Proteomics

Dataset Information

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Quantitative plasma proteome of mild cognitive impairment


ABSTRACT: Multiple Affinity Removal Spin Cartridge Human 14 kit (Agilent) was used to remove 14 major proteins from plasma. Samples were denatured with an equivalent volume of trifluoroethanol and reduced with dithiothreitol. Free cysteine residues were alkylated with iodoacetamide. Samples were incubated with trypsin and desalted with C18 ZipTip. Desalted samples were rehydrated in 0.1% formic acid (FA) and were analyzed by LC-MS using a nanoLC Eksigent 400 system (Eksigent, AB Sciex), coupled online to an TripleTOF6600 mass spectrometer (AB Sciex). Peptide separation was performed using liquid chromatography with a trap and elution configuration using a nano trap column (350 μm × 0.5 mm, 3 μm, 120 Å, AB Sciex) and a nano ChromXP C18 reverse phase column (75 μm × 15 cm, 3 μm, 120 Å, AB Sciex) at 300 nl/min with a 90 min linear gradient of 8-30% acetonitrile in 0.1% FA, and then, with a 10 min linear gradient of 30% to 40% acetonitrile in 0.1% FA. The mass spectrometer was operated in information-dependent acquisition (IDA) mode, scanning full spectra (400–1500 m/z) for 250 ms, followed by up to 30 MS/MS scans (100–1800 m/z for 50 ms each), for a cycle time of 1.8 s. Candidate ions with a charge state between +2 and + 5 and counts above a minimum threshold of 125 counts per second were isolated for fragmentation, and one MS/MS spectrum was collected before adding those ions to the exclusion list for 12 s. Rolling collision energy was used with a collision energy spread of 15. The mass spectrometer was operated using the Analyst TF 1.7.1 software program (AB Sciex). For data dependent acquisition (DDA, SWATH acquisition), the parameters were set as follows: 100 ms TOF MS scan, followed by 200 variable SWATH windows each at 50 ms accumulation time for m/z 400–1250. MS/MS SWATH scans were set at 5 Da window overlapping by 1 Da for m/z 400–1250 and varied on each side of the mass range. The total cycle time was 9.6 s. A rolling collision energy (CE) parameters script was used to automatically control the CE. Acquired spectra were searched against the UniProt reviewed database using the Paragon algorithm embedded in the ProteinPilot 5.0.1 software program (AB Sciex), with the following search parameters: (i) sample type: identification, (ii) Cys alkylation: iodoacetamide, (iii) digestion: trypsin, (iv) instrument: TripleTOF 6600, (v) species: Homo sapiens, (vi) ID focus: biological modifications, (vii) detected protein threshold: > 0.05 (10% confidence). The detected protein threshold was set to the minimum level to enhance the number of wrong answers to enable the curve fitting by an independent FDR analysis. This was carried out by the target-decoy approach provided with the ProteinPilot software program, which was used to assess the quality of the identifications. Positive identifications were considered when identified proteins and peptides reached a 1% local FDR. The resulting group file was loaded into Peakview (v2.2.0, AB Sciex) and peaks from SWATH runs were extracted with a peptide confidence threshold of 99% and a false discovery rate <1%. The SWATH files were then exported to the MarkerView software program (version 1.3.0.1; AB Sciex) and the peak areas of individual peptides were normalized to the sum of the peak areas of all detected peptides.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Ken Itoh 

PROVIDER: PXD025945 | JPOST Repository | Wed May 11 00:00:00 BST 2022

REPOSITORIES: jPOST

Dataset's files

Source:
Action DRS
180628_MCI001_2-1_SWATH.wiff.scan Wiff
180702_Control101_SWATH.wiff.scan Wiff
180702_Control102_SWATH.wiff.scan Wiff
180702_Control103_SWATH.wiff.scan Wiff
180702_Control104_SWATH.wiff.scan Wiff
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Publications


<h4>Background</h4>Since dementia is preventable with early interventions, biomarkers that assist in diagnosing early stages of dementia, such as mild cognitive impairment (MCI), are urgently needed.<h4>Methods</h4>Multiomics analysis of amnestic MCI (aMCI) peripheral blood (n = 25) was performed covering the transcriptome, microRNA, proteome, and metabolome. Validation analysis for microRNAs was conducted in an independent cohort (n = 12). Artificial intelligence was used to identify the most i  ...[more]

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