Changes in the Oligodendrocyte Progenitor Cell Proteome with Ageing.

Following central nervous system (CNS) demyelination, adult oligodendrocyte progenitor cells (OPCs) can differentiate into new myelin-forming oligodendrocytes in a regenerative process called remyelination. Although remyelination is very efficient in young adults, its efficiency declines progressively with ageing. Here we performed proteomic analysis of OPCs freshly isolated from the brains of neonate, young and aged female rats. Approximately 50% of the proteins are expressed at different levels in OPCs from neonates compared with their adult counterparts. The amount of myelin-associated proteins, and proteins associated with oxidative phosphorylation, inflammatory responses and actin cytoskeletal organization increased with age, whereas cholesterol-biosynthesis, transcription factors and cell cycle proteins decreased. Our experiments provide the first ageing OPC proteome, revealing the distinct features of OPCs at different ages. These studies provide new insights into why remyelination efficiency declines with ageing and potential roles for aged OPCs in other neurodegenerative diseases.

Oligodendrocyte progenitor cells (OPCs) are the cells responsible for generating new myelin sheath-forming oligodendrocytes in the regenerative process of remyelination. Remyelination restores rapid conduction of action potentials and metabolic support to axons, and prevents axonal degeneration in chronic demyelinating diseases such as multiple sclerosis (MS) (1). During remyelination, adult OPCs activate, divide, migrate and differentiate into myelin forming oligodendrocytes in response to a demyelinating insult (2, 3, 4). Remyelination is a highly efficient process in young adults: however, as with all regenerative processes, its efficiency declines progressively with ageing (5). A combination of environmental changes, such as the capacity of phagocytic cells to remove myelin debris (6, 7, 8), as well as cell-intrinsic epigenetic changes (9), contribute to the age-associated decline in remyelination efficiency (10). This decline is thought to be a major determinant of disease progression in MS patients, and is proposed to be a significant factor in the transition into the largely untreatable progressive phase of the disease (11).

Several brain related RNA-Seq datasets have been published recently (2, 12, 13), including recent RNA datasets comparing OPCs at different ages (14, 15). However, the correlation between RNA expression and the expression of proteins can be low (16, 17): hence, there is clear need for a comprehensive quantitative analysis of ageing OPCs proteome. Other publications have addressed the proteome of OPCs derived from human embryonic stem cells (18), the proteome of OPCs during differentiation to oligodendrocytes (19) and the proteome of various CNS specific cell types in the mouse brain, including oligodendrocytes (17). However, none of these have compared the changes in OPCs associated with ageing, which is a key factor in remyelination failure during disease progression (5, 10). To address the changes in protein abundance observed within ageing OPCs, we performed a comprehensive quantitative proteomic analysis of neonatal, young adult and aged rat OPCs. The OPC proteomes we have delineated represent a new and important resource to understand the age-related changes that underlie remyelination failure, which will help develop identify remyelination-enhancing approaches and define the role of OPCs in other age-related diseases.

