Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Proteomics01:33

Proteomics

7.3K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.3K
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.4K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
6.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Rapid Peptide Mapping of Monoclonal Antibodies with Direct Infusion Mass Spectrometry.

bioRxiv : the preprint server for biology·2026
Same author

SLX4IP limits replication stress globally and at ALT telomeres.

The EMBO journal·2026
Same author

Primate lineage specification requires suppression of Alu hyperediting.

bioRxiv : the preprint server for biology·2026
Same author

Regarding Emitter Positioning for Nanoflow Electrospray Ionization with a High-Capacity Inlet Capillary.

Journal of the American Society for Mass Spectrometry·2026
Same author

Proteomic and Lipidomic Atlas of Gut-Associated Lymph and Venous Depots in Female Piglets.

Arteriosclerosis, thrombosis, and vascular biology·2026
Same author

Atlas of lysine acetylation in the mouse.

bioRxiv : the preprint server for biology·2026
Same journal

From Method-Defined Signals to Reference Measurement Procedures: Two Decades of Mass Spectrometry-Based ProGRP Quantification.

Journal of proteome research·2026
Same journal

Proteomic Profiling of Extracellular Vesicle-Enriched Plasma Using Mag-Net for Biomarker Discovery in Pancreatic Ductal Adenocarcinoma.

Journal of proteome research·2026
Same journal

Computationally Efficient Bayesian Estimation of Graphical Networks for Omics Data.

Journal of proteome research·2026
Same journal

Hierarchy of MS-Based Evidence.

Journal of proteome research·2026
Same journal

Proteomic Profiling of Exosomes from HPV-Positive and HPV-Negative Head and Neck Squamous Cell Carcinoma: Selective Cargo Packaging.

Journal of proteome research·2026
Same journal

Proteomic Analysis Identifies ATE1-Dependent Arginylation Dysregulation across Meningioma Grades.

Journal of proteome research·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 2025

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation
07:12

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Published on: April 24, 2020

10.0K

Technical Evaluation of Plasma Proteomics Technologies.

William F Beimers1, Katherine A Overmyer1,2,3, Pavel Sinitcyn2,4,5

  • 1Department of Biomolecular Chemistry, University of Wisconsin, Madison, Wisconsin 53506, United States.

Journal of Proteome Research
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

This study evaluated six plasma proteomics technologies for biomedical research. Seer Proteograph XT offered the greatest proteomic depth and quantifiable proteins, though all methods showed differences in detecting cancer biomarkers.

Keywords:
LC-MSMag-NetOlinkPreOmicsSeermass spectrometrymethod comparisonplasmaproteomics

More Related Videos

Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
05:30

Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies

Published on: January 31, 2025

350
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

11.9K

Related Experiment Videos

Last Updated: Jun 14, 2025

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation
07:12

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Published on: April 24, 2020

10.0K
Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
05:30

Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies

Published on: January 31, 2025

350
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

11.9K

Area of Science:

  • Biomedical research
  • Proteomics
  • Analytical chemistry

Background:

  • Plasma proteomics technologies are crucial for biomedical research and rapidly advancing.
  • Evaluating these technologies is essential for selecting appropriate methods for various applications.

Purpose of the Study:

  • To conduct a technical evaluation of six prominent plasma proteomics technologies.
  • To compare their performance based on proteomic depth, reproducibility, linearity, lipid interference tolerance, and limit of detection/quantification.
  • To assess their applicability in distinguishing between healthy and non-small cell lung cancer (NSCLC) patient samples.

Main Methods:

  • Six plasma proteomics technologies were evaluated: unenriched (Neat), acid depletion, PreOmics ENRICHplus, Mag-Net, Seer Proteograph XT, and Olink Explore HT.
  • Performance metrics included proteomic depth, reproducibility, linearity, lipid interference tolerance, and limit of detection/quantification (LOD/LOQ).
  • A total of 618 liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments and 93 Olink Explore HT assays were performed. A non-small cell lung cancer (NSCLC) cohort was used to test clinical applicability.

Main Results:

  • Seer Proteograph XT achieved the highest proteomic depth (∼4500 proteins) and number of quantifiable proteins (LOD: 4407, LOQ: 2696).
  • Olink Explore HT detected ∼2600 proteins, with 2002 having an LOD and 1883 an LOQ.
  • Neat, Mag-Net, Seer, and Olink demonstrated good reproducibility, while PreOmics and Acid showed higher variability. All MS methods exhibited good linearity with spiked C-reactive protein (CRP).
  • All six methods detected differentially abundant proteins between healthy and NSCLC samples, but significant discrepancies were observed in the specific proteins identified.

Conclusions:

  • Seer Proteograph XT and Olink Explore HT offer superior proteomic depth and quantification capabilities among the evaluated methods.
  • While all tested technologies can detect differential protein abundance in NSCLC, method-specific biases exist, impacting the identification of significant biomarkers.
  • The choice of plasma proteomics technology significantly influences the outcomes of proteomic studies, particularly in biomarker discovery for diseases like NSCLC.