Proceedings of the EuBIC Winter School 2019

Recent advances of data‐independent acquisition mass spectrometry‐based proteomics

The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

Progress in Mass Spectrometry Acquisition Approach for Quantitative Proteomics

The introduction of mass spectrometry-based proteomics has revolutionized the high-density lipoprotein (HDL) field, with the description, characterization, and implication of HDL-associated proteins in an array of pathologies. However, acquiring robust, reproducible data is still a challenge in the quantitative assessment of HDL proteome. Data-independent acquisition (DIA) is a mass spectrometry methodology that allows the acquisition of reproducible data, but data analysis remains a challenge in the field. To date, there is no consensus on how to process DIA-derived data for HDL proteomics. Here, we developed a pipeline aiming to standardize HDL proteome quantification. We optimized instrument parameters and compared the performance of four freely available, user-friendly software tools (DIA-NN, EncyclopeDIA, MaxDIA, and Skyline) in processing DIA data. Importantly, pooled samples were used as quality controls throughout our experimental setup. A careful evaluation of precision, linearity, and detection limits, first using E. coli background for HDL proteomics and second using HDL proteome and synthetic peptides, was undertaken. Finally, as a proof of concept, we employed our optimized and automated pipeline to quantify the proteome of HDL and apolipoprotein B–containing lipoproteins. Our results show that determination of precision is key to confidently and consistently quantifying HDL proteins. Taking this precaution, any of the available software tested here would be appropriate for quantification of HDL proteome, although their performance varied considerably.

In shotgun proteomics, more abundant peptides are selected for MS/MS fragmentation leading to sequence assignment and their quantitative abundance is computed from peak area of the extracted ion chromatogram. This analysis framework is called data dependent acquisition (DDA). However, the bias towards abundant peptides limits reproducible extraction of peptide signals for a large proportion of the proteome. Recent advances in next generation mass spectrometers enabled implementation of an alternative approach called data independent acquisition (DIA), which improves data quality in terms of dynamic range, measurement precision, and more importantly, reproducible detection. In this chapter, we review the process of generating quantitative proteomics data with DIA, and present a computational tool mapDIA designed for data processing and statistical analysis of the DIA proteomics data. Using an example of renal cancer data set, we demonstrate that fragment intensity data from DIA provide a reliable repeated measure of peptide abundance after careful filtering, and direct modeling of the hierarchical data (protein → peptide → fragment) improves the detection of differentially expressed proteins compared to the analysis using protein intensity data derived by summation of fragment intensities.

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins invivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

Top-down protein identification using a time-of-flight mass spectrometer and data independent acquisition

In this article, we provide a proteomic reference dataset that has been initially generated for a benchmarking of software tools for Data-Independent Acquisition (DIA) analysis. This large dataset includes 96 DIA .raw files acquired from a complex proteomic standard composed of an E.coli protein background spiked-in with 8 different concentrations of 48 human proteins (UPS1 Sigma). These 8 samples were analyzed in triplicates on an Orbitrap mass spectrometer with 4 different DIA window schemes. We also provide the spectral libraries and FASTA file used for their analysis and the software outputs of the six tools used in this study: DIA-NN, Spectronaut, ScaffoldDIA, DIA-Umpire, Skyline and OpenSWATH. This dataset also contains post-processed quantification tables where the peptides and proteins have been validated, their intensities normalized and the missing values imputed with a noise value. All the files are available on ProteomeXchange. Altogether, these files represent the most comprehensive DIA reference dataset acquired on an Orbitrap instrument ever published. It will be a very useful resource to the proteomic scientists in order to assess the performance of DIA software tools or to test their processing pipelines, to the software developers to improve their tools or develop new ones and to the students for their training on proteomics data analysis.

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—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 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.

Tear fluid proteomics: a comparative study of DIA and DDA mass spectrometry

Enhancing protein discoverability by data independent acquisition assisted by ion mobility mass spectrometry

Background CanPathPro is designed to build and validate a combined experimental and systems biology platform, which will be used in testing cancer signaling hypotheses. It combines highly defined mouse and organotypic experimental systems, high-dimensional data including next generation sequencing and quantitative proteomics, and computational models for data integration, visualization and modelling. Mouse cancer models are characterized by quantitative transcriptome, quantitative mass spectrometry including phospho-proteome, histopathology and histochemistry. Phospho-proteomic data has been obtained using data independent acquisition (DIA) and used to refine signaling models in the mouse cell lines. Currently, the model integrates modules of the signal transduction pathways Egfr/Erbb2, Fgfr, insulin, Akt, Mtor, Myc, Ras, Hippo, Met, Tgfbr, Il6, integrin, Wnt, apoptosis and cell cycle regulation integrating in total about 380 different genes as well as related proteins and phospho-proteins Methods Two mouse mammary gland model cell lines, (Cdh1-fl/AKT1[E17K] and Cdh1-fl + PTEN-fl) were each treated with DMSO, a Pik3ca inhibitor (Wortmannin), or an Akt inhibitor (MK-2206). Cells were lysed and proteins were denatured, followed by reduction, alkylation and digestion with trypsin. The resulting peptides were desalted and enriched for phosphopeptides with TiO2 beads and cleaned up for mass spectrometry. A phosphopeptide library was generated from pooled phosphoenriched samples using LC-MS/MS shotgun measurements and included 22,893 phosphosites from 3,549 protein groups. DIA data was acquired on a Q Exactive HF mass spectrometer with a gradient length of 60 - 120 minutes on a C18 Easy LC 1200 nano-liquid chromatography system. The DIA data was extracted and processed with Spectronaut 11 (Biognosys) for analysis. Results In the AKT1[E17K] samples, 13,396 peptides (21,862 phospho-peptides) were quantified in the DIA runs and 12,297 peptides (19,928 phospho-peptides) were quantified in PTEN-fl samples. Under treatment with MK2206 and Wortmanin, 859 phosphopeptides from 548 protein groups were significantly changed across all comparisons in the AKT1[E17K] samples and with the same treatments 2,276 phosphopeptides from 976 proteins were significantly changed in the PTEN-fl cells. Based on this data 11 functionally relevant new phosphosites have been added to the model including ones on: EIF4B, FOX03, MAP2K4, PAK1, RAF1, and ULK1. Conclusions Phospho-proteomic profiling of cell lines using DIA mass spectrometry enables deep characterization of the phospho-signaling cascades modulated through small molecule inhibitors. Citation Format: Magdalena Bober, Monika Banko-Bielecka, Daniel Heinzmann, Oliver Rinner, Nicholas Dupuis, Christoph Wierling, Huaibiao Li, Thomas Kessler, Artur Muradyan, Louisa Krützfeldt, Moritz Schütte, Felix Dreher, Aspasia Ploubidou, Bodo Lange. Unbiased phospho-proteomic profiling of mouse breast cancer models with DIA mass spectrometry refines CanPath prototype, a platform for predictive cancer pathway modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3916.

