scholarly journals MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion

2020 ◽  
Vol 31 (7) ◽  
pp. 1459-1472
Author(s):  
Alexander R. Pelletier ◽  
Yun-En Chung ◽  
Zhibin Ning ◽  
Nora Wong ◽  
Daniel Figeys ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Real Time ◽  
Protein Identification ◽  
Mass Spectrometry Analysis ◽  
Spectrometry Analysis

scholarly journals MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion

2020 ◽  
Author(s):  
Alexander R. Pelletier ◽  
Yun-En Chung ◽  
Zhibin Ning ◽  
Nora Wong ◽  
Daniel Figeys ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Real Time ◽  
Mass Spectra ◽  
Protein Identification ◽  
Learning Algorithm ◽  
Mass Spectrometry Data ◽  
Mass Spectrometry Analysis ◽  
Complex Samples ◽  
Spectrometry Analysis ◽  
Abundance Proteins

ABSTRACTMass spectrometry-based proteomics technologies are the prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS’ efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

scholarly journals Rapid classification and prediction of COVID-19 severity by MALDI-TOF mass spectrometry analysis of serum peptidome

2020 ◽  
Author(s):  
Rosa M. Gomila ◽  
Gabriel Martorell ◽  
Pablo A. Fraile-Ribot ◽  
Antonio Doménech-Sánchez ◽  
Antonio Oliver ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Favourable Outcome ◽  
Mass Spectrometry Analysis ◽  
Learning Approaches ◽  
Maldi Tof Ms ◽  
Spectrometry Analysis ◽  
Maldi Tof ◽  
Critical Patients ◽  
Tof Ms

ABSTRACTClassification and early detection of severe COVID-19 patients is urgently required to establish an effective treatment. Here, we tested the utility of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to classify and predict the severity of COVID-19 in a clinical setting. We used this technology to analyse the mass spectra profiles of the sera from 80 COVID-19 patients, clinically classified as mild (33), severe (26) and critical (21), and 20 healthy controls. We found a clear variability of the serum peptidome profile depending on COVID-19 severity. Seventy-eight peaks were significantly different and 12 at least four fold more intense in the set of critical patients than in the mild ones. Analysis of the resulting matrix of peak intensities by machine learning approaches classified severe (severe and critical) and non-severe (mild) patients with a 90% of accuracy. Furthermore, machine learning predicted correctly the favourable outcome of the severe patients in 85% of the cases and the unfavourable in 38% of the cases. Finally, liquid chromatography mass spectrometry analysis of sera identified five proteins that were significantly upregulated in the critical patients. They included serum amyloid proteins A1 and A2, which probably yielded the most intense peaks with m/z 11,530 and 11,686 detected by MALDI-TOF MS.In summary, we demonstrated the potential of the MALDI-TOF MS as a bench to bedside technology to aid clinicians in their decisions to classify COVID-19 patients and predict their evolution.

scholarly journals Gel Electrophoresis/Electroelution Sorting Fractionator combined with Filter Aided Sample preparation (FASP) for deep proteomic analysis

2021 ◽  
Author(s):  
Yassel Ramos ◽  
Alexis Almeida ◽  
Jenis Carpio ◽  
Arielis Rodríguez-Ulloa ◽  
Yasser Perera ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Protein Identification ◽  
Protein Extraction ◽  
Complex Mixture ◽  
Fold Increase ◽  
Mass Spectrometry Analysis ◽  
Protein Fractionation ◽  
Spectrometry Analysis ◽  
Sds Page

AbstractSample preparation and protein fractionation are important issues in proteomic studies in spite of the technological achievements on protein mass spectrometry. Protein extraction procedures strongly affect the performance of fractionation methods by provoking protein dispersion in several fractions. The most notable exception is SDS-PAGE-based protein fractionation due to its extraordinary resolution and the effectiveness of SDS as a solubilizing agent. Its main limitation lies in the poor recovery of the gel-trapped proteins, where protein electro-elution is the most successful approach to overcome this drawback. We created a device to separate complex mixture of proteins and peptides (named “GEES fractionator”) that is based on the continuous Gel Electrophoresis/Electro-elution Sorting of these molecules. In an unsupervised process, complex mixtures of proteins or peptides are fractionated into the gel while separated fractions are simultaneously and sequentially electro-eluted to the solution containing wells. The performance of the device was studied for SDS-PAGE-based protein fractionation in terms of reproducibility, protein recovery and loading capacity. In the SDS-free PAGE setup, complex peptide mixtures can also be fractionated. More than 11 700 proteins were identified in the whole-cell lysate of the CaSki cell line by using the GEES fractionator combined with the Filter Aided Sample Preparation (FASP) method and mass spectrometry analysis. GEES-based proteome characterization shows a 1.7 fold increase in the number of identified proteins compared to the unfractionated sample analysis. Proteins involved in the co-regulated transcription activity, as well as cancer related pathways such as apoptosis signaling, P53 and RAS pathways are more represented in the protein identification output of GEES-based fractionation approaches.

