scholarly journals Time-resolved in vivo ubiquitinome profiling by DIA-MS reveals USP7 targets on a proteome-wide scale

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Martin Steger ◽  
Vadim Demichev ◽  
Mattias Backman ◽  
Uli Ohmayer ◽  
Phillip Ihmor ◽  
...  
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Sample Preparation ◽  
Mode Of Action ◽  
System Level ◽  
High Temporal Resolution ◽  
Time Resolved ◽  
Data Independent Acquisition ◽  
Wide Scale

AbstractMass spectrometry (MS)-based ubiquitinomics provides system-level understanding of ubiquitin signaling. Here we present a scalable workflow for deep and precise in vivo ubiquitinome profiling, coupling an improved sample preparation protocol with data-independent acquisition (DIA)-MS and neural network-based data processing specifically optimized for ubiquitinomics. Compared to data-dependent acquisition (DDA), our method more than triples identification numbers to 70,000 ubiquitinated peptides in single MS runs, while significantly improving robustness and quantification precision. Upon inhibition of the oncology target USP7, we simultaneously record ubiquitination and consequent changes in abundance of more than 8,000 proteins at high temporal resolution. While ubiquitination of hundreds of proteins increases within minutes of USP7 inhibition, we find that only a small fraction of those are ever degraded, thereby dissecting the scope of USP7 action. Our method enables rapid mode-of-action profiling of candidate drugs targeting DUBs or ubiquitin ligases at high precision and throughput.

Dipeptidyl peptidase 9 substrates and their discovery: current progress and the application of mass spectrometry-based approaches

2016 ◽  
Vol 397 (9) ◽  
pp. 837-856 ◽  
Author(s):  
Claire H. Wilson ◽  
Hui Emma Zhang ◽  
Mark D. Gorrell ◽  
Catherine A. Abbott
Keyword(s):  
Mass Spectrometry ◽  
Type Ii Diabetes ◽  
Dipeptidyl Peptidase ◽  
Dipeptidyl Peptidase 4 ◽  
Protein Substrates ◽  
Inflammatory Conditions ◽  
Protease Substrate ◽  
Wide Scale ◽  
Dipeptidyl Peptidases

Abstract The enzyme members of the dipeptidyl peptidase 4 (DPP4) gene family have the very unusual capacity to cleave the post-proline bond to release dipeptides from the N-terminus of peptide/protein substrates. DPP4 and related enzymes are current and potential therapeutic targets in the treatment of type II diabetes, inflammatory conditions and cancer. Despite this, the precise biological function of individual dipeptidyl peptidases (DPPs), other than DPP4, and knowledge of their in vivo substrates remains largely unknown. For many years, identification of physiological DPP substrates has been difficult due to limitations in the available tools. Now, with advances in mass spectrometry based approaches, we can discover DPP substrates on a system wide-scale. Application of these approaches has helped reveal some of the in vivo natural substrates of DPP8 and DPP9 and their unique biological roles. In this review, we provide a general overview of some tools and approaches available for protease substrate discovery and their applicability to the DPPs with a specific focus on DPP9 substrates. This review provides comment upon potential approaches for future substrate elucidation.

scholarly journals Decoding neurobiological spike trains using recurrent neural networks: a case study with electrophysiological auditory cortex recordings

2021 ◽  
Author(s):  
Péter Szabó ◽  
Péter Barthó
Keyword(s):  
Neural Network ◽  
Recurrent Network ◽  
Neural Code ◽  
High Temporal Resolution ◽  
Population Vector ◽  
Extraction Step ◽  
Cortical Responses ◽  
Artificial Datasets

AbstractRecent advancements in multielectrode methods and spike-sorting algorithms enable the in vivo recording of the activities of many neurons at a high temporal resolution. These datasets offer new opportunities in the investigation of the biological neural code, including the direct testing of specific coding hypotheses, but they also reveal the limitations of present decoder algorithms. Classical methods rely on a manual feature extraction step, resulting in a feature vector, like the firing rates of an ensemble of neurons. In this paper, we present a recurrent neural-network-based decoder and evaluate its performance on experimental and artificial datasets. The experimental datasets were obtained by recording the auditory cortical responses of rats exposed to sound stimuli, while the artificial datasets represent preset encoding schemes. The task of the decoder was to classify the action potential timeseries according to the corresponding sound stimuli. It is illustrated that, depending on the coding scheme, the performance of the recurrent-network-based decoder can exceed the performance of the classical methods. We also show how randomized copies of the training datasets can be used to reveal the role of candidate spike-train features. We conclude that artificial neural network decoders can be a useful alternative to classical population vector-based techniques in studies of the biological neural code.