Background CanPathPro is designed to build and validate a combined experimental and systems biology platform, which will be used in testing cancer signaling hypotheses. It combines highly defined mouse and organotypic experimental systems, high-dimensional data including next generation sequencing and quantitative proteomics, and computational models for data integration, visualization and modelling. Mouse cancer models are characterized by quantitative transcriptome, quantitative mass spectrometry including phospho-proteome, histopathology and histochemistry. Phospho-proteomic data has been obtained using data independent acquisition (DIA) and used to refine signaling models in the mouse cell lines. Currently, the model integrates modules of the signal transduction pathways Egfr/Erbb2, Fgfr, insulin, Akt, Mtor, Myc, Ras, Hippo, Met, Tgfbr, Il6, integrin, Wnt, apoptosis and cell cycle regulation integrating in total about 380 different genes as well as related proteins and phospho-proteins Methods Two mouse mammary gland model cell lines, (Cdh1-fl/AKT1[E17K] and Cdh1-fl + PTEN-fl) were each treated with DMSO, a Pik3ca inhibitor (Wortmannin), or an Akt inhibitor (MK-2206). Cells were lysed and proteins were denatured, followed by reduction, alkylation and digestion with trypsin. The resulting peptides were desalted and enriched for phosphopeptides with TiO2 beads and cleaned up for mass spectrometry. A phosphopeptide library was generated from pooled phosphoenriched samples using LC-MS/MS shotgun measurements and included 22,893 phosphosites from 3,549 protein groups. DIA data was acquired on a Q Exactive HF mass spectrometer with a gradient length of 60 - 120 minutes on a C18 Easy LC 1200 nano-liquid chromatography system. The DIA data was extracted and processed with Spectronaut 11 (Biognosys) for analysis. Results In the AKT1[E17K] samples, 13,396 peptides (21,862 phospho-peptides) were quantified in the DIA runs and 12,297 peptides (19,928 phospho-peptides) were quantified in PTEN-fl samples. Under treatment with MK2206 and Wortmanin, 859 phosphopeptides from 548 protein groups were significantly changed across all comparisons in the AKT1[E17K] samples and with the same treatments 2,276 phosphopeptides from 976 proteins were significantly changed in the PTEN-fl cells. Based on this data 11 functionally relevant new phosphosites have been added to the model including ones on: EIF4B, FOX03, MAP2K4, PAK1, RAF1, and ULK1. Conclusions Phospho-proteomic profiling of cell lines using DIA mass spectrometry enables deep characterization of the phospho-signaling cascades modulated through small molecule inhibitors. Citation Format: Magdalena Bober, Monika Banko-Bielecka, Daniel Heinzmann, Oliver Rinner, Nicholas Dupuis, Christoph Wierling, Huaibiao Li, Thomas Kessler, Artur Muradyan, Louisa Krutzfeldt, Moritz Schutte, Felix Dreher, Aspasia Ploubidou, Bodo Lange. Unbiased phospho-proteomic profiling of mouse breast cancer models with DIA mass spectrometry refines CanPath prototype, a platform for predictive cancer pathway modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3916.

Proteomic analysis with data-independent acquisition (DIA) approaches represented by the sequential window acquisition of all theoretical fragment ion spectra (SWATH) technique has gained intense interest in recent years because DIA is able to overcome the intrinsic weakness of conventional data-dependent acquisition (DDA) methods and afford higher throughout and reproducibility for proteome-wide quantification. Although the raw mass spectrometry (MS) data quality and the data-mining workflow conceivably influence the throughput, accuracy and consistency of SWATH-based proteomic quantification, there lacks a systematic evaluation and optimization of the acquisition and data-processing parameters for SWATH MS analysis. Herein, we evaluated the impact of major acquisition parameters such as the precursor mass range, isolation window width and accumulation time as well as the data-processing variables including peak extraction criteria and spectra library selection on SWATH performance. Fine tuning these interdependent parameters can further improve the throughput and accuracy of SWATH quantification compared to the original setting adopted in most SWATH proteomic studies. Furthermore, we compared the effectiveness of two widely used peak extraction software PeakView and Spectronaut in discovery of differentially expressed proteins in a biological context. Our work is believed to contribute to a deeper understanding of the critical factors in SWATH MS experiments and help researchers optimize their SWATH parameters and workflows depending on the sample type, available instrument and software.