dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts.

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.

On-Chip Preconcentration Microchip Capillary Electrophoresis Based CE-PRM-LIVE for High-Throughput Selectivity Profiling of Deubiquitinase Inhibitors.

The family of deubiquitinases (DUBs) comprises ∼100 enzymes that cleave ubiquitin from substrate proteins and thereby regulate key aspects of human physiology. DUBs have recently emerged as disease-relevant and chemically tractable, although currently there are no approved DUB-targeting drugs and most preclinical small molecules are low-potency and/or multitargeted. We paired a novel capillary electrophoresis microchip containing an integrated, "on-chip" C18 bed (SPE-ZipChip) with a TMT version of our recently described PRM-LIVE acquisition scheme on a timsTOF Pro mass spectrometer to facilitate rapid activity-based protein profiling of DUB inhibitors. We demonstrate the ability of the SPE-ZipChip to improve proteome coverage of complex samples as well as the quantitation integrity of CE-PRM-LIVE for TMT labeled samples. These technologies provide a platform to accurately quantify competitive binding of covalent and reversible inhibitors in a multiplexed assay that spans 49 endogenous DUBs in less than 15 min.

PRM-LIVE with Trapped Ion Mobility Spectrometry and Its Application in Selectivity Profiling of Kinase Inhibitors.

Parallel reaction monitoring (PRM) has emerged as a popular approach for targeted protein quantification. With high ion utilization efficiency and first-in-class acquisition speed, the timsTOF Pro provides a powerful platform for PRM analysis. However, sporadic chromatographic drift in peptide retention time represents a fundamental limitation for the reproducible multiplexing of targets across PRM acquisitions. Here, we present PRM-LIVE, an extensible, Python-based acquisition engine for the timsTOF Pro, which dynamically adjusts detection windows for reproducible target scheduling. In this initial implementation, we used iRT peptides as retention time standards and demonstrated reproducible detection and quantification of 1857 tryptic peptides from the cell lysate in a 60 min PRM-LIVE acquisition. As an application in functional proteomics, we use PRM-LIVE in an activity-based protein profiling platform to assess binding selectivity of small-molecule inhibitors against 220 endogenous human kinases.

Highly Multiplexed Targeted Proteomics Acquisition on a TIMS-QTOF.

Targeted proteomics allows the highly sensitive detection of specific peptides and proteins in complex biological samples. Here, we describe a methodology for targeted peptide quantification using a trapped ion mobility quadrupole time-of-flight mass spectrometer (timsTOF Pro). The prm-PASEF method exploits the multiplexing capability provided by the trapped ion mobility separation, allowing more than 200 peptides to be monitored over a 30 min liquid chromatography separation. Compared to conventional parallel reaction monitoring (PRM), precursor ions are accumulated in the trapped ion mobility spectrometry (TIMS) cells and separated according to their shape and charge before eluting into the quadrupole time-of-flight (QTOF) part of the mass spectrometer. The ion mobility trap allows measuring up to six peptides from a single 100 ms ion mobility separation with the current setup. Using these improved mass spectrometric capabilities, we detected and quantified 216 isotope-labeled synthetic peptides (AQUA peptides) spiked in HeLa human cell extract with limits of quantification of 17.2 amol for some peptides. The acquisition method is highly reproducible between injections and enables accurate quantification in biological samples, as demonstrated by quantifying KRas, NRas, and HRas as well as several Ras mutations in lung and colon cancer cell lines on fast 10 min gradient separations.

Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer.

In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, whereas mass spectra are acquired in about 100 microseconds with time-of-flight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive because of its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation - serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al., PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 600,000 fragmentation spectra in standard 120 min LC runs are achievable, which can be used for near exhaustive precursor selection in complex mixtures or accumulating the signal of weak precursors. In 120 min single runs of HeLa digest, MaxQuant identified more than 6,000 proteins without matching to a library and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,500 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored.

Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation.

MOTIVATION: Automatic classification of high-resolution mass spectrometry proteomic data has increasing potential in the early diagnosis of cancer. We propose a new procedure of biomarker discovery in serum protein profiles based on: (i) discrete wavelet transformation of the spectra; (ii) selection of discriminative wavelet coefficients by a statistical test and (iii) building and evaluating a support vector machine classifier by double cross-validation with attention to the generalizability of the results. In addition to the evaluation results (total recognition rate, sensitivity and specificity), the procedure provides the biomarker patterns, i.e. the parts of spectra which discriminate cancer and control individuals. The evaluation was performed on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) serum protein profiles of 66 colorectal cancer patients and 50 controls. RESULTS: Our procedure provided a high recognition rate (97.3%), sensitivity (98.4%) and specificity (95.8%). The extracted biomarker patterns mostly represent the peaks expressing mean differences between the cancer and control spectra. However, we showed that the discriminative power of a peak is not simply expressed by its mean height and cannot be derived by comparison of the mean spectra. The obtained classifiers have high generalization power as measured by the number of support vectors. This prevents overfitting and contributes to the reproducibility of the results, which is required to find biomarkers differentiating cancer patients from healthy individuals. AVAILABILITY: The data and scripts used in this study are available at http://www.math.uni-bremen.de/~theodore/MALDIDWT.