Comparative Analysis of Label-Free and 8-Plex iTRAQ Approach for Quantitative Tissue Proteomic Analysis

Abstract

High resolution proteomics approaches have been successfully utilized for the comprehensive characterization of the cell proteome. However, in the case of quantitative proteomics an open question still remains, which quantification strategy is best suited for identification of biologically relevant changes, especially in clinical specimens. In this study, a thorough comparison of a label-free approach (intensity-based) and 8-plex iTRAQ was conducted as applied to the analysis of tumor tissue samples from non-muscle invasive and muscle-invasive bladder cancer. For the latter, two acquisition strategies were tested including analysis of unfractionated and fractioned iTRAQ-labeled peptides. To reduce variability, aliquots of the same protein extract were used as starting material, whereas to obtain representative results per method further sample processing and MS analysis were conducted according to routinely applied protocols. Considering only multiple-peptide identifications, LC-MS/MS analysis resulted in the identification of 910, 1092 and 332 proteins by label-free, fractionated and unfractionated iTRAQ, respectively. The label-free strategy provided higher protein sequence coverage compared to both iTRAQ experiments. Even though pre-fraction of the iTRAQ labeled peptides allowed for a higher number of identifications, this was not accompanied by a respective increase in the number of differentially expressed changes detected. Validity of the proteomics output related to protein identification and differential expression was determined by comparison to existing data in the field (Protein Atlas and published data on the disease). All methods predicted changes which to a large extent agreed with published data, with label-free providing a higher number of significant changes than iTRAQ. Conclusively, both label-free and iTRAQ (when combined to peptide fractionation) provide high proteome coverage and apparently valid predictions in terms of differential expression, nevertheless label-free provides higher sequence coverage and ultimately detects a higher number of differentially expressed proteins. The risk for receiving false associations still exists, particularly when analyzing highly heterogeneous biological samples, raising the need for the analysis of higher sample numbers and/or application of adjustment for multiple testing.

Proteomic Analysis of the Secretome of Porcine Alveolar Macrophages Infected with Porcine Reproductive and Respiratory Syndrome Virus

“…Infection of PAMs with PRRSV results in an alteration to the release of secreted proteins, which is considered to influence the host immune and inflammatory responses in addition to PRRSV replication and pathogenesis . Therefore, a label‐free quantitative proteomic approach was used to globally analyze the secretome of PRRSV‐infected PAMs. A total of 95 differentially expressed proteins was identified, among which, 49 were up‐regulated and 46 were down‐regulated between the PRRSV‐ and mock‐infected cell supernatants.…”

Proteome‐based classification of Nonmuscle Invasive Bladder Cancer

“…Protease inhibitors (Roche, Basel, Switzerland) were added at a final concentration of 3.6%. Protein extracts (200 μg/sample) were processed using filter‐aided sample preparation (FASP) as described previously, with some minor modifications . Briefly, buffer exchange was performed in Amicon Ultra Centrifugal filter devices (0.5 ml, 30 kDa MWCO; Merck) at 16,000 rcf for 15 min at room temperature.…”

iTRAQ-Based Quantitative Proteomics Identifies Potential Regulatory Proteins Involved in Chicken Eggshell Brownness

“…After tryptic digestion, peptides were dried by vacuum centrifugation and then reconstituted in 0.5M TEAB and processed according to the manufacture’s protocol with 8-plexiTRAQ reagent (Applied biosystems, Foster City, CA) [22, 23]. Briefly, one unit of iTRAQ reagent was thawed and reconstituted in 24 μL isopropanol.…”