We present an update of the MaxQuant software for isobaric labeling data and evaluate its performance on benchmark datasets. Impurity correction factors can be applied to labels mixing C- and N-type reporter ions, such as TMT Pro. Application to a single-cell species mixture benchmark shows high accuracy of the impurity-corrected results. TMT data recorded with FAIMS separation can be analyzed directly in MaxQuant without splitting the raw data into separate files per FAIMS voltage. Weighted median normalization, is applied to several datasets, including large-scale human body atlas data. In the benchmark datasets the weighted median normalization either removes or strongly reduces the batch effects between different TMT plexes and results in clustering by biology. In datasets including a reference channel, we find that weighted median normalization performs as well or better when the reference channel is ignored and only the sample channel intensities are used, suggesting that the measurement of a reference channel is unnecessary when using weighted median normalization in MaxQuant. We demonstrate that MaxQuant including the weighted median normalization performs well on multi-notch MS3 data, as well as on phosphorylation data.

Data Summary: Each folder contains MaxQuant output tables used for data analysis with their respectively mqpar files. Please use the MaxQuant version specified in each dataset to open mqpar files. Perseus sessions are provided when Perseus was used for downstream analyses. Please use Perseus version Perseus version 2.1.2 to load the sessions.

MaxDIA is a universal platform for analyzing data-independent acquisition proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves cutting-edge proteome coverage with significantly better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate estimates on both library-to-DIA match and protein levels, also when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA – a framework for the hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s novel bootstrap-DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies, BoxCar acquisition and trapped ion mobility spectrometry, both lead to deep and accurate proteome quantification.

This table is derived from the MaxQuant output table ProteinGroups and contains the complete list of identified proteins/protein groups including the ones that we did not accept after application of criteria described in Materials and Methods. This table also contains additional data such as the complete set of accession numbers forming one group, the distribution of peptides among the 20 PAGE sections analyzed separately, the calculated molecular weight of each entry, the iBAQ intensity and the percentages calculated from it. (XLSX 265Â kb)

Table S2, derived from MaxQuant output table Peptides, contains all identified peptide sequences belonging to the proteins of Table S1 in alphabetic order starting with the first amino acid: It also contains other relevant data such as scores, charge states, and mass accuracy. (XLSX 2472Â kb)

Cumulative malaria parasite exposure in endemic regions often results in the acquisition of partial immunity and asymptomatic infections. There is limited information on how host-parasite interactions mediate maintenance of chronic symptomless infections that sustain malaria transmission. Here, we have determined the gene expression profiles of the parasite population and the corresponding host peripheral blood mononuclear cells (PBMCs) from 21 children (<15 years). We compared children who were defined as uninfected, asymptomatic and those with febrile malaria. Children with asymptomatic infections had a parasite transcriptional profile characterized by a bias toward trophozoite stage (~12 hours-post invasion) parasites and low parasite levels, while earlier ring stage parasites were characteristic of febrile malaria. The host response of asymptomatic children was characterized by downregulated transcription of genes associated with inflammatory responses, compared to children with ..., Proteins were extracted from PBMCs by resuspending the pellet with 5µl of 6M UREA (Thermo scientific). The protein samples were then adjusted with 50mM Triethylamonium bicarbonate (TEAB, Sigma-Aldrich) to 100µl and the protein concentration determined using the Bicinchoninic acid (BCA) protein assay (Thermo scientific). The protein samples were then reduced with 40mM dithiothretol, alkylated with 80mM iodoacetamide in the dark, and quenched with 80mM iodoacetamide at room temperature, followed by digestion with1µg/µl of trypsin (57). Nine pools, each containing 9 samples and 1 control for batch correction, were prepared by combining 1µl aliquots from each sample. The samples were pooled using a custom randomization R script. The pooled samples were then individually labelled using the Tandem Mass Tag (TMT) 10-plex kit (Thermo Scientific) according to the manufacturer’s instructions. One isobaric tag was used solely for the pooled samples and combined with peptides samples labelled with ..., The files can be opened using MaxQuant software, specifically version 2.0.3.0 was used for analysis. Differential protein abundance analysis of MaxQuant output was done using PERSEUS version 2.05.0 software. Protein-protein interaction and Gene ontology analyses was perforened using STRING database version 11.5 (https://string-db.org/)., # Proteome of peripheral mononuclear cells (PBMCs) from asymptomatic malaria and uninfected individuals and the ensuing febrile malaria episodes

Proteins were extracted from peripheral mononuclear cells (PBMCs), pooled using Tandem Mass Tags (TMT) (10-plex) and injected into the LC-MS/MS for proteomics analysis. The output raw files were loaded into MaxQuant software v2.0.3.0 for protein quantification. The output from MaxQuant was then read using PERSEUS software v2.05.0 and differential protein abundance analysis performed. The Proteomics_metadata file contains the metadata that links each sample to the raw data files and the treatment group (condition).