scholarly journals Assessing the feasibility and acceptability of online measurements of exhaled volatile organic compounds (VOCs) in children with preschool wheeze: a pilot study

2021 ◽  
Vol 5 (1) ◽  
pp. e001003
Author(s):  
Karl Holden ◽  
Misty Makinde ◽  
Michael Wilde ◽  
Matthew Richardson ◽  
Tim Coats ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Preschool Children ◽  
Volatile Organic Compounds ◽  
Real Time ◽  
Organic Compounds ◽  
Mass Spectrometry Analysis ◽  
Breathing Patterns ◽  
Spectrometry Analysis ◽  
Non Invasive ◽  
Volatile Organic

BackgroundInvestigating airway inflammation and pathology in wheezy preschool children is both technically and ethically challenging. Identifying and validating non-invasive tests would be a huge clinical advance. Real-time analysis of exhaled volatile organic compounds (VOCs) in adults is established, however, the feasibility of this non-invasive method in young children remains undetermined.AimTo determine the feasibility and acceptability of obtaining breath samples from preschool children by means of real-time mass spectrometry analysis of exhaled VOCs.MethodsBreath samples from preschool children were collected and analysed in real time by proton transfer reaction–time of flight–mass spectrometry (PTR–TOF–MS) capturing unique breath profiles. Acetone (mass channel m/z 59) was used as a reference profile to investigate the breath cycle in more detail. Dynamic time warping (DTW) was used to compare VOC profiles from adult breath to those we obtained in preschool children.Results16 children were recruited in the study, of which eight had acute doctor-diagnosed wheeze (mean (range) age 3.2 (1.9–4.5) years) and eight had no history of wheezing (age 3.3 (2.2–4.1) years). Fully analysable samples were obtained in 11 (68%). DTW was used to ascertain the distance between the time series of mass channel m/z 59 (acetone) and the other 193 channels. Commonality of 12 channels (15, 31, 33, 41, 43, 51, 53, 55, 57, 60, 63 and 77) was established between adult and preschool child samples despite differences in the breathing patterns.ConclusionReal-time measurement of exhaled VOCs by means of PTR–MS is feasible and acceptable in preschool children. Commonality in VOC profiles was found between adult and preschool children.

scholarly journals Unraveling endometriosis-associated ovarian carcinomas using integrative proteomics

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 189
Author(s):  
Felix Leung ◽  
Marcus Q. Bernardini ◽  
Kun Liang ◽  
Ihor Batruch ◽  
Marjan Rouzbahman ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Cell Carcinoma ◽  
Protein Identification ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Mass Spectrometry Analysis ◽  
Ovarian Carcinomas ◽  
Spectrometry Analysis ◽  
Proteomic Data ◽  
Fresh Frozen

Background: To elucidate potential markers of endometriosis and endometriosis-associated endometrioid and clear cell ovarian carcinomas using mass spectrometry-based proteomics. Methods: A total of 21 fresh, frozen tissues from patients diagnosed with clear cell carcinoma, endometrioid carcinoma, endometriosis and benign endometrium were subjected to an in-depth liquid chromatography-tandem mass spectrometry analysis on the Q-Exactive Plus. Protein identification and quantification were performed using MaxQuant, while downstream analyses were performed using Perseus and various bioinformatics databases. Results: Approximately 9000 proteins were identified in total, representing the first in-depth proteomic investigation of endometriosis and its associated cancers. This proteomic data was shown to be biologically sound, with minimal variation within patient cohorts and recapitulation of known markers. While moderate concordance with genomic data was observed, it was shown that such data are limited in their abilities to represent tumours on the protein level and to distinguish tumours from their benign precursors. Conclusions: The proteomic data suggests that distinct markers may differentiate endometrioid and clear cell carcinoma from endometriosis. These markers may be indicators of pathobiology but will need to be further investigated. Ultimately, this dataset may serve as a basis to unravel the underlying biology of the endometrioid and clear cell cancers with respect to their endometriotic origins.

