On the Statistical Significance of Compressed Ratios in Isobaric Labeling: A Cross-Platform Comparison

Enhanced Multiplexing Technology for Proteomics

The identification of thousands of proteins and their relative levels of expression has furthered understanding of biological processes and disease and stimulated new systems biology hypotheses. Quantitative proteomics workflows that rely on analytical assays such as mass spectrometry have facilitated high-throughput measurements of proteins partially due to multiplexing. Multiplexing allows proteome differences across multiple samples to be measured simultaneously, resulting in more accurate quantitation, increased statistical robustness, reduced analysis times, and lower experimental costs. The number of samples that can be multiplexed has evolved from as few as two to more than 50, with studies involving more than 10 samples being denoted as enhanced multiplexing or hyperplexing. In this review, we give an update on emerging multiplexing proteomics techniques and highlight advantages and limitations for enhanced multiplexing strategies.

PUMILIO proteins promote colorectal cancer growth via suppressing p21

PUMILIO (PUM) proteins belong to the highly conserved PUF family post-transcriptional regulators involved in diverse biological processes. However, their function in carcinogenesis remains under-explored. Here, we report that Pum1 and Pum2 display increased expression in human colorectal cancer (CRC). Intestine-specific knockout of Pum1 and Pum2 in mice significantly inhibits the progression of colitis-associated cancer in the AOM/DSS model. Knockout or knockdown of Pum1 and/or Pum2 in human CRC cells result in a significant decrease in the tumorigenicity and delayed G1/S transition. We identify p21/Cdkn1a as a direct target of PUM1. Abrogation of the PUM1 binding site in the p21 mRNA also results in decreased cancer cell growth and delayed G1/S transition. Furthermore, intravenous injection of nanoparticle-encapsulated anti-Pum1 and Pum2 siRNAs reduces colorectal tumor growth in murine orthotopic colon cancer models. These findings reveal the requirement of PUM proteins for CRC progression and their potential as therapeutic targets.

RNA binding proteins can contribute to colorectal cancer (CRC) initiation and development. Here the authors show that PUMILIO proteins, PUM1 and PUM2 contribute to CRC growth by inhibiting p21 expression.

Integration of Segmented Ion Fractionation and Differential Ion Mobility on a Q-Exactive Hybrid Quadrupole Orbitrap Mass Spectrometer

High-field asymmetric waveform ion mobility spectrometry (FAIMS) has gained popularity in the proteomics field for its capability to improve mass spectrometry sensitivity and to decrease peptide co-fragmentation. The recent implementation of FAIMS on Tribrid Orbitrap instruments enhanced proteome coverage and increased the precision of quantitative measurements. However, the FAIMS interface has not been available on older generation Orbitrap mass spectrometers such as the Q-Exactive. Here, we report the integration of the FAIMS Pro device with embedded electrical and gas connections to a Q-Exactive HF mass spectrometer. Proteomic experiments performed on HeLa tryptic digests with the modified mass spectrometer improved signal to noise and reduced interfering ions, resulting in an increase of 42% in peptide identification. FAIMS was also combined with segmented ion fractionation where 100 m/z windows were obtained in turn to further increase the depth of proteome analysis by reducing the proportion of chimeric MS/MS spectra from 50 to 27%. We also demonstrate the application of FAIMS to improve quantitative measurements when using isobaric peptide labeling. FAIMS experiments performed on a two-proteome model revealed that FAIMS Pro provided a 65% improvement in quantification accuracy compared to conventional LC-MS/MS experiments.

Stoichiometric Thiol Redox Proteomics for Quantifying Cellular Responses to Perturbations

Post-translational modifications regulate the structure and function of proteins that can result in changes to the activity of different pathways. These include modifications altering the redox state of thiol groups on protein cysteine residues, which are sensitive to oxidative environments. While mass spectrometry has advanced the identification of protein thiol modifications and expanded our knowledge of redox-sensitive pathways, the quantitative aspect of this technique is critical for the field of redox proteomics. In this review, we describe how mass spectrometry-based redox proteomics has enabled researchers to accurately quantify the stoichiometry of reversible oxidative modifications on specific cysteine residues of proteins. We will describe advancements in the methodology that allow for the absolute quantitation of thiol modifications, as well as recent reports that have implemented this approach. We will also highlight the significance and application of such measurements and why they are informative for the field of redox biology.

A critical comparison of three MS-based approaches for quantitative proteomics analysis

MS-based proteomics is expanding its role as a routine tool for biological discovery. Nevertheless, the task of accurately and precisely quantifying thousands of analytes in a single experiment remains challenging. In this study, the diagnostic accuracy of three popular data-dependent methods for protein relative quantification (label-free [LF], dimethyl labelling [DML] and tandem mass tags [TMT]) has been assessed using a mixed species proteome (three species) and five experimental replicates per condition. Data were produced using a quadrupole-Orbitrap mass spectrometer and analysed using a single platform (the MaxQuant/Perseus software suite). The whole comparative analysis was repeated three times over a period of 6 months, in order to assess the consistency of the reported findings. As expected, label-based methods reproducibly provided a lower false positives rate, whereas TMT and LF performed similarly, and significantly better than DML, in terms of proteome coverage using the same instrument time. Although parameters like proteome coverage and precision were consistent in between replicates, other parameters like sensitivity, intended as the capacity of correctly classifying true positives (regulated proteins), were found to be less reproducible, especially at challenging fold-changes (1.5). Collectively, data suggest that an increased interest in data reproducibility would be desirable in the quantitative proteomics field.

MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

Practical Integration of Multi-Run iTRAQ Data

In this chapter, we describe some of the approaches we employ in the analysis of iTRAQ data in our group, with an emphasis on practical issues that can occur in larger multi-run projects. Our pipeline starts with a well-established iTRAQ workflow, makes use of protein level quantitation using ProteinPilot, and continues either via a global analysis in the presence of a common reference, or by identifying pairwise comparisons of interest and applying a method taking the protein ratios and protein ratio confidence measures into consideration. Additionally we describe what issues can occur in the more subtle scenarios involving composite databases in multi-run situations, and an approach applicable in that setting.

Benchmarking common quantification strategies for large-scale phosphoproteomics

Comprehensive mass spectrometry (MS)-based proteomics is now feasible, but reproducible quantification remains challenging, especially for post-translational modifications such as phosphorylation. Here, we compare the most popular quantification techniques for global phosphoproteomics: label-free quantification (LFQ), stable isotope labeling by amino acids in cell culture (SILAC) and MS²- and MS³-measured tandem mass tags (TMT). In a mixed species comparison with fixed phosphopeptide ratios, we find LFQ and SILAC to be the most accurate techniques. MS²-based TMT yields the highest precision but lowest accuracy due to ratio compression, which MS³-based TMT can partly rescue. However, MS²-based TMT outperforms MS³-based TMT when analyzing phosphoproteome changes in the DNA damage response, since its higher precision and larger identification numbers allow detection of a greater number of significantly regulated phosphopeptides. Finally, we utilize the TMT multiplexing capabilities to develop an algorithm for determining phosphorylation site stoichiometry, showing that such applications benefit from the high accuracy of MS³-based TMT.

Quantitative phosphoproteomics has become a standard method in molecular and cell biology. Here, the authors compare performance and parameters of phosphoproteome quantification by LFQ, SILAC, and MS²-/MS³-based TMT and introduce a TMT-adapted algorithm for calculating phosphorylation site stoichiometry.