Description of the data and file structure

The RAW data files provided contains the output data from the LC-MS/MS per each pool. The pools serve as the input data for MaxQuant software.

The Proteomics_metadata contains the metadata information that links each sample to the condition/treatment group (i.e. asymptomatic, uninfecte...

Mass spectrometry (MS)-based proteomics is generally performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. While achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be measured in each run. In contrast, targeted proteomics aims to reproducibly and sensitively fragment a restricted number of precursors in each run, based on pre-scheduled mass-to-charge and retention time windows. Here we set out to merge these two concepts by a global targeting approach in which an arbitrary number of previously measured precursors is detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and recalibration in mass, retention time and intensity dimensions to predict peptide identity. MaxQuant.Live is freely available and has a graphical user interface to specify many pre-defined data acquisition strategies. Controlling the acquisition with MaxQuant.Live rather than the vendor software, we observed no decline in acquisition speed. The power of our approach is demonstrated with the acquisition of breakdown curves for thousands of precursors of interest. It is also possible to uncover precursors that are not even visible in MS1 scans, using elution time prediction based on co-eluting isotope standards or the auto-adjusted, predicted retention time alone. Finally, we demonstrate that more than 25,000 precursors can be successfully recognized and targeted in single LC-MS runs. We conclude that global targeting combines the advantages of two classical approaches in MS-based proteomics, while expanding the analytical toolbox with many new possibilities.

Table S3A: Raw MaxQuant data related to all identified and quantified proteins in partially purified CTAP-ERβ nuclear complexes before and after RNAse treatment Table S3B: Statistical analysis of protein levels in CTAP-ERβ nuclear complexes before and after RNase treatment Table S3C: Differentially represented proteins in CTAP-ERβ nuclear complexes before and after RNase treatment (statistically significant).

Label Free Quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyse and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardisation in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range.

The files serve as input and intermediate results for a MaxQuant and Msstats training on skin cancer tissues (https://doi.org/10.1016/j.matbio.2017.11.004) in the Galaxy training network (https://training.galaxyproject.org).

Input files: human FASTA database for Maxquant. Annotation file and comparison matrix file for Msstats.

Intermediate result files: MaxQuant protein groups, evidence and PTXQC.

The files serve as input and intermediate results for a MaxQuant and MsstatsTMT training on lysine methyl transferase 9 knockdown and control cell proteomics (https://doi.org/10.1186/s12935-020-1141-2) in the Galaxy training network (https://training.galaxyproject.org).

Input files: human FASTA protein database for Maxquant. MaxQuant experimental design template, MSstatsTMT annotation file

Intermediate result files: MaxQuant protein groups and evidence

Dataset 1 for the publication "SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering".

This dataset contains all files generated by MaxQuant from database searches of the studies used for assessment of SIMSI-Transfer in the publication.

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between liquid chromatography–mass spectrometry runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without the common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at http://maxquant.org.

SILAC analysis of human primary bladder smooth muscle cells treated with platelet-derived growth factor for 0, 4, and 24 h. Platelet-derived growth factor-BB (PDGF-BB) is a mitogen and motogen that has been implicated in the proliferation, migration and synthetic activities of smooth muscle cells (SMC) that characterize pathologic tissue remodeling in hollow organs. To explore the signals induced by PDGF on a global scale, we performed expression profiling and quantitative proteomics analysis of PDGF-treated human visceral SMC. 1695 genes and 241 proteins were identified as differentially expressed in PDGF-treated primary bladder SMC versus non-treated cells. Analysis of gene expression data revealed MYC, JUN, EGR1, MYB and RUNX1 as the transcription factors most significantly networked with upregulated genes; DDIT3, NFAT5, and SOX5 were most networked with downregulated genes. For protein identification and quantification, raw mass spectrometric data were analyzed with MaxQuant software (version 1.0.13.13). The parameters were set as follows. In the Quant module, SILAC triplets was selected; oxidation (M) and acetyl (Protein N-term) were set as variable modification; carbamidomethyl (C) was set as fixed modification; concatenated IPI human database (version 3.52) (74,190 forward sequences and 74,190 reverse sequences) was used for database searching; all other parameters were default. Tandem mass spectra were searched by Mascot (version 2.2.0.4) (Matrix Science, Boston, MA). In the Identify module, all parameters were default, except that maximal peptide posterior error probability was set as 0.05. False discovery rates for protein and peptide identifications were both set at 0.01.