Proteomic and direct analysis in real time mass spectrometry analysis of a Native American ceremonial hat

The Analyst ◽  
2019 ◽  
Vol 144 (24) ◽  
pp. 7437-7446 ◽  
Author(s):  
Timothy P. Cleland ◽  
G. Asher Newsome ◽  
R. Eric Hollinger
Keyword(s):  
Mass Spectrometry ◽  
Native American ◽  
Natural History ◽  
Real Time ◽  
Smithsonian Institution ◽  
Direct Analysis ◽  
Mass Spectrometry Analysis ◽  
National Museum ◽  
Spectrometry Analysis

Complementary mass spectrometry analyses were performed to study a broken ceremonial hat of the Tlingit in the collection of the Smithsonian Institution National Museum of Natural History.

Integrated plant proteomics — putting the green genomes to work

2003 ◽  
Vol 30 (5) ◽  
pp. 471 ◽  
Author(s):  
Joshua L. Heazlewood ◽  
A. Harvey Millar
Keyword(s):  
Mass Spectrometry ◽  
Data Storage ◽  
Large Scale ◽  
Protein Identification ◽  
Mass Spectrometry Analysis ◽  
Plant Science ◽  
Genomic Information ◽  
Plant Proteomics ◽  
Spectrometry Analysis ◽  
Complete Set

Protein analysis has been at the heart of plant science for many years, but with new questions emerging from an abundance of genomic information and further improvements in technology, there are now new opportunities to undertake large-scale analyses and to move to more complex systems than has been possible previously. This explosion of interest and data is often referred to simply as proteomics, which is the study of the complete set of proteins expressed at a given time and place, the proteome. As its name suggests proteomics is intricately linked to allied technologies such as genomics, transcriptomics and metabolomics. In this review of plant proteomics we outline a series of issues that face the practical user, particularly the largest problem that currently faces researchers, the myriad of options to choose from. The choices, problems and pitfalls of entering into gel-based and non-gel-based arraying techniques are discussed together with advances in pre-fractionation of samples, liquid chromatography separations and subcellular analyses. Issues relating to mass spectrometry analysis and the eventual protein identification are outlined, and the dilemmas of data storage and analysis are highlighted. During this tour we provide a series of references to the literature — experimental, theoretical and technical — to illustrate the breadth of current investigations using these techniques.

scholarly journals Unraveling endometriosis-associated ovarian carcinomas using integrative proteomics

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 189
Author(s):  
Felix Leung ◽  
Marcus Q. Bernardini ◽  
Kun Liang ◽  
Ihor Batruch ◽  
Marjan Rouzbahman ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Cell Carcinoma ◽  
Protein Identification ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Mass Spectrometry Analysis ◽  
Ovarian Carcinomas ◽  
Spectrometry Analysis ◽  
Proteomic Data ◽  
Fresh Frozen

Background: To elucidate potential markers of endometriosis and endometriosis-associated endometrioid and clear cell ovarian carcinomas using mass spectrometry-based proteomics. Methods: A total of 21 fresh, frozen tissues from patients diagnosed with clear cell carcinoma, endometrioid carcinoma, endometriosis and benign endometrium were subjected to an in-depth liquid chromatography-tandem mass spectrometry analysis on the Q-Exactive Plus. Protein identification and quantification were performed using MaxQuant, while downstream analyses were performed using Perseus and various bioinformatics databases. Results: Approximately 9000 proteins were identified in total, representing the first in-depth proteomic investigation of endometriosis and its associated cancers. This proteomic data was shown to be biologically sound, with minimal variation within patient cohorts and recapitulation of known markers. While moderate concordance with genomic data was observed, it was shown that such data are limited in their abilities to represent tumours on the protein level and to distinguish tumours from their benign precursors. Conclusions: The proteomic data suggests that distinct markers may differentiate endometrioid and clear cell carcinoma from endometriosis. These markers may be indicators of pathobiology but will need to be further investigated. Ultimately, this dataset may serve as a basis to unravel the underlying biology of the endometrioid and clear cell cancers with respect to their endometriotic origins.

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