scholarly journals HILIC-Enabled 13C Metabolomics Strategies: Comparing Quantitative Precision and Spectral Accuracy of QTOF High- and QQQ Low-Resolution Mass Spectrometry

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 63 ◽  
Author(s):  
André Feith ◽  
Attila Teleki ◽  
Michaela Graf ◽  
Lorenzo Favilli ◽  
Ralf Takors
Keyword(s):  
Mass Spectrometry ◽  
Isotope Dilution Mass Spectrometry ◽  
Absolute Quantification ◽  
Spectral Accuracy ◽  
Time Resolved ◽  
13C Tracer ◽  
Accurate Quantification ◽  
Tracer Experiments

Dynamic 13C-tracer-based flux analyses of in vivo reaction networks still require a continuous development of advanced quantification methods applying state-of-the-art mass spectrometry platforms. Utilizing alkaline HILIC chromatography, we adapt strategies for a systematic quantification study in non- and 13C-labeled multicomponent endogenous Corynebacterium glutamicum extracts by LC-QTOF high resolution (HRMS) and LC-QQQ tandem mass spectrometry (MS/MS). Without prior derivatization, a representative cross-section of 17 central carbon and anabolic key intermediates were analyzed with high selectivity and sensitivity under optimized ESI-MS settings. In column detection limits for the absolute quantification range were between 6.8–304.7 (QQQ) and 28.7–881.5 fmol (QTOF) with comparable linearities (3–5 orders of magnitude) and enhanced precision using QQQ-MRM detection. Tailor-made preparations of uniformly (U)13C-labeled cultivation extracts for isotope dilution mass spectrometry enabled the accurate quantification in complex sample matrices and extended linearities without effect on method parameters. Furthermore, evaluation of metabolite-specific m+1-to-m+0 ratios (ISR1:0) in non-labeled extracts exhibited sufficient methodical spectral accuracies with mean deviations of 3.89 ± 3.54% (QTOF) and 4.01 ± 3.01% (QQQ). Based on the excellent HILIC performance, conformity analysis of time-resolved isotopic enrichments in 13C-tracer experiments revealed sufficient spectral accuracy for QQQ-SIM detection. However, only QTOF-HRMS ensures determination of the full isotopologue space in complex matrices without mass interferences.

scholarly journals Implementing solid-phase-enhanced sample preparation for Co-Immunoprecipitation with Mass Spectrometry for C. elegans

2021 ◽  
Author(s):  
Guelkiz Baytek ◽  
Oliver Popp ◽  
Philipp Mertins ◽  
Baris Tursun
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Preparation Method ◽  
Solid Phase ◽  
Protein Protein Interactions ◽  
Sample Preparation Method ◽  
C Elegans

Studying protein-protein interactions in vivo can reveal key molecular mechanisms of biological processes. Co-Immunoprecipitation followed by Mass Spectrometry (CoIP-MS) allows detection of protein-protein interactions in high-throughput. The nematode Caenorhabditis elegans (C. elegans) is a powerful genetic model organism for in vivo studies. Yet, its rigid cuticle and complex tissues require optimization for protein biochemistry applications to ensure robustness and reproducibility of experimental outcomes. Therefore, we optimized CoIP-MS application to C. elegans protein lysates by combining a native CoIP procedure with an efficient sample preparation method called single-pot, solid-phase-enhanced, sample preparation method (SP3). Our results based on the subunits of the conserved chromatin remodeler FACT demonstrate that our SP3-integrated CoIP-MS procedure for C. elegans samples is highly accurate and robust. Moreover, in a previous study (Baytek et al. 2021), we extended our technique to studying the chromodomain factor MRG-1 (MRG15 in human), which resulted in unprecedented findings.

