Comparison of Two Solid-Phase Extraction (SPE) Methods for the Identification and Quantification of Porcine Retinal Protein Markers by LC-MS/MS

Abstract

Proper sample preparation protocols represent a critical step for liquid chromatography-mass spectrometry (LC-MS)-based proteomic study designs and influence the speed, performance and automation of high-throughput data acquisition. The main objective of this study was to compare two commercial solid-phase extraction (SPE)-based sample preparation protocols (comprising SOLAµTM HRP SPE spin plates from Thermo Fisher Scientific and ZIPTIP® C18 pipette tips from Merck Millipore) for analytical performance, reproducibility, and analysis speed. The house swine represents a promising animal model for studying human eye diseases including glaucoma and provides excellent requirements for the qualitative and quantitative MS-based comparison in terms of ocular proteomics. In total six technical replicates of two protein fractions [extracted with 0.1% dodecyl-ß-maltoside (DDM) or 1% trifluoroacetic acid (TFA)] of porcine retinal tissues were subjected to in-gel trypsin digestion and purified with both SPE-based workflows (N = 3) prior to LC-MS analysis. On average, 550 ± 70 proteins (1512 ± 199 peptides) and 305 ± 48 proteins (806 ± 144 peptides) were identified from DDM and TFA protein fractions, respectively, after ZIPTIP® C18 purification, and SOLAµTM workflow resulted in the detection of 513 ± 55 proteins (1347 ± 180 peptides) and 300 ± 33 proteins (722 ± 87 peptides), respectively (FDR < 1%). Venn diagram analysis revealed an average overlap of 65 ± 2% (DDM fraction) and 69 ± 4% (TFA fraction) in protein identifications between both SPE-based methods. Quantitative analysis of 25 glaucoma-related protein markers also showed no significant differences (P > 0.05) regarding protein recovery between both SPE methods. However, only glaucoma-associated marker MECP2 showed a significant (P = 0.02) higher abundance in ZIPTIP®-purified replicates in comparison to SOLAµTM-treated study samples. Nevertheless, this result was not confirmed in the verification experiment using in-gel trypsin digestion of recombinant MECP2 (P = 0.24). In conclusion, both SPE-based purification methods worked equally well in terms of analytical performance and reproducibility, whereas the analysis speed and the semi-automation of the SOLAµTM spin plates workflow is much more convenient in comparison to the ZIPTIP® C18 method.

Glycopatterns and Glycoproteins Changes in MCN and SCN: A Prospective Cohort Study

“…The heat map in Figures 5(b) and 5(c) shows the expression of WGA- and STL-binding glycoproteins in MCN and SCN accordingly. In the three replications of each sample, these glycoproteins were based on the quantitative LC-MS/MS results, because the glycoproteins are mostly low-abundance proteins [25]. In this study, the label free quantification (LFQ) intensity was found generally in the order of e 8-9 .…”

Delineation of the Olive Pollen Proteome and Its Allergenome Unmasks Cyclophilin as a Relevant Cross-Reactive Allergen

“…In addition, it has been reported that the use of different solid-phase extraction methods (i.e. ZipTip vs SOLAµ) after in-gel trypsin digestion and prior to nLC-MS/MS analysis can increase the coverage in the proteome (52). Moreover, previous studies have demonstrated that pollen grains from different olive cultivars differ in the quantity and number of proteoforms, including those of important allergens like Ole e 1, Ole e 2 and Ole e 5 (53)(54)(55).…”

An In-Depth View of the Porcine Trabecular Meshwork Proteome

“…MS parameters were set as follows: detection range= 300–2000 m/z , LTQ injection time = 50 ms, FT injection time = 500 ms, normalized energy of collision induced decay (CID) = 35, activation time = 30 ms, activation Q = 0.25, m/z isolation width for fragmentation = 2, dynamic exclusion = 90 s, repeat duration = 30 s, resolution = 30,000, centroid detection, fragmentation selection = top 5 monoisotopic m/z signals ( z = 1+, 2+, 3+ and 4+, intensity >500). The BULCMS workflow was established in previous studies analyzing neuroretinal cell extracts [62,63] as well as human and porcine retinal tissues [21,52,64]. All resulting LC MS/MS Thermo RAW data were combined for protein identification using database-dependent protein identification in Proteome Discoverer (version 1.1, Thermo Fisher Scientific, Rockford, IL, USA) implementing the MASCOT search engine (version 2.2.07).…”