Oligodendrocyte Progenitor Cell Isolation

OPC were isolated from neonatal (P0-P2), 3–4 months old and 15–18 months old Sprague-Dawley rats. Briefly, rats were euthanized by overdose of intraperitoneal injection of Pentoject and the decapitated following schedule 1 procedures (Animal Scientific Procedure Act 1986). The brains were removed from the skull and put in to “Hibernate A” (prepared in-house) (20). Meninges were removed and the brains were cut into small pieces (about 1 mm3). The brains were digested with a papain solution containing papain (33U/ml) and DNase (0.04 mg/ml) in Hibernate A for 40 min at 35 °C. Following tissue digestion papain was washed out with Hank's Balanced Salt Solution (HBSS) (−/−) (Gibco, Altrincham, UK) via centrifugation. The tissue was then triturated with a polished glass Pasteur pipette in Hibernate A with 1×B27 (Gibco) and 2 mm sodium pyruvate (Gibco). After trituration, the single cell suspension was passed through a 70 μm strainer and centrifuged for 20 min at 800 × g in a 22.5% Percoll (GE Healthcare, Amersham, UK) solution with DMEM F12(Gibco). Following centrifugation Percoll was removed by aspiration and the cells were washed with HBSS (−/−) (Gibco). Cells were incubated with anti-A2B5 antibody (Millipore, Dorset, UK, MAB312) for 25 min at 4 °C (2 μg of antibody per 107 cells) in wash buffer (Miltenyi Wash Buffer [MWB] = 0.5% BSA, 2 mm sodium pyruvate and 2 mm EDTA in 1× PBS). Excess antibody was washed with HBSS (−/−) and cells were incubated with secondary IgM-microbeads (Miltenyi Biotec) for 15 min at 4 °C (20 μl per 107 cells) in MWB. Excess antibody was again removed by washing with HBSS (−/−) and cells were magnetically sorted following the manufacturer's instructions (Milteny Biotech, Surrey, UK). Cells were eluted with OPC media (DMEM F12, 2 mm sodium pyruvate, 60 μg N-acetyl-cysteine (Sigma-Aldrich), 5 μg/ml Insulin (Gibco), 21 mmd-Glucose (Sigma-Aldrich), 50 μg/ml apo-transferrin (Sigma-Aldrich), 16.1 μg/ml putrescine (Sigma-Aldrich), 40 ng/ml sodium-selenite (Sigma-Aldrich) and 60 ng/ml progesterone (Sigma-Aldrich). Upon OPC removal cells were incubated with 2 μl of Biotin-MOG antibody per 107cells (R and D systems, Abingdon, UK) for 25min at 4 °C in 500 μl MWB to label oligodendrocytes. Excess antibody was washed with HBSS (−/−) and cells were incubated with secondary IgM-microbeads (Miltenyi Biotec) for 15 min at 4 °C (20 μl per 107 cells) in MWB. Excess antibody was again removed by washing with HBSS (−/−) and cells were magnetically sorted following the manufacturer's instructions (Miltenyi Biotech). Oligodendrocytes were also eluted with OPC media.

Experimental Design and Statistical Rationale

OPCs isolated from neonatal (P0-P2), 3–4 months old and 15–18 months old rats were processed as depicted below. Each biological replicate and condition consisted of cells isolated from independent rats. A sample size of six biological replicates was used for each age (neonatal, young, and aged OPCs) providing us with sufficient numbers to accommodate variability. The samples were labeled using TMT 10 plex (ThermoFisher Scientific, Altrincham, UK) in which each sample was labeled with a different isotype to allow the distinction of the proteome of each sample. The 6 biological sample were divided into two different TMT 10 plexes (named as multiplex 1 and 2) to enable labeling of all the samples avoiding a potential plate effect and to provide relative quantitation across the three ages. Multiplex 1 included neonatal 1–3, young 1–3 and aged 1–3 sample, whereas multiplex 2 included neonatal 4–6, young 4–6 and aged 4–6. A sample consisting of pooled material from all replicates and conditions was labeled with the 10th TMT tag with the intent of minimizing the number of missing values between multiplexes but was not used for statistical analysis. Furthermore, each multiplex containing three biological replicates of each age was pre-fractionated as describe below and each fraction ran twice in different rounds of LC-MS to increase the proteome coverage. We used LIMMA (linear model for microarray analysis) (23) statistical analysis because of its robustness in dealing with the number of missing values and variability encountered on high-throughput quantitative proteomic studies of primary cells as ours. Neonates were used as control condition except when comparing young versus aged rats where young was the control.

Sample digestion and TMT labeling

Cells obtained by magnetic cell sorting were pelleted by centrifugation and supernatant was removed. The cell pellet was lysed using lysis buffer (8 m urea, 100 mm triethylammonium bicarbonate [TEAB] pH 8.0) (Life-Technologies, Altrincham, UK) followed by freeze-thawing in dry ice and ultrasonic bath incubation. Each lysate was immediately reduced with 20 mm dithiothreitol (DTT) (Sigma-Aldrich) in TEAB at RT for 60 min, alkylated with 40 mm iodoacetamide (Sigma-Aldrich) in the dark for 60 min at room temperature (RT) and digested overnight at RT with 1 μg endoproteinase Lys-C (Promega, Madison, Winsconsin). The following day, the solution was diluted to a final urea concentration of 1 m and 1 μg of modified trypsin (Promega) per 100 μg of cell lysate was added and incubated for 3 h at RT. The samples were acidified with trifuoroacetic acid (TFA) (Life Technologies) (0.1% (v/v) final concentration) and debris were pelleted by centrifugation at 15,000 × g for 10 min and supernatant frozen at −80 °C until peptide concentration and clean-up.