Alzheimer's disease is a neurodegenerative disorder accounting for more than 50% of cases of dementia. Diagnosis of Alzheimer's disease relies on cognitive tests and analysis of amyloid beta, protein tau, and hyperphosphorylated tau in cerebrospinal fluid. Although these markers provide relatively high sensitivity and specificity for early disease detection, they are not suitable for monitor of disease progression. In the present study, we used label-free shotgun mass spectrometry to analyse the cerebrospinal fluid proteome of Alzheimer's disease patients and non-demented controls to identify potential biomarkers for Alzheimer's disease. We processed the data using five programs (DecyderMS, Maxquant, OpenMS, PEAKS, and Sieve) and compared their results by means of reproducibility and peptide identification, including three different normalization methods. After depletion of high abundant proteins we found that Alzheimer's disease patients had lower fraction of low-abundance proteins in cerebrospinal fluid compared to healthy controls (p<0.05). Consequently, global normalization was found to be less accurate compared to using spiked-in chicken ovalbumin for normalization. In addition, we determined that Sieve and OpenMS resulted in the highest reproducibility and PEAKS was the programs with the highest identification performance. Finally, we successfully verified significantly lower levels (p<0.05) of eight proteins (A2GL, APOM, C1QB, C1QC, C1S, FBLN3, PTPRZ, and SEZ6) in Alzheimer's disease compared to controls using an antibody-based detection method. These proteins are involved in different biological roles spanning from cell adhesion and migration, to regulation of the synapse and the immune system.

Sweet Google O’ Mine - The Importance of Online Search Engines for MS-facilitated, Database-independent Identification of Peptide-encoded Book Prefaces

Raw protein lists after MaxQuant identification. Input files for quantitative and qualitative data processing.

Technological advances have enabled the analysis of cellular protein and RNA levels with unprecedented depth and sensitivity, allowing for an unbiased re-evaluation of gene regulation during fundamental biological processes. Here, we have chronicled the dynamics of protein and mRNA expression levels across a minimally perturbed cell cycle in human myeloid leukemia cells using centrifugal elutriation combined with mass spectrometry-based proteomics and RNA-Seq, avoiding artificial synchronization procedures. We identify myeloid-specific gene expression and variations in protein abundance, isoform expression and phosphorylation at different cell cycle stages. We dissect the relationship between protein and mRNA levels for both bulk gene expression and for over ∼6000 genes individually across the cell cycle, revealing complex, gene-specific patterns. This data set, one of the deepest surveys to date of gene expression in human cells, is presented in an online, searchable database, the Encyclopedia of Proteome Dynamics (http://www.peptracker.com/epd/).

Mass spectrometry-based proteomics of the Aurantiochytrium limacinum ATCC MYA-1381 zoospore-to-vegetative cell transition (MaxQuant processed data)

Dataset Overview

This dataset contains proteomic data from an analysis of the Aurantiochytrium limacinum ATCC MYA-1381 zoospore-to-vegetative cell transition. Mass spectrometry data from three biological replicates were processed using MaxQuant and analyzed separately against two different protein prediction sets: JGI genome annotation (MycoCosm), and Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) annotation.

Each dataset includes protein abundance measurements across five time points (T0, T2, T4, T6, T8), capturing proteomic changes associated with cellular remodeling, metabolism, and ectoplasmic network formation.

Contents of the Dataset

The dataset consists of two main spreadsheet files corresponding to the two protein prediction sets used in MaxQuant analysis:

• JGI_maxquant.xlsx
• **MME...

Evaluation of sensitivity and accuracy of widely used scoring functions Sequest, MaxQuant, Peaks, and Byonic. Spectra from E. coli were matched to human sequences, and human spectra from K052 cells were matched to A. loki sequences to determine specificity of matching and FDR estimation. A subsampling analysis of SCX-RP fractionation was performed.

In mesenchymal-like cell migration, cells need to polarise into a protrusive front and a retracting cell body. To understand this process better, we set out to quantify the distribution of cellular proteins between protrusions and cell-body by proteomics, using MDA-MB-231 cells, a highly invasive breast cancer cell-line. We utilised a transwell filter based fractionation method in conjugation with SILAC proteomics. In this method, cells are seeded on top of 3μm transwell filters to enable protrusions to form through the pores of the filters but to prevent the cell-bodies passing through due to the small size of the pores, thus resulting in separation of protrusions and cell-bodies on opposite sides of the filter, which can then be lysed and prepared separately. Prepration of protrusion and cell-body fractions from heavy and light SILAC labelled cells then allows for reciprocal mixing and quantification of proteins between protrusions and cell-body. In this study, we determined the relative distribution of 3240 proteins between protrusions and cell-body from two SILAC mixes. Associated ArrayExpress data: E-MTAB-2546.