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Targeting lymphoid-derived IL-17 signaling to delay skin aging

Targeting lymphoid-derived IL-17 signaling to delay skin aging

 


Mouse handling and husbandry

Mice were housed under a regimen of 120h/12-h light/dark cycles and specific-pathogen-free conditions. The temperature of the animal facility was maintained between 20 and 24 °C and humidity ranged from 45 to 65%. Animals were handled following the ethical regulations and guidelines of Scientific Park of Barcelona and the Government of Catalunya. All procedures were evaluated and approved by the Ethical Committee for Animal Experimentation of the Government of Catalunya (approval reference no. 10712). All mice used were from the C57BL/6 J strain. Aged mice were either bred in-house or were retired C57BL/6 J breeder females purchased from Charles River, and were kept until the desired age in the animal facility at Barcelona Science Park. Control adult mice were either bred in-house or purchased from Charles River to generate matching cohorts. Mostly female mice were used due to this fact, and also to differences in skin-digesting efficiency between sexes, because longer male skin digestion times are required and this reduced the survival of sorted dermal cells, skewing the results towards the most resilient cell types. For experiments that did not require enzymatical digestion of the dermis, both males and females were used. Mice were always killed during darkness, to coincide with their active phase. Aged mice were between 80 and 90 weeks of age and adult mice were between 17 and 25 weeks of age. Mice with signs of skin inflammation (dermatitis, wounds or redness) were excluded from the experiments to avoid interference with the results.

Epidermal and dermal cell isolation for genomics analysis

Mice were killed and whole-torso skin was removed as rapidly as possible. Hypodermal fat was removed by scalpel and, after two washes in PBS, skins were floated (dermal side down) in dispase II solution (5 mg ml–1; no. D4693, Sigma-Aldrich) in PBS for 30–40 min at 37 °C. Epidermis was removed by scalpel. For dermal cell isolation, dermis was mechanically dissociated using a McIlwain Tissue Chopper (The Mickle Laboratory Engineering Co.) and then further digested in Liberase TM (6.5 Wünsch units per reaction, Roche) diluted in DMEM (no. 41965, Thermo Fisher Scientific) for 20–30 min at 37 °C with gentle agitation. Afterwards, DNase I (1 mg ml–1; no. DN25, Sigma-Aldrich) was added to the mix with incubation for 15 min at 37 °C without agitation. Digested dermis was first strained through a 100-µm strainer and then through a 40-µm strainer, to obtain single-cell suspensions.

RNA-seq

For bulk RNA-seq of adult and aged, and aged/control IgG-treated and anti-IL-17A/F-treated, mice, four mice were used per condition. Cells in single-cell suspensions were frozen in 1 ml of Trizol (Invitrogen) for posterior RNA isolation. RNA was extracted from epidermal cell pellets frozen in Trizol using the RNeasy Mini Kit (Qiagen) and further processed for mRNA-seq with Illumina sequencing technology.

Flow cytometry and cell sorting (yielding CD45+, CD45EpCAM)

For 10X scRNA-seq, single-cell dermal suspensions were incubated with CD45–APC (clone 30-F11, 1:100, no. 559864 BD Biosciences) and EpCAM–PE (clone G8.8, 1:200, no. 552370 BD Biosciences) for 45 min on ice. After two washes in PBS, cells were resuspended in 2 µg ml–1 DAPI (no. 32670, Sigma-Aldrich) to stain DNA. These were then analyzed using a BD FACSAria Fusion flow cytometer with FACSDiva software v.8.0.1, in which CD45± cells were retained and EpCAM+ cells excluded. For flow cytometry analyses, FlowJo v.10.0.8.r1 was used.

In vivo anti-IL-17A/F-neutralizing treatment

Cohorts of aging (73-week-old) mice were randomly distributed into two groups and were treated with either (1) a mixture of 105 µg of anti-IL-17A (clone 17F3, no. BE0173, BioXCell) and 105 µg of anti-IL-17F (clone MM17F8F5.1A9, no. BE0303, BioXCell) or (2) 210 µg of control IgG1 (clone MOPC-2, no. BE0083, BioXCell). Injections of 100 µl of antibody solution in PBS were administered intraperitoneally and performed three times per week during the dark cycle at the same time of day (2–3 h into the dark phase). After 12 weeks of treatment, mice were either (1) killed to obtain samples of either dermal cells for 10X scRNA-seq or epidermal bulk RNA-seq of the epidermis and histology analysis or (2) used to measure skin aging traits.