PorosOligo R3 (Life-Technologies) resin was equilibrated in 0.1% TFA. Each sample was desalted by sequentially incubating for 5 min with occasional vortexing with 20 μl of slurry (about 10 μl resin). The samples were then spun down, and the supernatant transferred to a fresh microtube with another 20 μl slurry. This step was repeated 3 times. Peptide-loaded resins were combined and packed into p200 tips over a 3 m Empore C8 “plug” (about 1 cm long), washed twice with 50 μl 0.1% TFA and eluted with 200 μl 60% acetonitrile (ACN) (adapted from Rappsilber et al. 2003 (21)) and quantified using QubitTM (Life Technologies). The volume equivalent to 12 μg of peptides (equivalent to the amount of peptide in the lowest yielding condition) were transferred to new microtube to be dried down before TMT-labeling.

Samples were labeled according to manufacturer's instructions (ThermoFisher Scientific) with minor modifications. Briefly, each dry desalted sample was resuspended in 122.5 μl 100 mm TEAB and each TMT label (0.8 mg) resuspended and mixed in 41 μl of pure acetonitrile, then mixed for labeling. Samples were labeled for one hour at RT, quenched with 5% hydroxylamine for 15 min and multiplexed. Each multiplex was then dried and stored at −80 °C for pre-fractionation for total proteome analysis.

Alkaline Reverse-phase Prefractionation and LC-MS/MS Analysis

TMT-10plex labeled peptides from OPCs were combined and fractionated using the alkaline reversed-phase chromatography (22) on Dionex Ultimate RSLC system. Separation was carried out on an XBridge BEH C18 column (2.1 × 100 mm, 5 μm, 130 Å) (Waters) using a gradient of acetonitrile (2 to 98%) in 10 mm TEAB with a flow rate of 300 μl/min and a running buffer pH = 10. Ninety-six fractions were collected and concatenated into 18 fractions (supplemental Fig. S1) and concentrated to dryness using a speedvac with a chilled vacuum trap and stored at −80 °C.

Eighteen fractionated peptide samples were analyzed in replicate on an Orbitrap Fusion Tribrid (Thermo Scientific) mass spectrometer interfaced with Dionex Ultimate 3000 nanoRSLC system. Nanospray LC system comprised of a 20 mm enrichment column (ReproSil-Pur 120 C18-AQ, 7 μm, Dr. Maish, Ammerbuch-Entringen, Germany) and a Picofrit (New Objective) analytical column (ReproSil-Pur 120 C18-AQ, 2.4 μm, 50 cm length). Peptides were separated using a gradient of acetonitrile with 0.1% formic acid up to 80% over a period of 110 min. TMT labeled peptides were analyzed using the following data dependent acquisition parameters: Scan range for full MS, m/z 400–1600, resolution 120,000 (200 m/z), charge state(s) 2–7, higher collision dissociation fragmentation (The fragmentation mode was a MS/MS fragmentation of top 15 most intense peptide ion peaks fragmented through higher energy collisional dissociation (HDC) with 35% energy) at 60,000 resolution, precursor isolation window m/z 1.3 with m/z 0.3 offset. Fragmented peptides were excluded for 30 s, monoisotopic selection precursor (MIPS) was enabled with constant internal calibration using fluoranthene ion.

Mass spectrometry data were analyzed using Proteome Discoverer 2.1 (PD) (Thermo Fisher Scientific) software with search engines Mascot v2.6.0, Amandav2.1.5.4882 and Sequest HTv.1.1.0.158 nodes. Data was searched using latest Uniprot Rattus norvegicus protein database with common laboratory contaminants included (2016_03 version with 30030 entries including Rattus norvegicus and common laboratory contaminant sequences). Search parameters included 2 missed cleavage sites, oxidation (M) and deamidation (N, Q) as variable modifications. Tandem mass tag (229.163Da) at N terminus and lysine residue and carbamidomethylation on cysteine residue were set as fixed modifications. The mass tolerances on precursor and fragment masses were set at 10 ppm and 0.05 Da, respectively. False discovery rate (FDR) cut-off value was set at 0.01. Unique peptide spectrum matches with protein information and non-normalized reporter ion intensity was exported and used for statistical analysis.

