scholarly journals BoxCarmax: a high-selectivity data-independent acquisition mass spectrometry method for the analysis of protein turnover and complex samples

2020 ◽  
Author(s):  
Barbora Salovska ◽  
Wenxue Li ◽  
Yi Di ◽  
Yansheng Liu
Keyword(s):  
Mass Spectrometry ◽  
Protein Turnover ◽  
High Selectivity ◽  
Isotope Labeling ◽  
Cultured Cells ◽  
Protein Quantification ◽  
Serum Starvation ◽  
Complex Samples ◽  
Data Independent Acquisition ◽  
Ms Analysis

ABSTRACTThe data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labelled peptide pairs. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1- and MS2-levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax modestly increased the identification rate for label-free and labeled samples but significantly improved the quantitative accuracy in SILAC and pSILAC samples. We further applied BoxCarmax in studying the protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.

scholarly journals Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments

2020 ◽  
Vol 19 (6) ◽  
pp. 944-959 ◽  
Author(s):  
Tsung-Heng Tsai ◽  
Meena Choi ◽  
Balazs Banfai ◽  
Yansheng Liu ◽  
Brendan X. MacLean ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Profile ◽  
Protein Quantification ◽  
Selected Reaction Monitoring ◽  
Estimation Accuracy ◽  
Spectral Features ◽  
Data Set ◽  
Post Translational Modifications ◽  
Data Independent Acquisition ◽  
Manual Curation

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

scholarly journals Data-independent acquisition mass spectrometry enables reproducible characterization of microbiota function

2018 ◽  
Author(s):  
Juhani Aakko ◽  
Sami Pietilä ◽  
Tomi Suomi ◽  
Mehrad Mahmoudian ◽  
Raine Toivonen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Dynamic Range ◽  
Functional Characterization ◽  
Research Area ◽  
Microbial Composition ◽  
Complex Samples ◽  
Data Independent Acquisition ◽  
Novel Approach ◽  
Open Source Software Package

AbstractMetaproteomics is an emerging research area which aims to reveal the functionality of microbial communities – unlike the increasingly popular metagenomics providing insights only on the functional potential. So far, the common approach in metaproteomics has been data-dependent acquisition mass spectrometry (DDA). However, DDA is known to have limited reproducibility and dynamic range with samples of complex microbial composition. To overcome these limitations, we introduce here a novel approach utilizing data-independent acquisition (DIA) mass spectrometry, which has not been applied in metaproteomics of complex samples before. For robust analysis of the data, we introduce an open-source software package diatools, which is freely available at Docker Hub and runs on various operating systems. Our highly reproducible results on laboratory-assembled microbial mixtures and human fecal samples support the utility of our approach for functional characterization of complex microbiota. Hence, the approach is expected to dramatically improve our understanding on the role of microbiota in health and disease.

scholarly journals Interrogation of Dystrophin and Dystroglycan Complex Protein Turnover After Exon Skipping Therapy

2021 ◽  
pp. 1-20
Author(s):  
James S. Novak ◽  
Rita Spathis ◽  
Utkarsh J. Dang ◽  
Alyson A. Fiorillo ◽  
Ravi Hindupur ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Expression ◽  
Protein Turnover ◽  
Isotope Labeling ◽  
Exon Skipping ◽  
Mdx Mice ◽  
Wild Type ◽  
Gene Correction ◽  
Dystrophin Protein

