Mass Dynamics 1.0: A Streamlined, Web-Based Environment for Analyzing, Sharing, and Integrating Label-Free Data

AbstractLabel Free Quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web based analysis environment that can analyze and visualize LFQ data produced by software such as Maxquant. Unlike other tools, MD 1.0 utilizes cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardisation in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range.The MD 1.0 platform is available globally via: https://app.massdynamics.com/[email protected]

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BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics

Analytical Chemistry ◽

10.1021/acs.analchem.1c03338 ◽

2022 ◽

Author(s):

Devang Mehta ◽

Sabine Scandola ◽

R. Glen Uhrig

Keyword(s):

Quantitative Proteomics ◽

Depth Range ◽

Label Free ◽

Data Independent Acquisition ◽

Free Data

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Serum peptidome: diagnostic window into pathogenic processes following occupational exposure to carbon nanomaterials

Particle and Fibre Toxicology ◽

10.1186/s12989-021-00431-0 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Ekaterina Mostovenko ◽

Matthew M. Dahm ◽

Mary K. Schubauer-Berigan ◽

Tracy Eye ◽

Aaron Erdely ◽

...

Keyword(s):

Carbon Nanomaterials ◽

Model Building ◽

Peptide Sequence ◽

Vascular Pathology ◽

Label Free ◽

Industrial Setting ◽

Free Data ◽

Significant Release ◽

Diagnostic Window ◽

Systemic Health

Abstract Background Growing industrial use of carbon nanotubes and nanofibers (CNT/F) warrants consideration of human health outcomes. CNT/F produces pulmonary, cardiovascular, and other toxic effects in animals along with a significant release of bioactive peptides into the circulation, the augmented serum peptidome. While epidemiology among CNT/F workers reports on few acute symptoms, there remains concern over sub-clinical CNT/F effects that may prime for chronic disease, necessitating sensitive health outcome diagnostic markers for longitudinal follow-up. Methods Here, the serum peptidome was assessed for its biomarker potential in detecting sub-symptomatic pathobiology among CNT/F workers using label-free data-independent mass spectrometry. Studies employed a stratified design between High (> 0.5 µg/m3) and Low (< 0.1 µg/m3) inhalable CNT/F exposures in the industrial setting. Peptide biomarker model building and refinement employed linear regression and partial least squared discriminant analyses. Top-ranked peptides were then sequence identified and evaluated for pathological-relevance. Results In total, 41 peptides were found to be highly discriminatory after model building with a strong linear correlation to personal CNT/F exposure. The top-five peptide model offered ideal prediction with high accuracy (Q2 = 0.99916). Unsupervised validation affirmed 43.5% of the serum peptidomic variance was attributable to CNT/F exposure. Peptide sequence identification reveals a predominant association with vascular pathology. ARHGAP21, ADAM15 and PLPP3 peptides suggest heightened cardiovasculature permeability and F13A1, FBN1 and VWDE peptides infer a pro-thrombotic state among High CNT/F workers. Conclusions The serum peptidome affords a diagnostic window into sub-symptomatic pathology among CNT/F exposed workers for longitudinal monitoring of systemic health risks. Graphical abstract

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Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS)

10.1101/2021.03.22.436473 ◽

2021 ◽

Author(s):

Michael Fanous ◽

Chuqiao Shi ◽

Megan Caputo ◽

Laurie Rund ◽

Rodney Johnson ◽

...

