An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing.

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling.

Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale.

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments.

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).

Allogeneic Hematopoietic Stem Cell Transplantation for Paroxysmal Nocturnal Hemoglobinuria: Multicenter Analysis by the Polish Adult Leukemia Group.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the sole potential cure for paroxysmal nocturnal hemoglobinuria (PNH); however, the data on its utility in PNH are limited. This retrospective analysis of patients with PNH who underwent allo-HSCT in 11 Polish centers between 2002 and 2016 comprised 78 patients with PHN, including 27 with classic PNH (cPNH) and 51 with bone marrow failure-associated PNH (BMF/PNH). The cohort was 59% male, with a median age of 29 years (range, 12 to 65 years). There was a history of thrombosis in 12% and a history of hemolysis in 81%, and 92% required erythrocyte transfusions before undergoing allo-HSCT. No patient received eculizumab, and 26% received immunosuppressive treatment. The median time from diagnosis to allo-HSCT was 12 months (range, 1 to 127 months). Almost all patients (94%) received reduced-toxicity conditioning, 66% with treosulfan. The stem cell source was peripheral blood in 72% and an identical sibling donor in 24%. Engraftment occurred in 96% of the patients. With a median follow-up of 5.1 years in patients with cPNH and 3.2 years in patients with BMF/PNH, 3-year overall survival (OS) was 88.9% in the former and 85.1% in the latter (P = not significant [NS]). The 3-year OS for patients with/without thrombosis was 50%/92% (P = NS) in the cPNH group and 83.3%/85.3% (P = NS) in the BMF/PNH group. The 3-year OS for in the BMF/PNH patients with/without hemolysis was 93.9%/62.9% (hazard ratio, .13; P = .016). No other factors impacted OS. After allo-HSCT, the frequency of the PNH clone was reduced to 0%, <1%, and <2.4% in 48%, 48%, and 4% of cPNH patients and in 84%, 11%, and 5% of BMF/PNH patients, respectively. The frequency of acute graft-versus-host disease (GVHD) grade II-IV was 23%, and the cumulative 1-year incidence of extensive chronic GVHD was 10.8% in the BMF/PNH group and 3.7% in the cPNH group. Allo-HSCT is a valid option for PNH patients, effectively eliminating the PNH clone with satisfactory overall survival and acceptable toxicity. Reduced-toxicity conditioning with treosulfan is effective and safe in patients with cPNH and BMF/PNH.