Recently, the Food and Drug Administration granted accelerated approvals for four exon skipping therapies –Eteplirsen, Golodirsen, Viltolarsen, and Casimersen –for Duchenne Muscular Dystrophy (DMD). However, these treatments have only demonstrated variable and largely sub-therapeutic levels of restored dystrophin protein in DMD patients, limiting their clinical impact. To better understand variable protein expression and the behavior of truncated dystrophin protein in vivo, we assessed turnover dynamics of restored dystrophin and dystroglycan complex (DGC) proteins in mdx mice after exon skipping therapy, compared to those dynamics in wild type mice, using a targeted, highly-reproducible and sensitive, in vivo stable isotope labeling mass spectrometry approach in multiple muscle tissues. Through statistical modeling, we found that restored dystrophin protein exhibited altered stability and slower turnover in treated mdx muscle compared with that in wild type muscle (∼44 d vs. ∼24 d, respectively). Assessment of mRNA transcript stability (quantitative real-time PCR, droplet digital PCR) and dystrophin protein expression (capillary gel electrophoresis, immunofluorescence) support our dystrophin protein turnover measurements and modeling. Further, we assessed pathology-induced muscle fiber turnover through bromodeoxyuridine (BrdU) labeling to model dystrophin and DGC protein turnover in the context persistent fiber degeneration. Our findings reveal sequestration of restored dystrophin protein after exon skipping therapy in mdx muscle leading to a significant extension of its half-life compared to the dynamics of full-length dystrophin in normal muscle. In contrast, DGC proteins show constant turnover attributable to myofiber degeneration and dysregulation of the extracellular matrix (ECM) in dystrophic muscle. Based on our results, we demonstrate the use of targeted mass spectrometry to evaluate the suitability and functionality of restored dystrophin isoforms in the context of disease and propose its use to optimize alternative gene correction strategies in development for DMD.

scholarly journals Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis

2019 ◽  
Vol 20 (23) ◽  
pp. 5932 ◽  
Author(s):  
Yusuke Kawashima ◽  
Eiichiro Watanabe ◽  
Taichi Umeyama ◽  
Daisuke Nakajima ◽  
Masahira Hattori ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Proteomic Analysis ◽  
Protein Quantification ◽  
Single Shot ◽  
Human Embryonic Kidney Cells ◽  
Practical Applications ◽  
Data Independent Acquisition ◽  
Highly Sensitive ◽  
Specific Pathogen ◽  
Sequence Database Search

Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria.

scholarly journals Improved SILAC quantification with data independent acquisition to investigate bortezomib-induced protein degradation

2020 ◽  
Author(s):  
Lindsay K Pino ◽  
Josue Baeza ◽  
Richard Lauman ◽  
Birgit Schilling ◽  
Benjamin A Garcia
Keyword(s):  
Protein Turnover ◽  
Isotope Labeling ◽  
26S Proteasome ◽  
Turnover Rates ◽  
Data Independent Acquisition ◽  
Accuracy And Precision ◽  
Quantitative Accuracy ◽  
Order Of Magnitude ◽  
Ubiquitin Proteasome ◽  
Using Data

ABSTRACTStable isotope labeling by amino acids in cell culture (SILAC) coupled to data-dependent acquisition (DDA) is a common approach to quantitative proteomics with the desirable benefit of reducing batch effects during sample processing and data acquisition. More recently, using data-independent acquisition (DIA/SWATH) to systematically measure peptides has gained popularity for its comprehensiveness, reproducibility, and accuracy of quantification. The complementary advantages of these two techniques logically suggests combining them. Here, we develop a SILAC-DIA-MS workflow using free, open-source software. We determine empirically that using DIA achieves similar peptide detection numbers as DDA and that DIA improves the quantitative accuracy and precision of SILAC by an order of magnitude. Finally, we apply SILAC-DIA-MS to determine protein turnover rates of cells treated with bortezomib, a 26S proteasome inhibitor FDA-approved for multiple myeloma and mantle cell lymphoma. We observe that SILAC-DIA produces more sensitive protein turnover models. Of the proteins determined differentially degraded by both acquisition methods, we find known ubiquitin-proteasome degrands such as HNRNPK, EIF3A, and IF4A1/EIF4A-1, and a slower turnover for CATD, a protein implicated in invasive breast cancer. With improved quantification from DIA, we anticipate this workflow making SILAC-based experiments like protein turnover more sensitive.

scholarly journals A Novel Diagnostic Biomarker, PZP, for Detecting Colorectal Cancer in Type 2 Diabetes Mellitus Patients Identified by Serum-Based Mass Spectrometry

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiayue Yang ◽  
Weigang Fang ◽  
Wenjun Wu ◽  
Zhen Tian ◽  
Rong Gao ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Colorectal Cancer ◽  
Mass Spectrometry ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diagnostic Value ◽  
Serum Samples ◽  
Data Independent Acquisition ◽  
Ms Analysis