Epilation

Adult (n = 6, 18–20-week-old), aged/IgG control and aged/anti-IL-17A/F (n = 6, 85-week-old) female mice treated for 12 weeks with either IgG control (n = 6, 85-week-old) or anti-IL-17A/F were used (n = 6, 85 weeks old). Mice were anesthetized using a mixture of ketamine (75 mg kg–1 body weight) and medetomidine (1 mg kg–1) by intraperitoneal injection. Buprenorphine (0.05 mg kg–1) was injected subcutaneously as analgesic and anti-inflammatory treatment. An area of about 2–3 cm2 of back skin was epilated using wax papers until hair was completely removed (usually two or three rounds of waxing). Atipamezole (1 mg kg–1) was injected to reverse anesthesia, and mice were left on heating pads until completely recovered. Mice were housed individually to avoid scratching and contact of the epilated areas. Animal health status was monitored daily. After 8 days post hair removal, mice were killed and images of shaved back skin captured. Samples of back skin were taken, fixed in neutral buffered formalin (10%) for 3 h at room temperature, dehydrated and embedded in paraffin blocks for later histological assessment.

Wounding and wound-healing experiments

Adult mice (n = 15, 18–20 weeks old) and aged mice (n = 14, 85 weeks old) treated for 12 weeks with IgG control as previously described, and aged male and female mice (n = 15, 85 weeks old) treated for 12 weeks with anti-IL-17A/F-, were used. For the IL-17-blocked group, IL-17A/F-blocking injections were stopped 1 week before starting the wound-healing assay to normalize IL-17 endogenous activity and separate the role of this cytokine in wound healing74 from its role during aging.

Mice were anesthetized using 3–4% isoflurane, and buprenorphine (0.05 mg kg–1) was injected subcutaneously for analgesia. Body temperature of the animals was maintained by placing them on a heating mat covered by a sterile surgical drape throughout the process. Hair was shaved in the back region and the area sterilized with 10% povidone-iodine in distilled H2O solution. Two skin biopsies were carried out per animal, one on each flank using a 5-mm-diameter sterile, disposable circular biopsy punch (5 mm diameter, Kai medical, no. BP-50F). Sharp scissors were used to specifically dissect the skin layer biopsy after using the punch, to avoid affecting other underlying tissue. Mice were placed on warm pads until fully recovered from anesthesia and then housed individually throughout the entire experiment. Wound areas were macroscopically assessed by daily measurement of length and width using a digital caliper (Traceable Digital Caliper). For wound area analysis, measurements of two wounds per animal were averaged. All wounds were collected, fixed in neutral buffered formalin (10%) for 3 h at room temperature, dehydrated and embedded in paraffin blocks for later histological assessment.

Barrier recovery measurement

Barrier integrity was assessed by measurement of TEWL, a parameter that measures water evaporation directly on the skin surface using a probe (Tewameter TM Nano, Courage+Khazaka). The animals used included adult (n = 6, 18–20-week-old), aged/IgG control (n = 7, 85-week-old) and aged/IL-17A/F-treated (n = 5, 85-week-old) female mice. Back skins were shaved 24 h before barrier disruption assay. Mice were anesthetized using 3–4% isoflurane and their temperature maintained constant at 37 °C by keeping them on a tightly regulated thermal plate during TEWL measurements. The mice were left to acclimate after induction of anesthesia and before starting tape stripping and measurements. All measurements were carried out at the same location by the same person, and room temperature and humidity were recorded. The order of data collection was randomized on the first occasion but was then replicated for each consequent measure.

The epidermal barrier was disrupted by tape stripping (Corneofix CF 20, Courage+Khazaka) and the number of tape strips adjusted according to mouse age to obtain TEWL > 20 g m–2 h–1 (ref. 75). A range of between six and ten strips was needed for adults, and 12–16 for aged/IgG controls and aged/anti-IL-17-treated mice. For TEWL measurements the probe was used according to the manufacturer’s instructions. The probe was placed in each location of the skin surface being measured for 40 s before stabilization, each measurement lasting 1 min. TEWL probe measurements were carried out immediately after tape stripping (t0) and 6, 10, 24 and 48 h post tape stripping (t6, t10, t24, t48, respectively). Two measurements per animal were recorded, and we averaged their TEWL value to obtain one single value per animal and per time point. Also, for consistency, the probe was placed at these same two locations for each consecutive measurement. The locations used for measurements were carefully selected to avoid areas where tape stripping had caused the appearance of small wounds, to prevent alteration of the actual barrier recovery values.