Keyword(s):

Brain Tissue ◽

Dry Mass ◽

Phase Imaging ◽

Label Free ◽

Light Interference ◽

Free Data ◽

Highly Sensitive ◽

Expert Pathologist ◽

Molecular Specificity ◽

The Central Nervous System

Inadequate myelination in the central nervous system is associated with neurodevelopmental complications. Thus, quantitative, high spatial resolution measurements of myelin levels are highly desirable. We used spatial light interference microcopy (SLIM), a highly sensitive quantitative phase imaging (QPI) technique, to correlate the dry mass content of myelin in piglet brain tissue with dietary changes and gestational size. We combined SLIM micrographs with an AI classifying model that allows us to discern subtle disparities in myelin distributions with high accuracy. This concept of combining QPI label-free data with AI for the purpose of extracting molecular specificity has recently been introduced by our laboratory as phase imaging with computational specificity (PICS). Training on nine thousand SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate gestational size and diet type with an accuracy of 82% and 80%, respectively. To our knowledge, this type of evaluation is impossible to perform by an expert pathologist or other techniques.

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The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics

F1000Research ◽

10.12688/f1000research.2-272.v2 ◽

2014 ◽

Vol 2 ◽

pp. 272 ◽

Cited By ~ 22

Author(s):

Jakob Vowinckel ◽

Floriana Capuano ◽

Kate Campbell ◽

Michael J. Deery ◽

Kathryn S. Lilley ◽

...

Keyword(s):

Sample Preparation ◽

Protein Identification ◽

Protein Detection ◽

Cost Effective ◽

High Signal ◽

Label Free ◽

Signal Variation ◽

Signal Stability ◽

Advantages And Disadvantages ◽

Free Data

The combination of qualitative analysis with label-free quantification has greatly facilitated the throughput and flexibility of novel proteomic techniques. However, such methods rely heavily on robust and reproducible sample preparation procedures. Here, we benchmark a selection of in gel, on filter, and in solution digestion workflows for their application in label-free proteomics. Each procedure was associated with differing advantages and disadvantages. The in gel methods interrogated were cost effective, but were limited in throughput and digest efficiency. Filter-aided sample preparations facilitated reasonable processing times and yielded a balanced representation of membrane proteins, but led to a high signal variation in quantification experiments. Two in solution digest protocols, however, gave optimal performance for label-free proteomics. A protocol based on the detergent RapiGest led to the highest number of detected proteins at second-best signal stability, while a protocol based on acetonitrile-digestion, RapidACN, scored best in throughput and signal stability but came second in protein identification. In addition, we compared label-free data dependent (DDA) and data independent (SWATH) acquisition on a TripleTOF 5600 instrument. While largely similar in protein detection, SWATH outperformed DDA in quantification, reducing signal variation and markedly increasing the number of precisely quantified peptides.

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Proteins Involved in the Induction of Procoagulant Activity and Autoimmune Response in Patients With Primary Antiphospholipid Syndrome

Clinical and Applied Thrombosis/Hemostasis ◽

10.1177/1076029620905338 ◽

2020 ◽

Vol 26 ◽

pp. 107602962090533

Author(s):

Débora Medeiros Araújo ◽

Carlos Ewerton Maia Rodrigues ◽

Nidyedja Goyanna Gomes Gonçalves ◽

Carlos Nobre Rabelo-Júnior ◽

Marina Duarte Pinto Lobo ◽

...

Keyword(s):

Antiphospholipid Syndrome ◽

Protein Profile ◽

Procoagulant Activity ◽

Autoimmune Response ◽

Diagnostic Tools ◽

Primary Antiphospholipid Syndrome ◽

Label Free ◽

Diagnosis And Prognosis ◽

Free Data ◽

Α Chain

The aim of this study was to determine the plasma protein profile of patients with primary antiphospholipid syndrome (PAPS) compared to healthy controls and identify proteins that might be used in the evaluation, diagnosis, and prognosis of this condition. The sample consisted of 14 patients with PAPS and 17 sex- and age-matched controls. Plasma samples were submitted to proteomic analysis (albumin and immunoglobulin G depletion, concentration, digestion, and label-free data-independent mass spectrometry). The software ExpressionE was used to quantify intergroup differences in protein expression. The analysis yielded 65 plasma proteins of which 11 were differentially expressed (9 upregulated and 2 downregulated) in relation to controls. Four of these are known to play a role in pathophysiological mechanisms of thrombosis: fibrinogen α chain, fibrinogen α chain, apolipoprotein C-III, and α-1-glycoprotein-1. Our analysis revealed autoimmune response and the presence of proteins believed to be functionally involved in the induction of procoagulant activity in patients with PAPS. Further studies are necessary to confirm our findings and may eventually lead to the development of significantly more accurate diagnostic tools.