Statistical Analysis of TMT-labeled Samples

Statistical analysis of TMT labeled samples was performed with RStudio® software. The data from the time course analysis consisted of exported peptide-to-spectrum matches (PSMs) from two multiplexes containing three biological replicates each (a total of 6 biological replicates). Identification results from technical replicates and/or fractions were merged and any repeated protein groups were removed. PSMs having ≥50% isolation interference were removed. PSMs from proteins from the common contaminant database were removed. The false discovery rate (FDR) for PSM assignments was calculated against a decoy database using Percolator v2.05 node. PSMs with missing value in one or more conditions within a biological replicate were removed. The intensity values of each sample and each TMT label were log2-transformed and then subjected to median sweeping on the log2 intensity data by each plate which provides a protein abundance value for further LIMMA (Linear Model for Microarray Analysis) statistical analysis (23).

LIMMA analysis provides sufficient power to deal with low replicate numbers and additional missing values (24). We therefore carried out unpaired LIMMA analysis comparing all and corrected them for multiple testing using the R function provided within VSClust (25). All proteins with q-values below 0.05 (5% FDR) and log2 change over 0.6 (more than 1.5-fold change) when compared with neonates were considered to be regulated. In the case of young adult and aged OPC comparison, all proteins with q-values below 0.05 (5% FDR) and log2 change over 0.6 (more than 1.5-fold change) were considered significantly regulated.

Downstream Bioinformatics Analysis

All protein identification data were mapped to gene symbols using UniProt version 2016_03 (26). Annotation for Gene Ontology (GO) pathways such as biological processes, molecular function, cellular compartment as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using DAVID (27). When analyzing the GO terms of the total protein groups detected, the whole proteome included in DAVID for Rattus norvegicus was used as background using Benjamini-Hochberg (28). The statistical test PCA clustering analysis was performed using the Log2 transformed protein intensity. For the cluster analysis, we calculated the log2 protein intensity mean over all biological replicate values for each condition of those proteins with q-value < 0.05 and log2 change over 0.6 (more than 1.5 fold change) when compared neonatal OPCs with young and aged OPCs. Fuzzy c-means clustering (29) was applied using the “FcmClusterPEst.R” R function (https://bitbucket.org/veitveit/vsclust/src/master/ (25)) and “Mfuzz” R library. We determined the value of the fuzzifier and obtained the number of clusters according to Schwämmle and Jensen (30) (supplemental Table S3). The GO term analysis for the different clusters obtained with Fuzzy c-means clustering as well as for the protein groups with more than 1.5-fold change expression between young and aged OPCs were obtained with DAVID, using the total protein groups detected in our proteome as background. For the heatmap, the Z score from each age category was used. The Z score was calculated using the scale function within RStudio® software. The heatmaps were calculated using the “pheatmap” R package from Kolde (https://cran.r-project.org/web/packages/pheatmap/) from the R Bioconductor tool set and the protein groups shown in each heatmap were obtained from the corresponding DAVID GO term shown in the tittle of each heatmap. To analyze GO terms of the differentially expressed proteins in the different aged group, each list was uploaded to DAVID and compared with the total proteome detected in the Ageing OPC proteome. Then, to elaborate the list of proteins involved in each of the GO terms, the Uniprot accession protein list provided by DAVID was used. The GO terms and the Uniprot accession protein lists associated to each GO term are provided in the supplemental Table S5 and the fold enrichment of the GO terms that were significantly enriched in the category Biological processes (supplemental Fig. S3), KEGG pathway (supplemental Fig. S4), Cellular component (supplemental Fig. S5), or Molecular function (supplemental Fig. S6) in each of the clusters was represented using a heatmap value for the fold enrichment compared with the total proteome, which was used as background. The GO term network for proteins differentially regulated between young and aged proteins was created using the Cytoscape application BINGO (31). In supplemental Fig. S7, the different GO biological processes that are associated are linked with a gray line. The size of the node indicates the number of proteins among the regulated proteins that are involved in that GO term. The bigger the node the higher the number or proteins. Each node is then divided in 2 colors, blue and orange. Blue indicates proteins that are downregulated in aged OPCs and orange proteins that are up-regulated in aged OPCs. Again, the size of each color is associated to the number of proteins.