Chromatin immunoprecipitation

Four independent mice per condition of adult, aged/IgG-treated control and aged/anti-IL-17A/F-treated were used for p65 chromatin immunoprecipitation sequencing (ChIP–seq). To isolate epidermal cells, the epidermal cell isolation protocol described below was followed (Epidermal and dermal cell isolation for genomics analysis). Instead of dispase II solution, 0.8% trypsin solution (trypsin 1:250, dissolved in PBS) was used to separate dermis from epidermis for 30–40 min at 37 °C. Around 30 million pelleted epidermal cells were used per reaction. The cells were crosslinked using Gold fixative (no. C01019027, Diagenode) following the manufacturer’s directions. After two PBS washes, a second fixation was carried out in methanol-free 1% formaldehyde (no. 28908, Thermo Fisher Scientific) in MEM calcium-free medium with 10% calcium-chelated fetal bovine serum for 10 min with rotation. Glycine was then added to a final concentration of 125 mM to stop crosslinking, with incubation for 5 min under rotation. The pellet was washed twice with cold PBS and the cells then resuspended in 7.5 ml of swelling buffer (25 mM Hepes pH 7.9, 1.5 mM MgCl2, 10 mM KCl, 0.1% NP-40 supplemented with 1× protease inhibitors without EDTA). This cell suspension was homogenized 50 times with a Dounce homogenizer and tight pestle (no. 885302-0015, Kimble) and the extracts were centrifuged (3,000g, 5 min). Pelleted nuclear extracts were resuspended (in one-tenth of the volume used for the swelling buffer) in ChIP buffer (10 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% Triton X-100, 5 mM EDTA, 0.5 mM DTT, supplemented with 0.2% SDS and 1× protease inhibitors without EDTA). This suspension was incubated for 15 min and then transferred to sonication tubes (no. C01020031, Diagenode). Sonication was carried out in 30 cycles of 30/30 min on/off at 4 °C (Bioruptor Pico, Diagenode) and the sonicated supernatant was centrifuged (14,000g, 10 min). The supernatant containing the chromatin was quantified for ChIP–seq.

For immunoprecipitation, 30 μg of chromatin was diluted with ChIP buffer without SDS to dilute the SDS concentration to 0.1% in all samples. Then, 4 μg ml–1 anti-p65 (no. 8242, Cell Signaling Technology) was added and samples rotated overnight at 4 °C. Sepharose beads (no. 17-5280-01, Merck) were used according to the manufacturer’s instructions, added to the tubes that included each sample and incubated with rotation for 4 h at 4 °C. Samples were then centrifuged (1,000g, 3 min) and washed with low (L)- and high (H)-salt buffers (L, H, L, H, L), centrifuging between washes. Low-salt buffer comprises 50 mM HEPES pH 7.5, 140 mM NaCl, 1% Triton and 1× protease inhibitors without EDTA, and high-salt buffer contains 50 mM HEPES pH 7.5, 500 mM NaCl, 1% Triton and 1× protease inhibitors without EDTA. A final wash was carried out with TE buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA). Immunoprecipitated chromatin was eluted in elution buffer (1% SDS and 100 mM NaHCO3, freshly prepared) by incubation of samples in the buffer for 30 min at 65 °C with agitation. Samples were then centrifuged (1,000g, 3 min), 5 M NaCl was added to a final concentration of 200 mM per sample followed by incubation for between 5 h and overnight at 65 °C, with agitation. Next, Tris-HCl pH 6.8 was added to a final concentration of 40 mM, EDTA to 10 mM, protease K to 50 μg ml–1 and incubation for 1 h at 45 °C. This product was purified with PCR columns.

