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 Experiment Videos

Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries.

Lindsay K Pino1, Seth C Just2, Michael J MacCoss3

  • 1Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Molecular & Cellular Proteomics : MCP
|April 22, 2020
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Interlaboratory Comparison of a Glucagon and Oxyntomodulin Immuno-LC-MS/MS Assay: Implications for Diabetes Research.

Clinical chemistry·2026
Same author

Spontaneous Isomerization of Tau is Most Prevalent in Alzheimer's Disease.

NeuroMarkers·2026
Same author

Revisiting resonance-excitation collision-induced dissociation for data-independent acquisition.

bioRxiv : the preprint server for biology·2026
Same author

ToxBase: A Multidimensional ToxCast Reference Database for High-Throughput Human Exposome Analysis.

Environmental science & technology·2026
Same author

Prioritizing peptides for targeted mass spectrometry experiments using deep learning.

bioRxiv : the preprint server for biology·2026
Same author

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

bioRxiv : the preprint server for biology·2026

This study presents a novel data-independent acquisition (DIA) proteomics workflow that bypasses the need for data-dependent acquisition (DDA) libraries. This method enables efficient quantitative proteomics, even with challenging samples or limited yields.

Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Quantitative Biology

Background:

  • Data-independent acquisition (DIA) is a powerful technique for quantitative proteomics.
  • Traditional DIA methods rely on extensive, sample-specific spectral libraries generated by data-dependent acquisition (DDA), which can be impractical for non-human samples, splice variants, or low-yield samples.
  • The development of DDA-based libraries is a significant bottleneck in many proteomic studies.

Purpose of the Study:

  • To demonstrate a DIA proteomics workflow that does not require DDA-generated spectrum libraries.
  • To provide a method for generating DIA-only chromatogram libraries using gas-phase fractionation (GPF).
  • To offer best practices for DIA data acquisition on Orbitrap instruments and discuss library-free analysis strategies.

Main Methods:

Keywords:
DIAData evaluationdata independent acquisitionlabel-free quantificationmass spectrometryprotein identificationquantification

Related Experiment Videos

  • Acquisition, queuing, and validation of DIA data without prior DDA libraries.
  • Generation of DIA-only chromatogram libraries utilizing gas-phase fractionation (GPF).
  • Development of specific DIA acquisition strategies for Orbitrap mass spectrometers.
  • Exploration of various library-free DIA data analysis techniques.

Main Results:

  • Successful acquisition and validation of DIA data without relying on DDA libraries.
  • Efficient generation of DIA-only chromatogram libraries using the proposed GPF workflow.
  • Insights into optimizing DIA acquisition on Orbitrap instruments, differentiating from time-of-flight approaches.
  • Demonstration of feasible library-free analysis methods for DIA data.

Conclusions:

  • A robust DIA proteomics workflow is presented, eliminating the need for DDA-based spectrum libraries.
  • This approach significantly enhances the applicability of quantitative DIA proteomics to diverse and challenging biological samples.
  • The study provides practical guidance for DIA data acquisition and analysis, broadening the utility of DIA mass spectrometry.