphatic capillaries in the meninges of the human optic nerve. J Neuroophthalmol. 1999;19(4):222–8.

    ArticleCASGoogle Scholar

  9. Da Mesquita S, Fu Z, Kipnis J. The meningeal lymphatic system: a new player in neurophysiology. Neuron. 2018;100(2):375–88.

    ArticleGoogle Scholar

  10. Da Mesquita S, Louveau A, Vaccari A, Smirnov I, Cornelison RC, Kingsmore KM, et al. Functional aspects of meningeal lymphatics in ageing and Alzheimer’s disease. Nature. 2018;560(7717):185–91.

    ArticleGoogle Scholar

  11. Zou W, Pu T, Feng W, Lu M, Zheng Y, Du R, et al. Blocking meningeal lymphatic drainage aggravates Parkinson’s disease-like pathology in mice overexpressing mutated alpha-synuclein. Transl Neurodegener. 2019;8:7.

    ArticleGoogle Scholar

  12. Nayak D, Zinselmeyer BH, Corps KN, McGavern DB. In vivo dynamics of innate immune sentinels in the CNS. Intravital. 2012;1(2):95–106.

    ArticleGoogle Scholar

  13. O’Brien CA, Overall C, Konradt C, O’Hara Hall AC, Hayes NW, Wagage S, et al. CD11c-expressing cells affect regulatory t cell behavior in the meninges during central nervous system infection. J Immunol. 2017;198(10):4054–61.

    ArticleCASGoogle Scholar

  14. Ribeiro M, Brigas HC, Temido-Ferreira M, Pousinha PA, Regen T, Santa C, et al. Meningeal gammadelta T cell-derived IL-17 controls synaptic plasticity and short-term memory. Sci Immunol. 2019.

    ArticleGoogle Scholar

  15. Da Mesquita S, Herz J, Wall M, Dykstra T, de Lima KA, Norris GT, et al. Aging-associated deficit in CCR7 is linked to worsened glymphatic function, cognition, neuroinflammation, and beta-amyloid pathology. Sci Adv. 2021.

    ArticleGoogle Scholar

  16. Alves de Lima K, Rustenhoven J, Da Mesquita S, Wall M, Salvador AF, Smirnov I, et al. Meningeal gammadelta T cells regulate anxiety-like behavior via IL-17a signaling in neurons. Nat Immunol. 2020;21(11):1421–9.

    ArticleCASGoogle Scholar

  17. Brioschi S, Wang WL, Peng V, Wang M, Shchukina I, Greenberg ZJ, et al. Heterogeneity of meningeal B cells reveals a lymphopoietic niche at the CNS borders. Science. 2021.

    ArticleGoogle Scholar

  18. Goldmann T, Wieghofer P, Jordao MJ, Prutek F, Hagemeyer N, Frenzel K, et al. Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat Immunol. 2016;17(7):797–805.

    ArticleCASGoogle Scholar

  19. Mrdjen D, Pavlovic A, Hartmann FJ, Schreiner B, Utz SG, Leung BP, et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity. 2018;48(3):599.

    ArticleCASGoogle Scholar

  20. Derk J, Jones HE, Como C, Pawlikowski B, Siegenthaler JA. Living on the edge of the CNS: meninges cell diversity in health and disease. Front Cell Neurosci. 2021;15:703944.

    ArticleCASGoogle Scholar

  21. DeSisto J, O’rourke R,琼斯,他Pawlikowski B,马lek AD, Bonney S, et al. Single-cell transcriptomic analyses of the developing meninges reveal meningeal fibroblast diversity and function. Dev Cell. 2020;54(1):43-59 e4.

    ArticleCASGoogle Scholar

  22. Dorrier CE, Jones HE, Pintaric L, Siegenthaler JA, Daneman R. Emerging roles for CNS fibroblasts in health, injury and disease. Nat Rev Neurosci. 2022;23(1):23–34.

    ArticleCASGoogle Scholar

  23. Zeleny TNC, Kohler C, Neutzner A, Killer HE, Meyer P. Cell-cell interaction proteins (gap junctions, tight junctions, and desmosomes) and water transporter aquaporin 4 in meningothelial cells of the human optic nerve. Front Neurol. 2017;8:308.