ChIP–seq library construction and sequencing

Libraries were prepared using the NEBNext Ultra DNA Library Prep for Illumina kit (no. E7370) according to the manufacturer’s protocol. Briefly, input and ChIP-enriched DNA were subjected to end repair and the addition of ‘A’ bases to 3′ ends, ligation of the NEB adapter and USER excision. All purification steps were performed using AgenCourt AMPure XP beads (no. A63882, Beckman Coulter). Library amplification was performed by PCR using NEBNext Multiplex Oligos for Illumina (96 Unique Dual Index Primer Pairs, nos. E6440, E6442, E6444, E6446). Final libraries were analyzed using either an Agilent Bioanalyzer or Fragment analyzer High Sensitivity assay (no. 5067‐4626 or DNF‐474) to estimate the quantity and check size distribution, and were then quantified by quantitative PCR using the KAPA Library Quantification Kit (no. KK4835, KapaBiosystems). Libraries were sequenced 1 × 50 + 8 + 8 base pairs (bp) on Illumina’s NextSeq2000. Between 35 and 40 million reads were obtained per sample.

ChIP–seq analysis

Reads were trimmed, adapters removed and low-quality reads discarded using Trimmomatic76 (v.0.36, TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:36). Reads were then aligned to the mm10 genome with the Burrows–Wheeler aligner77 (v.0.7.12, -n 2 -l 20 -k 1 -t 2). Each sample was processed individually and, once reads were aligned, the .bam files were merged and deduplicated with SAMtools78 (v.1.5) and downsampled to 100 million reads to obtain a pool of four replicates per condition. Peaks were called in these pooled files using MACS2 with the parameters -q 0.01–nomodel–extsize 300 -B –SPMR. Peaks were then annotated to the closest gene with Homer79 (v.4.11), with those peaks annotated to nonprotein-coding genes not used for further analysis.

H&E staining

Formalin-fixed, paraffin-embedded (FFPE) blocks were cut into 2–4-µm sections and H&E staining was performed according to the standard protocol.

Immunofluorescence

In all cases, FFPE blocks were cut into 2–4-µm sections.

IL-17A: following heat-mediated antigen retrieval (20 min at 97 °C with citrate pH 6.0), sections were blocked with 10% goat serum in PBS for 1 h at room temperature. Primary antibody incubation (anti-IL-17A, no. ab79056, abcam 1:200 in EnVision FLEX antibody diluent, Dako) was performed overnight at 4 °C. After three washes in PBS, incubation of secondary antibody (anti-rabbit Alexa Fluor 488, no. A-21206, Molecular Probes, 1:400 in EnVision FLEX antibody diluent, Dako) was carried out at room temperature for 1 h. Nuclei were counterstained with DAPI (5 µg ml–1 for 10 min at room temperature). Six adult and five aged mice per condition were analyzed.

CD4: antigen retrieval was performed using BOND Epitope Retrieval 2 (no. AR9640, Leica). For primary antibody incubation, rat IgG2b kappa anti-CD4 (clone 4SM95, no. 14-976-682, Thermo Fisher) was used at 1:100 and incubated for 120 min using Leica BOND RX. Nonspecific unions were blocked using 5% goat normal serum mixed with 2.5% bovine serum albumin diluted in wash buffer for 60 min at room temperature. For secondary antibody incubation, goat anti-rat IgG (H + L) and Alexa Fluor 647 (no. A-21247, Thermo Fisher) were used at 1:500 for 60 min. Nuclei were stained with DAPI and slides mounted with fluorescence Mounting Medium (no. S3023, Dako – Agilent). Five adult and five aged mice per condition were analyzed.

FISH

For IL17A and IL17F, human adult (22–29 years old, n = 4, two women and two men) and aged (60–72 years old, n = 4, two women and two men) skin FFPE 5-µm tissue sections were purchased from Genoskin. These samples were approved by the French Ethics Committee and the French Ministry of Research and Higher Education (approval reference no. AC-2022-4863). They were then stained with RNAscope Probes—Hs-IL17A, Interleukin 17A probe (no. 310931, Bio-techne) and Hs-IL17F, Interleukin 17F probe (no. 313901, Bio-techne)—using RNAscope Intro Pack 2.5 HD Reagent Kit Red (no. 322350, Bio-techne). DAPI was used for staining of all nuclei.

