Raw LC–MS/MS and Bulk RNA-Seq data

LC–MS/MS raw data

Raw files were searched against species-specific proteomes, using Proteome Discoverer Software 2.0 with SEQUEST HT and MS Amanda search engines.  Our search parameters identified fixed modifications, including cysteine carbamidomethylation, variable methionine oxidation, lysine carbamylation, and N-terminal acetylation and oxidation. The maximum number of missed cleavages permitted was two. Mass tolerance for precursor ions was set to 50 ppm and for fragment ions, 0.6 Da. Peptides with an Xcorr threshold ≤1% were subjected to validation through the MS Amanda search engine. A strict peptide false positive rate of 5% was used to accept proteins based on spectral match. Each distinct sample was analyzed in technical triplicates. Proteins identified in the extracellular spaces of rat or mouse crush-injured sciatic nerves are reported. Mass spectrometry interaction STatistics (MiST) scores were calculated, based on the abundance of the protein captured, reproducibility of capture, and specificity relative to the control bait (Free Fc) (Verschueren et al., 2016). CCR2/CCR4-binding interactions that generated MiST scores higher than 0.84 were reported

Bulk RNA-Seq data

Quality control of the raw fastq files was performed using the software tool FastQC (Andrew S., 2010) v0.11.8. Sequencing reads were trimmed with Trimmomatic (Bolger AM et al., 2014) v0.38 and aligned to the rat genome (mRatBN7.2 (The genomic reference consortium) using the STAR aligner (Dobin A et al., 2014) v2.5.1a. Read quantification was performed with RSEM (Li B. et al., 2011) v1.3.0 and the Ensembl release 107 annotation. The R BioConductor packages edgeR (Robinson MD et al., 2010) and limma (Ritchie ME et al., 2015) were used to implement the limma-voom (Law CW et al., 2014) method for differential expression analysis. The voom method was employed to model the mean-variance relationship in the log-cpm values weighted for inter-subject correlations in repeated measures of patients, after which lmFit was used to fit per-gene linear models and empirical Bayes moderation was applied with the eBayes function. Significance was defined by using an adjusted p-value cut-off of 0.4 after multiple testing correction (Benjamini Y et al., 1995) using a moderated t-statistic in limma and a minimum absolute log-fold-change threshold of 0