    ArticleGoogle Scholar

  24. Hemion C, Li J, Kohler C, Scholl HPN, Meyer P, Killer HE, et al. Clearance of neurotoxic peptides and proteins by meningothelial cells. Exp Cell Res. 2020;396(2):112322.

    ArticleCASGoogle Scholar

  25. Li J, Fang L, Killer HE, Flammer J, Meyer P, Neutzner A. Meningothelial cells as part of the central nervous system host defence. Biol Cell. 2013;105(7):304–15.

    ArticleCASGoogle Scholar

  26. Li J, Fang L, Meyer P, Killer HE, Flammer J, Neutzner A. Anti-inflammatory response following uptake of apoptotic bodies by meningothelial cells. J Neuroinflammation. 2014;11:35.

    ArticleCASGoogle Scholar

  27. Pikor NB, Cupovic J, Onder L, Gommerman JL, Ludewig B. Stromal cell niches in the inflamed central nervous system. J Immunol. 2017;198(5):1775–81.

    ArticleCASGoogle Scholar

  28. Wu Z, Zhang J, Nakanishi H. Leptomeningeal cells activate microglia and astrocytes to induce IL-10 production by releasing pro-inflammatory cytokines during systemic inflammation. J Neuroimmunol. 2005;167(1–2):90–8.

    ArticleCASGoogle Scholar

  29. Fan B, Bordigari G, Flammer J, Killer HE, Meyer P, Neutzner A. Meningothelial cells participate in immunological processes in the cerebrospinal fluid. J Neuroimmunol. 2012;244(1–2):45–50.

    ArticleCASGoogle Scholar

  30. Hao J, Kohler C, van den Dorpel H, Scholl HPN, Meyer P, Killer HE, et al. The extracellular matrix composition of the optic nerve subarachnoid space. Exp Eye Res. 2020;200:108250.

    ArticleCASGoogle Scholar

  31. Louveau A, Filiano AJ, Kipnis J. Meningeal whole mount preparation and characterization of neural cells by flow cytometry. Curr Protoc Immunol. 2018;121(1):e50.

    ArticleCASGoogle Scholar

  32. Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29.

    ArticleGoogle Scholar

  33. Thevenon J, Milh M, Feillet F, St-Onge J, Duffourd Y, Juge C, et al. Mutations in SLC13A5 cause autosomal-recessive epileptic encephalopathy with seizure onset in the first days of life. Am J Hum Genet. 2014;95(1):113–20.

    ArticleCASGoogle Scholar

  34. Westergaard N, Waagepetersen HS, Belhage B, Schousboe A. Citrate, a ubiquitous key metabolite with regulatory function in the CNS. Neurochem Res. 2017;42(6):1583–8.

    ArticleCASGoogle Scholar

  35. Brooks GA. The science and translation of lactate shuttle theory. Cell Metab. 2018;27(4):757–85.

    ArticleCASGoogle Scholar

  36. Engelhardt B, Coisne C. Fluids and barriers of the CNS establish immune privilege by confining immune surveillance to a two-walled castle moat surrounding the CNS castle. Fluids Barriers CNS. 2011;8(1):4.

    ArticleGoogle Scholar

  37. Meyer C, Martin-Blondel G, Liblau RS. Endothelial cells and lymphatics at the interface between the immune and central nervous systems: implications for multiple sclerosis. Curr Opin Neurol. 2017;30(3):222–30.

    ArticleGoogle Scholar

  38. Rustenhoven J, Drieu A, Mamuladze T, de Lima KA, Dykstra T, Wall M, et al. Functional characterization of the dural sinuses as a neuroimmune interface. Cell. 2021;184(4):1000-16 e27.

    ArticleCASGoogle Scholar

  39. Strazza M, Mor A. Consider the chemokines: a review of the interplay between chemokines and T cell subset function. Discov Med. 2017;24(130):31–9.

    Google Scholar

  40. Brynjolfsson SF, Persson Berg L, Olsen Ekerhult T, Rimkute I, Wick MJ, Martensson IL, et al. Long-lived plasma cells in mice and men. Front Immunol. 2018;9:2673.