For Rspo3, FFPE 3-µm tissue sections of mouse back skin from aged/anti-IL-17-treated or aged/IgG control mice were stained with RNAscope Probe Mm-Rspo3-O1 (no. 429861, Bio-techne) using RNAscope Intro Pack 2.5 HD Reagent Kit Red (no. 322350, Bio-techne). DAPI was used to stain all nuclei.

Histopathological analysis

Full images were acquired with a NanoZoomer-2.0 HT C9600 digital scanner (Hamamatsu) with the ×20 objective, in which one pixel corresponds to 0.46 µm, and coupled to a mercury lamp unit L11600-05 and using NDP.scan 3.4 software U10074-03 (Hamamatsu).

Scaled images were analyzed with Qupath (v.0.3.0 and v.0.3.2). For cornified layer thickness we performed ten measurements per mouse, which were then averaged. Eight mice per condition and age were used. We always ensured that the area analyzed was in the resting hair follicle stage (telogen), to avoid interference of this parameter with the desired quantification. For wound-healing assay, qualitative analysis of dermal granulation tissue and epidermal maturation were performed in a double-blind study by histopathologists. For anagen stage assessment we assigned each hair follicle to an anagen stage based on previous reports55.

For immunostaining, multiple selections of the region of interest of the skin dermis, excluding the epidermis and hair follicles, were done manually in two portions of skin. The ‘positive cell count’ algorithm was used to detect positive cells. Results are presented as the percentage of positive cells. For Il-17A, NDP view2 (v.2.9.29, Hamamatsu) was used to generate snapshots from representative areas. Brightness and contrast were adjusted to help distinguish positive cells from background, using the same parameters for both conditions. For CD4 immunofluorescence, the images displayed in the manuscript were captured with an SP5 confocal microscope (Leica). Images of whole stacks were projected on a single image and brightness and contrast were adjusted to enhance the signal of positive cells, using the same parameters for both conditions. For all immunostaining, these modified images were used exclusively for visualization and never for quantification.

For IL17A and IL17F FISH, skin upper dermis—excluding the epidermis, hair follicles and skin appendages—was selected manually. In case of tissue artifacts, these were excluded from the region of interest (for example, broken areas, folds). The positive cell count algorithm was used to detect positive labeling in the TRITC channel. Results are presented as the percentage of cells with IL17A or IL17F signal (either spots—single mRNA copy—or clusters—accumulation of several mRNA copies). For visualization purposes only, representative snapshots were obtained with NDP view2 (v.2.9.29m, Hamamatsu) and brightness and contrast were adjusted to help distinguish FISH signal from background, using the same parameters for both conditions.

For Rspo3 FISH, dermal papillae were selected manually in well-oriented telogen hair follicles. The ‘Cell detection’ algorithm was used to detect nuclei within the selected areas, and the ‘Subcellular spot detection’ algorithm to detect Rspo3-positive labeling in the TRITC channel in those nuclei. Individual nuclei were not always distinguishable due to high packing of dermal papillae cells. Therefore, FISH signal (spots and clusters) was not calculated per cell, but per dermal papillae. Five mice per condition were used. Graphical representations were performed with GraphPad Prism 9. For visualization purposes only, representative snapshots were obtained with NDP view2 (v.2.9.29, Hamamatsu) and brightness and contrast were adjusted to help distinguish FISH signal from background, using the same parameters for both conditions. All FISH quantifications were performed on the raw images and never on adjusted ones. Modified images were used exclusively for clearer visualization in figures.

Bulk RNA-seq library construction and sequencing

For the adult versus aged comparison, libraries were prepared using the TruSeq Stranded Total RNA Library Prep Kit with the Ribo-Zero Human/Mouse/Rat Kit (no. RS-122-2201/2202, Illumina) according to the manufacturer’s protocol, using 150–300 ng of total RNA; for ribosomal RNA depletion, RNA was then fragmented for 4.5 min at 94 °C. The remaining steps were followed according to the manufacturer’s instructions. Final libraries were analyzed on an Agilent Technologies 2100 Bioanalyzer system using the Agilent DNA 1000 chip to estimate the quantity and validate size distribution; libraries were then quantified by quantitative R using KAPA Library Quantification Kit KK4835 (no. 07960204001, Roche) before amplification with Illumina’s cBot. Finally, libraries were sequenced on the Illumina HiSeq 2500 sequencing system using paired-end 50-bp-long reads.