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Differential urine proteome analysis of a ventilator-induced lung injury rat model by label-free quantitative and parallel reaction monitoring proteomics

Scientific Reports ◽

10.1038/s41598-021-01007-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Weiwei Qin ◽

Xiao Zhang ◽

Lingnan Chen ◽

Qiujie Li ◽

Benwang Zhang ◽

...

Keyword(s):

Lung Injury ◽

Quantitative Proteomics ◽

Urinary Biomarkers ◽

Reaction Monitoring ◽

Label Free ◽

Parallel Reaction ◽

Ventilator Induced Lung Injury ◽

Parallel Reaction Monitoring ◽

Free Data ◽

Differential Proteins

AbstractUrine is a promising resource for biomarker research. Therefore, the purpose of this study was to investigate potential urinary biomarkers to monitor the disease activity of ventilator-induced lung injury (VILI). In the discovery phase, a label-free data-dependent acquisition (DDA) quantitative proteomics method was used to profile the urinary proteomes of VILI rats. For further validation, the differential proteins were verified by parallel reaction monitoring (PRM)-targeted quantitative proteomics. In total, 727 high-confidence proteins were identified with at least 1 unique peptide (FDR ≤ 1%). Compared to the control group, 110 proteins (65 upregulated, 45 downregulated) were significantly changed in the VILI group (1.5-fold change, P < 0.05). The canonical pathways and protein–protein interaction analyses revealed that the differentially expressed proteins were enriched in multiple functions, including oxidative stress and inflammatory responses. Finally, thirteen proteins were identified as candidate biomarkers for VILI by PRM validation. Among these PRM-validated proteins, AMPN, MEP1B, LYSC1, DPP4 and CYC were previously reported as lung-associated disease biomarkers. SLC31, MEP1A, S15A2, NHRF1, XPP2, GGT1, HEXA, and ATPB were newly discovered in this study. Our results suggest that the urinary proteome might reflect the pathophysiological changes associated with VILI. These differential proteins are potential urinary biomarkers for the activity of VILI.

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Bioinformatics analysis of NSCLC multi-omics data

10.21203/rs.3.pex-1562/v1 ◽

2021 ◽

Cited By ~ 1

Author(s):

Janne Lehtiö ◽

Taner Arslan ◽

Ioannis Siavelis ◽

Yanbo Pan ◽

Fabio Socciarelli ◽

...

Keyword(s):

Bioinformatics Analysis ◽

Molecular Subtypes ◽

Recursive Feature Elimination ◽

Support Vector ◽

Omics Data ◽

Label Free ◽

Proteomics Data ◽

Small Cell Lung ◽

Free Data ◽

Related Proteins

Abstract The associated publication reports proteogenomic analysis of non-small cell lung cancer (NSCLC), 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 sections of the bioinformatics analysis of the multi-omics data, namely, data analysis and processing for panel sequencing, identification of cancer- and driver-related proteins in proteomics data, proteogenomics search, and machine learning-based classifiers for NSCLC subtyping. Specifically, a cohort classifier was built using support-vector machine-recursive feature elimination (SVM-RFE) algorithm applied to in-depth proteomics data from a cohort of 141 samples. The classifier was then validated in three external datasets. Another classifier, suitable for single-sample subtyping, was built using k-top scoring pairs (k-TSP) algorithm applied to label-free data from a cohort of 136 samples. The k-TSP-based classifier was validated in two independent cohorts and an additional external dataset.

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