    ArticleGoogle Scholar

  41. Mammana S, Fagone P, Cavalli E, Basile MS, Petralia MC, Nicoletti F, et al. The role of macrophages in neuroinflammatory and neurodegenerative pathways of Alzheimer’s disease, amyotrophic lateral sclerosis, and multiple sclerosis: pathogenetic cellular effectors and potential therapeutic targets. Int J Mol Sci. 2018.

    ArticleGoogle Scholar

  42. Carmona S, Zahs K, Wu E, Dakin K, Bras J, Guerreiro R. The role of TREM2 in Alzheimer’s disease and other neurodegenerative disorders. Lancet Neurol. 2018;17(8):721–30.

    ArticleCASGoogle Scholar

  43. Fatoba O, Itokazu T, Yamashita T. Complement cascade functions during brain development and neurodegeneration. FEBS J. 2022;289(8):2085–109.

    ArticleCASGoogle Scholar

  44. Rua R, McGavern DB. Advances in meningeal immunity. Trends Mol Med. 2018;24(6):542–59.

    ArticleCASGoogle Scholar

  45. Zebhauser PT, Berthele A, Goldhardt O, Diehl-Schmid J, Priller J, Ortner M, et al. Cerebrospinal fluid lactate levels along the Alzheimer’s disease continuum and associations with blood-brain barrier integrity, age, cognition, and biomarkers. Alzheimers Res Ther. 2022;14(1):61.

    ArticleCASGoogle Scholar

  46. Peters K, Herman S, Khoonsari PE, Burman J, Neumann S, Kultima K. Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid. Sci Rep. 2021;11(1):18822.

    ArticleCASGoogle Scholar

  47. Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. Sci Adv. 2022;8(42):eadd6155.

    ArticleCASGoogle Scholar

  48. Liao Y, Smyth GK, Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013;41(10):e108.

    ArticleGoogle Scholar

  49. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30.

    ArticleCASGoogle Scholar

  50. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.

    ArticleGoogle Scholar

  51. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.

    ArticleCASGoogle Scholar

  52. Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res. 2016.

    ArticleGoogle Scholar

  53. Kramer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics. 2014;30(4):523–30.

    ArticleGoogle Scholar

  54. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12(2):115–21.

    ArticleCASGoogle Scholar

  55. Yu G, He QY. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol Biosyst. 2016;12(2):477–9.

    ArticleCASGoogle Scholar

  56. Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009;4(8):1184–91.

    ArticleCASGoogle Scholar

  57. Tsuyuzaki K, Morota G, Ishii M, Nakazato T, Miyazaki S, Nikaido I. MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis. BMC Bioinformatics. 2015;16:45.

    ArticleGoogle Scholar

  58. Hadley W. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.

    Google Scholar

  59. Gu Z, Gu L, Eils R, Schlesner M, Brors B. circlize implements and enhances circular visualization in R. Bioinformatics. 2014;30(19):2811–2.

    ArticleCASGoogle Scholar

Download references


The authors want to acknowledge the animal caretaker team of the Department of Biomedicine, University Hospital Basel.


This work was supported by a grant to AN and HPK from the Herta-Messerli-Foundation (Switzerland).

Author information

Authors and Affiliations



MN, HPK, CK performed experimentation, edited and read the manuscript. MN, HPK, and AN conceived of the study. AN performed data analysis and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence toAlbert Neutzner.

Ethics declarations

Ethics approval and consent to participate

Animal experiments were approved by the Cantonal Veterinary Office of Basel-Stadt, Switzerland under licence #3023.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Figure S1.

Processing of RNASeq data.Figure S2.Comparing expression of known meningeal markers between young and aged animals.Figure S3. Cluster analysis of IPA.Figure S4.Overview expression of collagens and laminins.Table S1.Sample overview.


Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neutzner, M., Kohler, C., Frank, al.Impact of aging on meningeal gene expression.Fluids Barriers CNS20, 12 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Meninges
  • Aging
  • beplay靠谱
  • Neuroimmunology
  • Neurodegeneration
  • Inflammation
  • Brain