Nanoacquity uplc xevo qtof ms system

All proteomic analyses were performed according to protocols previously described elsewhere [24 (link), 25 (link)]. Nine animals per group were euthanized and used in the proteomic approach. The samples of motor cortex from two animals were pooled, and all the procedures were carried out in triplicate. Briefly, the proteomic consists of protein extraction by lysis buffer. Then, the samples were reduced, alkylated, and finally digested by trypsin and desalted by C18 spin column (Pierce, Thermo Fisher, USA). Afterward, the samples were resuspended in the solution containing 12 μL of alcohol dehydrogenase standard (1 pmol/μL) + 108 μL of 3% acetonitrile and 0.1% formic acid.
The reading and identification of the peptides were performed on a nanoAcquity UPLC-Xevo QTof MS system (Waters Corporation, Wilmslow, UK), which were interpreted by Protein Lynx Global Server (PLGS) software applying the Monte-Carlo algorithm. After comparing the experimental groups, it was considered p < 0.05 for downregulated proteins and 1 − p > 0.95 for upregulated proteins. It was used the Rattus norvegicus proteome downloaded from Uniprot. After, the proteins identified were analyzed by a bioinformatic approach using Cytoscape 3.6.1 (Java®) with ClueGO plugin [26 (link)].

The mass spectrometry system used for the proteomic approach was a nanoAcquity UPLC-Xevo QTof MS system (Waters, Manchester, UK), using the Protein Lynx Global Server (PLGS) software, after downloading the Uniprot database. The difference in expression between the groups was analyzed by t-test (p < 0.05), using the PLGS software. After protein identification and categorization, Cytoscape 3.6.1 (Java^®) software was used for bioinformatics analyses with the ClusterMarker plugin for protein-protein interaction networks and the ClueGO plugin for the determination of biological process groups (Bindea et al., 2009 (link)).

The reading and identification of the peptides were performed on a nanoAcquity UPLC-Xevo QTof MS system (Waters, Manchester, UK), using the Protein Lynx Global Server (PLGS), as previously described by Lima-Leite et al. [73 (link)]. PLGS software, applying the Monte-Carlo algorithm, was used to obtain the difference of protein expression between the groups, considering p < 0.05 for down-regulated proteins and p > 0.95 for up-regulated proteins. The database used for protein identification was the Rattus norvegicus (reviewed only, UniProtKB/Swiss-Prot) download on June 2019 from UniProtKB (http://www.uniprot.org/). After, the proteins identified were analyzed by a bioinformatic approach using Cytoscape 3.6.1 (Java^®) with ClusterMarker plugin for protein-interaction network, and for determination of biological processes groups we used ClueGO plugin [74 (link)].

Peptides identification was performed as previously described ¹⁵ on a nanoACQUITY UPLC-Xevo QTof MS system (Waters, Manchester, New Hampshire UK). The nanoACQUITY UPLC was equipped with nanoACQUITY HSS T3, analytical reverse phase column (75 μm x 150 mm, 1.8 μm particle size, Waters).
ProteinLynx Global Server (PLGS) version 3.0 (Waters Co., Manchester, New Hampshire, UK) was used to process and search the continuum LC-MSE data. Proteins were identified with the embedded ion accounting algorithm in the software and a search of the Homo sapiens database (reviewed only, UniProtKB/ Swiss-Prot), downloaded on June 2015 from UniProtKB ( http://www.uniprot.org/ ). The identified proteins were classified and assigned by biological function ^16,25 , origin and molecular interaction ( http://www.uniprot.org/ ).
For label-free quantitative proteome, three MS raw files from each pooled group were analysed using the PLGS software. All the proteins identified with confidence score greater than 95% were included in the quantitative analysis. Identical peptides from each triplicate by sample were grouped based on mass accuracy (<10 ppm) and on time of retention tolerance <0.25 min, using the clustering software embedded in the PLGS. Difference in expression among the groups was expressed as p<0.05 for down-regulated proteins and 1-p>0.95 for up-regulated proteins.

A total of 6 animal per group was used in these analyses. The methodology was carried out in biological triplicate, after pooling two animals from the same group into one single sample. After, the samples were submitted to a cryogenic mill, followed by protein extraction by buffer lysis under constant stirring and 4 °C. Then, a standardized protein concentration was determined by Bradford’s method (1 μg/μL) and a fixed protein amount (50 μg) was used for the following steps: alkylation, digestion, desalting and elution. The reading and identification of the peptides was performed by using the nanoAcquity UPLC-Xevo QTof MS system (Waters Co., UK). Detailed methodology is available elsewhere^{23 (link),26 (link)}.

The peptide identification was performed on a nanoAcquity UPLC-Xevo QTof MS system (Waters Corporation, Manchester, UK), as previously described^{32 (link)}. Differences in expression among groups were obtained using the ProteinLynx Global Server (PLGS) software provided by Waters Corporation and are expressed as p < 0.05 for downregulated proteins and 1 − p > 0.95 for upregulated proteins. Bioinformatics analysis was performed for comparison of the groups exposed to F relative to the control group (Tables S1–S6), as reported earlier^{32 (link)–35 (link)}.

The identification and reading of the peptides in the samples was performed with nanoAcquity UPLCXevo QTof MS system (Waters, Mancester, UK), using the Protein Lynx Global Server (PLGS), as previously described by Lima Leite et al., (2014) [85 (link)]. The PLGS software, applying the Monte-Carlo algorithm, was used to obtain the protein expression difference between the groups, considering p < 0.05 for down-regulated proteins and p > 0.95 for up-regulated proteins. Protein identifications were determined by downloading Uniprot databases. Then, bioinformatic analyses were performed using Cytoscapes 3.6 (Java) with the ClueGO plug-in for the determination of biological process groups, based on Gene Ontology annotations [86 (link)].