scholarly journals Matrix-matched calibration curves for assessing analytical figures of merit in quantitative proteomics

2019 ◽  
Author(s):  
Lindsay K Pino ◽  
Han-Yin Yang ◽  
William Stafford Noble ◽  
Brian C Searle ◽  
Andrew N Hoofnagle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Quantitative Proteomics ◽  
Protein Abundance ◽  
Complex Samples ◽  
Figures Of Merit ◽  
Data Independent Acquisition ◽  
Proteomics Experiment ◽  
Series Of Experiments ◽  
Analytical Figures Of Merit

AbstractMass spectrometry is a powerful tool for quantifying protein abundance in complex samples. Advances in sample preparation and the development of data independent acquisition (DIA) mass spectrometry approaches have increased the number of peptides and proteins measured per sample. Here we present a series of experiments demonstrating how to assess whether a peptide measurement is quantitative by mass spectrometry. Our results demonstrate that increasing the number of detected peptides in a proteomics experiment does not necessarily result in increased numbers of peptides that can be measured quantitatively.

scholarly journals Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification by data independent acquisition mass spectrometry

2021 ◽  
Author(s):  
Jian Song ◽  
Fangfei Zhang ◽  
Changbin Yu
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Deep Neural Network ◽  
Peptide Identification ◽  
Prediction Performance ◽  
Fragmentation Mechanism ◽  
Statistical Validation ◽  
Test Dataset ◽  
Data Independent Acquisition ◽  
Fragment Ions

ABSTRACTMotivationIdentification of peptides in data-independent acquisition (DIA) mass spectrometry (MS) typically relies on the scoring for the peak groups upon extracted chromatograms of fragment ions. Expanding fragment scoring features closer to the genuine experimental spectra can improve DIA identification. Deep learning is able to predict fragment presence without understanding the fragmentation mechanism that can enrich the scoring features in DIA identification.ResultsIn this work, we developed a deep neural network-based model, Alpha-Frag, to predict the fragment ions that should be present for a given peptide by reporting their probabilities of existence. The prediction performance was evaluated in terms of intersection over union (IoU), and Alpha-Frag achieved an average of >0.7 and outperformed substantially the benchmarks across the validation datasets. Furthermore, qualitative scores based on Alpha-Frag were designed and incorporated into the peptide statistical validation tools as auxiliary scores. Our preliminary experiments show that the qualitative scores by Alpha-Frag are profitable for DIA identification, especially in the case of short gradient, and yielded an increase of 10.1%-29.3% improvements for the test dataset compared to the same scoring strategy but using Prosit.Availability and ImplementationSource code and the trained model are available at www.github.com/YuAirLab/Alpha-Frag.

scholarly journals Mass spectrometry-based proteomic analysis of NSCLC tumor and biopsy samples

2021 ◽  
Author(s):  
Janne Lehtiö ◽  
Taner Arslan ◽  
Ioannis Siavelis ◽  
Yanbo Pan ◽  
Fabio Socciarelli ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Molecular Subtypes ◽  
Solid Phase ◽  
Median Number ◽  
Label Free ◽  
Biopsy Material ◽  
Entire Cohort ◽  
Data Independent Acquisition ◽  
Depth Analysis

Abstract The associated publication reports proteogenomic analysis of non-small cell lung cancer, where we identified molecular subtypes with distinct immune evasion mechanisms and therapeutic targets, and validated our classification method in separate clinical cohorts. This protocol describes the sample preparation and mass spectrometry (MS)-based in-depth and rapid proteomic analyses of tumor and biopsy samples. We deployed single-pot solid-phase-enhanced sample preparation (SP3). For the in-depth analysis, we used TMT labeling, followed by high-resolution isoelectric focusing (HiRIEF) prefractionation and LC-MS with data-dependent acquisition (DDA). The reported protocol achieved analytical depth of close to 14,000 quantified proteins and almost 10,000 across the entire cohort of 141 samples. The rapid analysis was label-free, based on LC-MS with data-independent acquisition (DIA). The median number of identified proteins was 3,967 and 3,552 in two independent cohorts of tumor samples (n = 141 and 208, respectively), and 2,494 in another cohort of biopsy material (n = 84).

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