The correlation coefficient between RNA expression from young and aged OPCs and the proteome of young and aged OPCs was performed as follows: RNAseq data (GSE134765) was pre-processed through sortMeRNA (Kopylova et al., 2012) to filter out ribosomal RNA reads, adapter was trimmed and quality paired reads were extracted using Trimmomatics (32). Rat cDNA (Rnor_6.0) was indexed and reads were quantified using Salmon quasi mode (33). Salmon output was processed through tximport (34) and Deseq2 (35) and normalized length scaled TPM counts were calculated. Normalized log2 transformed values of transcript levels were used for correlation analysis with log2 transformed proteomic data set (15).

Ensemble gene IDs corresponding to Uniport protein IDs were extracted. Genes for which expression value was not determined ('NA') in any one sample were removed. Finally, 6110 genes for 'young' OPC samples and 6101 genes for ‘aged’ OPC samples remained. For each gene, a correlation coefficient (Biweight midcorrelation or bicor) between transcript levels and protein levels was estimated and the corresponding Student p value was determined using bicorAndPvalue function (36) in “R.” The advantage of bicor over Pearson's correlation coefficient is based on its robust measurement in presence of outliers. p value was adjusted using FDR (Benjamini-Hochberg) and the adjusted p value <0.05 was considered significant.

Western Blot Analysis

Protein lysate was mixed with NuPAGE loading buffer and NuPAGE reducing agent (Life Technologies) and boiled for 10 min at 95 °C. Five μg of protein lysate were loaded into Bolts 4–12% gels (LifeTechnologies). Electrophoresis gel was run at 120 volts (V) for 90 min and then gels were transferred onto a methanol pre-activated PVDF membrane (Millipore). The transfer was done using 1X Bio-Rad transfer buffer with 20% of ethanol at 100V for 90 min. Upon transfer the membranes were blocked with Blocking buffer (TBS blocking agent 1:1 (Li-Cor, Lincoln, Nebraska) with TBS (Fischer Scientific)-0.1% Tween (Sigma-Aldrich)) for 1 h at RT. Membranes were incubated overnight at 4 °C with the different antibodies (Rabbit MOBP (Abbexa, Cambridge, UK, abx002902) 1:500; Rat MBP (Serotec, Kidlington, UK, MCA490S) 1:500; Mouse PADI2 (Proteintech, Manchester, UK, 66386–1-lg) 1:500; Goat FABP5 (Cell Signaling Technologies, Leiden, Netherlands, 39926) 1:500; Mouse CNPase (Sigma-Aldrich, C5922) 1:500; Goat MOG (R and D systems, AF2439) 1:1000; Rabbit PLP (Abcam, Cambridge, UK, ab28486) 1:500; Mouse CRYAB (Abcam, ab13496) 1:500; Rabbit proteasome 19S (Abcam, ab137109), alpha tubulin (1:1000) (Millipore, T9026). Following antibody incubation membranes were washed with TBS-0.1% Tween (TBST) three times for 10 min. Membranes were then incubated with fluorophore conjugated secondary antibodies (Li-Cor) for 2 h at RT and developed using Li-Cor developing machine. Western blot analysis was performed measuring the integrated density through the Li-Cor software and normalized by the signal of HRP conjugated β-actin (Sigma-Aldrich, Birmingham, Alabama). Statistical analysis was performed with GraphPad Prism version 8.0. First, normality was tested and if the number of samples was too low, normality test using residuals was performed. If samples were normally distributed 1 way, ANOVA with Sidak's multiple comparisons test was performed and if samples were not normally distributed samples were analyzed using Kruskal Wallis and Dunn's Multiple Comparison Tests. Statistics were represented as follows: ns (not significant), *