Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins

SIK2 Controls the Homeostatic Character of the POMC Secretome Acutely in Response to Pharmacological ER Stress Induction

The neuronal etiology of obesity is centered around a diet-induced inflammatory state in the arcuate nucleus of the hypothalamus, which impairs the functionality of pro-opiomelanocortine neurons (POMCs) responsible for whole-body energy homeostasis and feeding behavior. Intriguingly, systemic salt inducible kinase 2 (SIK2) knockout mice demonstrated reduced food intake and energy expenditure along with modestly dysregulated metabolic parameters, suggesting a causal link between the absence of SIK2 activity in POMCs and the observed phenotype. To test this hypothesis, we conducted a comparative secretomics study from POMC neurons following pharmacologically induced endoplasmic reticulum (ER) stress induction, a hallmark of metabolic inflammation and POMC dysregulation in diet-induced obese (DIO) mice. Our data provide significant in vitro evidence for the POMC-specific SIK2 activity in controlling energy metabolism and feeding in DIO mice by regulating the nature of the related POMC secretome. Our data also suggest that under physiological stress conditions, SIK2 may act as a gatekeeper for the secreted inflammatory factors and signaling molecules critical for cellular survival and energy homeostasis. On the other hand, in the absence of SIK2, the gate opens, leading to a surge of inflammatory cytokines and apoptotic cues concomitant with the dysregulation of POMC neurons.

A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics

Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering

Isobaric stable isotope labeling techniques such as tandem mass tags (TMTs) have become popular in proteomics because they enable the relative quantification of proteins with high precision from up to 18 samples in a single experiment. While missing values in peptide quantification are rare in a single TMT experiment, they rapidly increase when combining multiple TMT experiments. As the field moves toward analyzing ever higher numbers of samples, tools that reduce missing values also become more important for analyzing TMT datasets. To this end, we developed SIMSI-Transfer (Similarity-based Isobaric Mass Spectra 2 [MS2] Identification Transfer), a software tool that extends our previously developed software MaRaCluster (© Matthew The) by clustering similar tandem MS2 from multiple TMT experiments. SIMSI-Transfer is based on the assumption that similarity-clustered MS2 spectra represent the same peptide. Therefore, peptide identifications made by database searching in one TMT batch can be transferred to another TMT batch in which the same peptide was fragmented but not identified. To assess the validity of this approach, we tested SIMSI-Transfer on masked search engine identification results and recovered >80% of the masked identifications while controlling errors in the transfer procedure to below 1% false discovery rate. Applying SIMSI-Transfer to six published full proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis Consortium led to an increase of 26 to 45% of identified MS2 spectra with TMT quantifications. This significantly decreased the number of missing values across batches and, in turn, increased the number of peptides and proteins identified in all TMT batches by 43 to 56% and 13 to 16%, respectively.

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Spectrum clustering enables peptide identification transfer between LC–MS/MS runs.

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The SIMSI pipeline supports processing full proteome and phosphoproteome data.

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SIMSI increases the number of quantifiable PSMs by 26 to 45%.

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SIMSI reduces missing values in multibatch TMT labeling experiments by up to 21%.

Protein phosphatase 2A catalytic subunit β suppresses PMA/ionomycin-induced T-cell activation by negatively regulating PI3K/Akt signaling

The precise regulation of the T-cell activation process is critical for overall immune homeostasis. Although protein phosphatase 2A (PP2A) is required for T-cell development and function, the role of PPP2CB, which is the catalytic subunit β isoform of PP2A, remains unknown. In the present study, using a T cell-specific knockout mouse of PPP2CB (PPP2CB^fl/fl Lck-Cre⁺ ), we demonstrated that PPP2CB was dispensable for T-cell development in the thymus and peripheral lymphoid organs. Furthermore, PPP2CB deletion did not affect T-cell receptor (TCR)-induced T-cell activation or cytokine-induced T-cell responses; however, it specifically enhanced phorbol myristate acetate (PMA) plus ionomycin-induced T-cell activation with increased cellular proliferation, elevated CD69 and CD25 expression, and enhanced cytokine production (inteferon-γ, interleukin-2 and tumor necrosis factor). Mechanistic analyses suggested that the PPP2CB deletion enhanced activation of the phosphoinositide 3-kinase/Akt signaling pathway and Ca²⁺ flux following stimulation with PMA plus ionomycin. Moreover, the specific PI3K inhibitor rescued the augmented cell activation in PPP2CB-deficient T cells. Using mass spectrometry-based phospho-peptide analysis, we identified potential substrates of PPP2CB during PMA plus ionomycin-induced T-cell activation. Collectively, our study provides evidence of the specific role of PPP2CB in controlling PMA plus ionomycin-induced T-cell activation.

Quantitative Proteomics in Yeast : From bSLIM and Proteome Discoverer Outputs to Graphical Assessment of the Significance of Protein Quantification Scores

Simple light isotope metabolic labeling (bSLIM) is an innovative method to accurately quantify differences in protein abundance at the proteome level in standard bottom-up experiments. The quantification process requires computation of the ratio of intensity of several isotopologs in the isotopic cluster of every identified peptide. Thus, appropriate bioinformatic workflows are required to extract the signals from the instrument files and calculate the required ratio to infer peptide/protein abundance. In a previous study (Sénécaut et al., J Proteome Res 20:1476-1487, 2021), we developed original open-source workflows based on OpenMS nodes implemented in a KNIME working environment. Here, we extend the use of the bSLIM labeling strategy in quantitative proteomics by presenting an alternative procedure to extract isotopolog intensities and process them by taking advantage of new functionalities integrated into the Minora node of Proteome Discoverer 2.4 software. We also present a graphical strategy to evaluate the statistical robustness of protein quantification scores and calculate the associated false discovery rates (FDR). We validated these approaches in a case study in which we compared the differences between the proteomes of two closely related yeast strains.