For IL-17-neutralized versus control samples, libraries were prepared using TruSeq stranded mRNA Library Prep (no. 20020595, Illumina) according to the manufacturer’s protocol, to convert total RNA to a library of template molecules of known strand origin suitable for subsequent cluster generation and DNA sequencing. Briefly, 500–1,000 ng of total RNA was used for poly(A)-mRNA selection with poly-T oligonucleotides attached to magnetic beads, and two rounds of purification. During the second elution of poly-A RNA, RNA was fragmented under an elevated temperature and primed with random hexamers for complementary DNA synthesis. Cleaved RNA fragments were then copied into first-strand cDNA using reverse transcriptase (SuperScript II, no. 18064-014, Invitrogen) and random primers. Note that the addition of actinomycin D to the First Stand Synthesis Act D mix (FSA) improved strand specificity by prevention of spurious DNA-dependent synthesis while allowing RNA-dependent synthesis. Second-strand cDNA was then synthesized by removal of the RNA template and synthesis of a replacement strand, incorporating dUTP in place of dTTP to generate double-stranded cDNA using DNA polymerase I and RNase H. These cDNA fragments then had a single A base added to the 3′ ends of the blunt fragments, to prevent them from ligating to one another during adapter ligation. A corresponding single T-nucleotide on the 3′ end of the adapter provided a complementary overhang for ligation of the adapter to the fragments, ensuring a low rate of chimera (concatenated template) formation. Subsequent ligation of the multiple indexing adapter to the ends of dscDNA was carried out. Finally, PCR selectively enriched DNA fragments with adapter molecules on both ends, and the amount of DNA in the library was amplified. PCR was performed with a PCR Primer Cocktail that anneals to the ends of the adapters. Final libraries were analyzed using either Bioanalyzer DNA 1000 or Fragment Analyzer Standard Sensitivity (Agilent) to estimate the quantity and validate size distribution; libraries were then quantified by quantitative PCR using KAPA Library Quantification Kit KK4835 (Roche) before amplification with Illumina’s cBot. Finally, libraries were sequenced on the Illumina HiSeq 2500 sequencing system using single-end 50-bp-long reads.

10X scRNA-seq

Stained dermal single-cell suspensions were prepared as described above. CD45+ and CD45EpCAM cells were FACS sorted separately in a BD Fusion cell sorter to enrich for the less abundant CD45+ cells following the sorting strategy shown in Supplementary Fig. 1a. Cells were collected in PBS + 0.5% bovine serum albumin at 4 °C in LoBind tubes (Eppendorf) and processed immediately with the microfluidics Chromium platform (10X Genomics). For the adult and aged experiment, three technical replicates were performed for CD45+ sorting. Replicate 1 included skin dermal cells of three adult and one aged mice, replicate 2 had two adult and one aged mice and replicate 3 comprised two adult and two aged mice. For CD45EpCAM cell sorting two replicates were carried out, both comprising one mouse per condition. For aged/IL-17-blocked and aged/IgG control 10X scRNA-seq of dermal skin cells, both CD45+ and CD45EpCAM conditions included four replicates in total. In both conditions, replicate 1 consisted of two aged/IL-17-blocked and two aged/IgG control mice. Replicates 2, 3 and 4 consisted in all cases of one aged/IL-17-blocked and one aged/IgG control mouse.

Data preprocessing

Sequences were demultiplexed and aligned according to the Cell Ranger pipeline (v.6.0.0) with default parameters. Sequencing reads were mapped against the mouse GRCm38 reference genome to generate feature-barcode matrices separately for all CD45+ and CD45 replicates.

Quality control and technical bias corrections

Gene count matrixes were analyzed with the Seurat package (v.4.0.4) in R (v.4.0.3)80. Replicates were merged, analyzed and annotated separately for CD45+ and CD45 datasets before integration. Cells were filtered with >10% of mitochondrial gene content and genes not found in at least five cells. As part of quality control, cells situated between the minimum and first quartile (according to the distribution of number of genes per cell of each compartment dataset) were removed. To avoid contamination of epithelial cells in the immune compartment, EPCAM+ cells found in CD45+ sorted cells were filtered out.

In addition, to remove technical bias arising after merging the different replicates we computed differentially expressed genes among replicates using the function FindAllMarkers, then we ensured that the top 500 differentially expressed genes between replicates were not present in highly variable genes (HVGs). Thus we removed the intersection between computed differentially expressed genes and HVG.