Background: Growing evidence has confirmed that populations with type 2 diabetes mellitus (T2DM) have an increasing risk of developing colorectal cancer (CRC). Thus, convenient and effective screening strategies for CRC should be developed for the T2DM population to increase the detection rate of CRC.Methods: Twenty serum samples extracted from five healthy participants, five T2DM patients, five CRC patients and five T2DM patients with CRC (T2DM + CRC) were submitted to data-independent acquisition mass spectrometry (DIA-MS) analysis to discover unique differentially altered proteins (DAPs) for CRC in patients with T2DM. Then, the diagnostic value of pregnancy zone protein (PZP) was validated by ELISA analysis in the validated cohort.Results: Based on DIA-MS analysis, we found eight unique proteins specific to T2DM patients with CRC. Among these proteins, four proteins showed different expression between the T2DM + CRC and T2DM groups, and PZP exhibited the largest difference. Next, the diagnostic value of serum PZP was validated by ELISA analysis with an AUC of 0.713. Moreover, the combination of PZP, CA199 and CEA exhibited encouraging diagnostic value, and the AUC reached 0.916.Conclusion: Overall, our current research implied that PZP could be regarded as a newfound serum biomarker for CRC medical diagnosis in T2DM patients.

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 Ultra-streamlined single cell proteomics by all-in-one chip and data-independent-acquisition mass spectrometry

2021 ◽  
Author(s):  
Hsiung-Lin Tu ◽  
Sofani Gebreyesus ◽  
Asad Ali Siyal ◽  
Reta Birhanu Kitata ◽  
Eric Sheng-Wen Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Single Cell ◽  
Missing Values ◽  
Protein Quantification ◽  
Single Cell Level ◽  
Cell Level ◽  
Proteome Coverage ◽  
Data Independent Acquisition ◽  
General Utility ◽  
Ultrahigh Sensitivity

Abstract Single cell proteomics provides the ultimate resolution to reveal cellular phenotypic heterogeneity and functional network underlying biological processes. Here, we present an ultra-streamlined workflow combining an integrated proteomic chip (iProChip) and data-independent-acquisition (DIA) mass spectrometry for sensitive microproteomics analysis down to single cell level. The iProChip offers multiplexed and automated all-in-one station from cell isolation/counting/imaging to complete proteomic processing within a single device. By mapping to project-specific spectra libraries, the iProChip-DIA enables profiling of 1160 protein groups from triplicate analysis of a single mammalian cell. Furthermore, the applicability of iProChip-DIA was demonstrated using both adherent and non-adherent malignant cells, which reveals 5 orders of proteome coverage, highly consistent ~100-fold protein quantification (1-100 cells) and high reproducibility with low missing values (<16%). With the demonstrated all-in-one cell characterization, ultrahigh sensitivity, robustness, and versatility to add other functionalities, the iProChip-DIA is anticipated to offer general utility to realize advanced proteomics applications at single cell level.

scholarly journals Analyzing Assay Redundancy in Metabolomics using Unique Ion Signatures

2021 ◽  
Author(s):  
Premy Shanthamoorthy ◽  
Hannes Rost
Keyword(s):  
High Resolution ◽  
High Selectivity ◽  
Metabolite Identification ◽  
Compound Identification ◽  
Complex Samples ◽  
Data Independent Acquisition ◽  
Fragment Ions ◽  
Unambiguous Detection ◽  
Unique Compound ◽  
Acquisition Method

Targeted, untargeted and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have enjoyed popularity in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex co-isolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite identification in complex backgrounds using precursor and fragment ion mass-to-charge separation, comparing three benchmarked MS methods (MS1, MRM, DIA). We simulated MS1, MRM and DIA using the NIST LC-MS library as a complex background (8274 compounds at collision energy=35) and compared these methods with regards to unambiguous detection using unique ion signatures. Our simulations show that data generated with DIA with 25 Da mass windows outperformed MS1-only and MRM-based methods by a factor of 13.6-fold and 8.7-fold, respectively. Additionally, we use saturation analysis to show that for highly complex samples, a large portion of MS1-only detection (44.9%) is ambiguous while MRM (6.6%) and DIA (0.6%) present lower ambiguity. Our analysis demonstrates the power of using both high resolution precursor and high resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both DDA and MRM with regards to unique compound identification potential.

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