Normalization and missing value imputation for label-free LC-MS analysis

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data

MSqRob takes the missing hurdle : uniting intensity- and count-based proteomics

Missing values are a major issue in quantitative data-dependent mass spectrometry-based proteomics. We therefore present an innovative solution to this key issue by introducing a hurdle model, which is a mixture between a binomial peptide count and a peptide intensity-based model component. It enables dramatically enhanced quantification of proteins with many missing values without having to resort to harmful assumptions for missingness. We demonstrate the superior performance of our method by comparing it with state-of-the-art methods in the field

Introduction to opportunities and pitfalls in functional mass spectrometry based proteomics

With the advent of mass spectrometry based proteomics, the identification of thousands of proteins has become commonplace in biology nowadays. Increasingly, efforts have also been invested toward the detection and localization of posttranslational modifications. It is furthermore common practice to quantify the identified entities, a task supported by a panel of different methods. Finally, the results can also be enriched with functional knowledge gained on the proteins, detecting for instance differentially expressed gene ontology terms or biological pathways. In this study, we review the resources, methods and tools available for the researcher to achieve such a quantitative functional analysis. These include statistics for the post-processing of identification and quantification results, online resources and public repositories. With a focus on free but user-friendly software, preferably also open-source, we provide a list of tools designed to help the researcher manage the vast amount of data generated. We also indicate where such applications currently remain lacking. Moreover, we stress the eventual pitfalls of every step of such studies. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.acceptedVersio

Proteins Inform Survival-Based Differences in Patients with Glioblastoma

Background: Improving the care of patients with glioblastoma (GB) requires accurate and reliable predictors of patient prognosis. Unfortunately, while protein markers are an effective readout of cellular function, proteomics has been underutilized in GB prognostic marker discovery. Methods: For this study, GB patients were prospectively recruited and proteomics discovery using liquid chromatography-mass spectrometry analysis (LC-MS/MS) was performed for 27 patients including 13 short-term survivors (STS) (≤10 months) and 14 long-term survivors (LTS) (≥18 months). Results: Proteomics discovery identified 11 941 peptides in 2495 unique proteins, with 469 proteins exhibiting significant dysregulation when comparing STS to LTS. We verified the differential abundance of 67 out of these 469 proteins in a small previously published independent dataset. Proteins involved in axon guidance were upregulated in STS compared to LTS, while those involved in p53 signaling were upregulated in LTS. We also assessed the correlation between LS MS/MS data with RNAseq data from the same discovery patients and found a low correlation between protein abundance and mRNA expression. Finally, using LC-MS/MS on a set of 18 samples from 6 patients, we quantified the intratumoral heterogeneity of more than 2256 proteins in the multisample dataset. Conclusions: These proteomic datasets and noted protein variations present a beneficial resource for better predicting patient outcome and investigating potential therapeutic targets

Pyrenoid loss impairs carbon-concentrating mechanism induction and alters primary metabolism in Chlamydomonas reinhardtii

Carbon-concentrating mechanisms (CCMs) enable efficient photosynthesis and growth in CO2-limiting environments, and in eukaryotic microalgae localisation of Rubisco to a microcompartment called the pyrenoid is key. In the model green alga Chlamydomonas reinhardtii, Rubisco preferentially relocalises to the pyrenoid during CCM induction and pyrenoid-less mutants lack a functioning CCM and grow very poorly at low CO2. The aim of this study was to investigate the CO2 response of pyrenoid-positive (pyr+) and pyrenoid-negative (pyr–) mutant strains to determine the effect of pyrenoid absence on CCM induction and gene expression. Shotgun proteomic analysis of low-CO2-adapted strains showed reduced accumulation of some CCM-related proteins, suggesting that pyr– has limited capacity to respond to low-CO2 conditions. Comparisons between gene transcription and protein expression revealed potential regulatory interactions, since Rubisco protein linker (EPYC1) protein did not accumulate in pyr– despite increased transcription, while elements of the LCIB/LCIC complex were also differentially expressed. Furthermore, pyr− showed altered abundance of a number of proteins involved in primary metabolism, perhaps due to the failure to adapt to low CO2. This work highlights two-way regulation between CCM induction and pyrenoid formation, and provides novel candidates for future studies of pyrenoid assembly and CCM function

Pyrenoid loss impairs carbon-concentrating mechanism induction and alters primary metabolism in Chlamydomonas reinhardtii.

Carbon-concentrating mechanisms (CCMs) enable efficient photosynthesis and growth in CO2-limiting environments, and in eukaryotic microalgae localisation of Rubisco to a microcompartment called the pyrenoid is key. In the model green alga Chlamydomonas reinhardtii, Rubisco preferentially relocalises to the pyrenoid during CCM induction and pyrenoid-less mutants lack a functioning CCM and grow very poorly at low CO2. The aim of this study was to investigate the CO2 response of pyrenoid-positive (pyr+) and pyrenoid-negative (pyr-) mutant strains to determine the effect of pyrenoid absence on CCM induction and gene expression. Shotgun proteomic analysis of low-CO2-adapted strains showed reduced accumulation of some CCM-related proteins, suggesting that pyr- has limited capacity to respond to low-CO2 conditions. Comparisons between gene transcription and protein expression revealed potential regulatory interactions, since Rubisco protein linker (EPYC1) protein did not accumulate in pyr- despite increased transcription, while elements of the LCIB/LCIC complex were also differentially expressed. Furthermore, pyr- showed altered abundance of a number of proteins involved in primary metabolism, perhaps due to the failure to adapt to low CO2. This work highlights two-way regulation between CCM induction and pyrenoid formation, and provides novel candidates for future studies of pyrenoid assembly and CCM function