To evaluate integration of the different replicates we used the local inverse Simpson’s index (LISI) (https://github.com/immunogenomics/LISI)30. This score defines the effective number of samples in the local neighborhood of a cell. Under ideal mixing we would expect to obtain LISI scores equal to the number of different replicates in our datasets. This indicates that the neighborhoods are well represented by all samples and that the cell types/states previously identified exhibit good mixing across all replicates. In addition we assessed the performance of replicate integration by qualitative inspection of UMAPs. We checked that all cell types and states were well represented by each replicate and that our dataset did not contain any technical cluster entirely driven by replicate effect.

Clustering

Cell-to-cell variations were normalized by expression values using a scale factor of 100,000 and log transformation. Gene expression measurements were scaled and centered. Scaled z-score values were then used as normalized gene measurement input for both clustering and visualization of differences in expression between cell clusters. HVGs were selected by assessment of the relationship of log(variance) and log(mean) and choosing those with the highest variance:mean ratio. Principal component analysis was used to reduce the dimensionality of the dataset, and ElbowGraph to select the number of dimensions for the clustering of significant principal components. Cluster identification was performed using the functions FindNeighbors and FindClusters, which calculate k-nearest neighbors and generate the shared nearest-neighbor graph to cluster cells. The algorithm applied was the Louvain method, which allows tuning of the number of clusters with a resolution parameter. To explore clusters in more detail, either the resolution parameter was increased in FindClusters function or FindSubCluster was used for specific clusters. UMAP was used as a nondimensional reduction method to visualize clustering.

In the case of the aged/control IgG-treated versus aged/anti-IL-17A/F-treated fibroblast clusters, we conducted a subclustering analysis of all populations obtained to detect potential fibroblast populations responding to the blocking treatment. With this we separated the previously named fibroblast 1 cluster obtained in the aged versus adult comparison into three subclusters (fibroblasts 1.0–1.2). Then, fibroblast 2 cluster was divided into two new clusters (fibroblasts 2.0 and 2.1) and fibroblast 3 cluster was separated into four clusters (fibroblasts 3.0–3.3); fibroblast 4 and 5 clusters remained as separate units. This separation allowed detection of subtle differences in the IL-17A/F blockade response in fibroblast subgroups.

Cell type annotation

For annotation of cell types and states, enriched genes were first identified in each of the clusters with the function FindAllMarkers using the Wilcoxon rank-sum test to find cluster-specific markers. These cluster-specific genes were then explored to find previously reported cell population marker genes. Examples of marker genes used to annotate the cell populations include: CD4+ T cells: Cd28 and Cd4; CD8+ T cells: Cd8a and Cd8b1; dermal cells: Cd207; fibroblast 1: Crabp1, Inhba and Notum; fibroblast 2: Col1a1, Col1a2, Cd34, Robo1 and Col3a1; fibroblast 3: Efemp1, Il1r2 and Ccl11; fibroblast 4: Col11a1, Aspn and Coch; fibroblast 5: Myoc and Dcn; ILCs: Il13 and Kit; lymphatic endothelial cells: Lyve1 and Hes1; macrophage 1: Il1b; macrophage 2: Tnfsf9; macrophage 3: Ear2 and Cd163; monocyte 1: Plac8 and Cd14; monocyte 2: Ccl8 and C1qa; monocyte 3: Retnla, C1qb and Ccr2; natural killer cells: Gzmc, Ccl5 and Nkg7; pericytes: Acta2 and Rgs5; proliferating macrophages: Mki67; proliferating T cells: Hmgb2; Schwann cells: Cryab, Plekha4 and Scn7a; Schwann cells 1: Kcna1; Schwann cells 2: Sox10; T regulatory cells: Foxp3; venous endothelial cell arterioles and capillaries: Ptprb and Flt1; venous endothelial cell venules: Aqp1, Sele and Pecam1; and γδ T cells: Trdc and Trgc1. For the annotation of IgG-treated versus anti-IL-17A/F-treated clusters we used the markers mentioned above.

Differential expression analysis for each cluster

To find differentially expressed genes between adult versus aged, and IgG-treated versus anti-IL-17A/F-treated, in different annotated cell type populations, differential expression analysis was performed between conditions for each cluster with the function FindMarkers. To control for multiple comparisons we used the FDR Benjamini–Hochberg method.