Discovery of Missing Proteins from an Aneuploidy Cell Line Using a Proteogenomic Approach

With the steadfast development of proteomic technology, the number of missing proteins (MPs) has been continuously shrinking, with approximately 1470 MPs that have not been explored yet. Due to this phenomenon, the discovery of MPs has been increasingly more difficult and elusive. In order to face this challenge, we have hypothesized that a stable aneuploid cell line with increased chromosomes serves as a useful material for assisting MP exploration. Ker-CT cell line with trisomy at chromosome 5 and 20 was selected for this purpose. With a combination strategy of RNA-Seq and LC-MS/MS, a total of 22 178 transcripts and 8846 proteins were identified in Ker-CT. Although the transcripts corresponding to 15 and 15 MP genes located at chromosome 5 and 20 were detected, none of the MPs were found in Ker-CT. Surprisingly, 3 MPs containing at least two unique non-nest peptides of length ≥9 amino acids were identified in Ker-CT, whose genes are located on chromosome 3 and 10, respectively. Furthermore, the 3 MPs were verified using the method of parallel reaction monitoring (PRM). These results suggest that the abnormal status of chromosomes may not only impact the expression of the corresponding genes in trisomy chromosomes, but also influence that of other chromosomes, which benefits MP discovery. The data obtained in this study are available via ProteomeXchange (PXD028647) and PeptideAtlas (PASS01700), respectively.

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow, thereby preventing valuable information from being discarded in the identification stage. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This reduces search time due to the data reduction. We can now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Quandenser+Triqler outperforms the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins for all tested datasets. Software is available for all major operating systems at https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.

Defeating the trypanosomatid trio: proteomics of the protozoan parasites causing neglected tropical diseases

Mass spectrometry-based proteomics enables accurate measurement of the modulations of proteins on a large scale upon perturbation and facilitates the understanding of the functional roles of proteins in biological systems. It is a particularly relevant methodology for studying Leishmania spp., Trypanosoma cruzi and Trypanosoma brucei, as the gene expression in these parasites is primarily regulated by posttranscriptional mechanisms. Large-scale proteomics studies have revealed a plethora of information regarding modulated proteins and their molecular interactions during various life processes of the protozoans, including stress adaptation, life cycle changes and interactions with the host. Important molecular processes within the parasite that regulate the activity and subcellular localisation of its proteins, including several co- and post-translational modifications, are also accurately captured by modern proteomics mass spectrometry techniques. Finally, in combination with synthetic chemistry, proteomic techniques facilitate unbiased profiling of targets and off-targets of pharmacologically active compounds in the parasites. This provides important data sets for their mechanism of action studies, thereby aiding drug development programmes.

LFQ-Analyst: An Easy-To-Use Interactive Web Platform To Analyze and Visualize Label-Free Proteomics Data Preprocessed with MaxQuant

Relative label-free quantification (LFQ) of shotgun proteomics data using precursor (MS1) signal intensities is one of the most commonly used applications to comprehensively and globally quantify proteins across biological samples and conditions. Due to the popularity of this technique, several software packages, such as the popular software suite MaxQuant, have been developed to extract, analyze, and compare spectral features and to report quantitative information of peptides, proteins, and even post-translationally modified sites. However, there is still a lack of accessible tools for the interpretation and downstream statistical analysis of these complex data sets, in particular for researchers and biologists with no or only limited experience in proteomics, bioinformatics, and statistics. We have therefore created LFQ-Analyst, which is an easy-to-use, interactive web application developed to perform differential expression analysis with "one click" and to visualize label-free quantitative proteomic data sets preprocessed with MaxQuant. LFQ-Analyst provides a wealth of user-analytic features and offers numerous publication-quality result graphics to facilitate statistical and exploratory analysis of label-free quantitative data sets. LFQ-Analyst, including an in-depth user manual, is freely available at https://bioinformatics.erc.monash.edu/apps/LFQ-Analyst .

2018 YPIC Challenge: A Case Study in Characterizing an Unknown Protein Sample

For the 2018 YPIC Challenge, contestants were invited to try to decipher two unknown English questions encoded by a synthetic protein expressed in Escherichia coli. In addition to deciphering the sentence, contestants were asked to determine the three-dimensional structure and detect any post-translation modifications left by the host organism. We present our experimental and computational strategy to characterize this sample by identifying the unknown protein sequence and detecting the presence of post-translational modifications. The sample was acquired with dynamic exclusion disabled to increase the signal-to-noise ratio of the measured molecules, after which spectral clustering was used to generate high-quality consensus spectra. De novo spectrum identification was used to determine the synthetic protein sequence, and any post-translational modifications introduced by E. coli on the synthetic protein were analyzed via spectral networking. This workflow resulted in a de novo sequence coverage of 70%, on par with sequence database searching performance. Additionally, the spectral networking analysis indicated that no systematic modifications were introduced on the synthetic protein by E. coli. The strategy presented here can be directly used to analyze samples for which no protein sequence information is available or when the identity of the sample is unknown. All software and code to perform the bioinformatics analysis is available as open source, and self-contained Jupyter notebooks are provided to fully recreate the analysis.