Age effect analysis

To assess age-related effects among immune cells, a deep learning approach was created based on autoencoders. During training this method can identify features relevant to the data structure and then use them to predict different cell types in a test dataset. To generate the model, adult data were divided into two smaller subsets that could be used as training and test sets. The model displayed high probability scores (P > 0.5) in the prediction of cell types from the test set among the different immune cell types and a low rate of unpredicted cells (unclassified rate <5%). If the rate of unclassified cell type is strictly biased for aged cells, this would reflect the effect of aging on the cell phenotype. Based on this assumption an age-deviance score was defined as 1 – q, where q is the probability of the unpredicted cell being in the true corresponding cell type class in aged cells. The proportion of unclassified cells within each cell type in aged cells, and the proportion of age-affected cells, were then measured and normalized by the model’s error rate (for example, by subtracting the cell proportion in each adult cell type that could not be predicted by the model).

Cell composition analysis

To evaluate the significance of differences in cell type abundance between conditions we applied sccomp81, a tool designed for differential composition and variability analyses that relies on sum-constrained independent beta-binomial distributions. This model uses mean-variability association, which allows modeling of the compositional properties of the data while enabling the exclusion of outliers. We assessed significant differences (FDR < 0.025, using a Benjamini–Hochberg procedure to control for multiple testing) for composition and/or group-specific variability in each cell type population, comparing adult versus aged conditions and aged/anti-IL-17A/F-treated versus aged/IgG-treated control.

Bioinformatics analyses of bulk RNA-seq data

FastQ files were aligned against the mm10 reference genome using STAR 2.5.2b82 with default options. Unless otherwise specified, all downstream analyses were performed using R 3.5.1. Differentially expressed genes (DEGs) between conditions were determined using DESeq2 1.22.1 (ref. 83), using mm10 gene counts as generated by the function featureCounts from the RSubread package v.1.32.4 (ref. 84), with options annot.inbuilt = ‘mm10’,allowMultiOverlap = TRUE,countMultiMappingReads = FALSE,minMQS = 1,ignoreDup = FALSE). Genes were selected as DEGs with the thresholds |lfcShrink foldChange | >1.25 and Benjamini–Hochberg-determined P < 0.1, using batch as covariate. Gene set enrichment analysis was performed using gene set collections at the Mus musculus gene symbol level. Gene set collections used were GOBP, GOMF, GOCC and KEGG, obtained using the package org.Mm.eg.db (November 2014); GOSLIM, obtained from geneontology.org (November 2014); and Broad Hallmarks, obtained from the Broad Institute MSigDB website (https://www.gsea-msigdb.org/gsea/msigdb/) and mapped from human to mouse genes using homology information from Ensembl biomart archive (July 2016). Analyses were performed using regularized log transformation (rlog) applied to count data using the DESeq2 R package 1.22, with ROAST85 and the MaxMean statistic (http://statweb.stanford.edu/~tibs/GSA/).

GO analysis

GO analysis for scRNA-seq was performed on lists ranked by increasing Benjamini–Hochberg-adjusted P values with g:Profiler86 (https://biit.cs.ut.ee/gprofiler/gost), using the biological processes database. A GO category was considered significant with adjusted P < 0.05. In the aged versus adult comparison, genes with fold change > 0.35 (log2) were considered. In the aged/anti-IL-17A/F-treated versus aged/IgG-treated control, genes with fold change > 0.25 (log2) were considered.

GO analysis for both epidermal bulk RNA-seq and p65 ChIP–seq was performed with enrichR87 (https://maayanlab.cloud/Enrichr/). A category was considered significant when P < 0.01.

Statistics and reproducibility

No statistical methods were used to predetermine sample size. This was decided taking into the account variability between samples, particularly in aged mice, and was based on our expertise with the skin of aged mice1,2,22.

In general graphs show individual values and median (depicted as a line) and P values were obtained using the nonparametric two-tailed Mann–Whitney U-test with Prism v.9, unless otherwise stated in the figure legends.

Data collection and analysis were not performed blind to the conditions of the experiments, except for the histopathological wound healing analysis.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Sources

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2/ https://www.nature.com/articles/s43587-023-00431-z

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