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Data-Driven Optimization of DIA Mass Spectrometry by DO-MS.

Georg Wallmann1, Andrew Leduc1, Nikolai Slavov1,2

  • 1Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA 02115, USA.

Biorxiv : the Preprint Server for Biology
|February 13, 2023
PubMed
Summary
This summary is machine-generated.

We present DO-MS v2.0, a new tool for optimizing mass spectrometry methods, especially for data-independent acquisition (DIA) and single-cell proteomics. This framework enhances protein quantification accuracy and data quality control for large-scale proteomics studies.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Mass spectrometry (MS) is crucial for protein quantification, but requires optimized data acquisition and quality control for large datasets.
  • Data-independent acquisition (DIA) offers high throughput and sensitivity, necessitating efficient methods for data analysis and optimization.

Approach:

  • Developed DO-MS app v2.0, a data-driven framework for optimizing mass spectrometry methods, specifically for DIA.
  • The framework supports label-free and multiplexed DIA (plexDIA), with features tailored for single-cell proteomics.
  • Incorporates optimization of duty cycle, peptide separation, survey scans, and quality control for plexDIA data.

Key Points:

  • DO-MS v2.0 facilitates optimization of critical parameters in DIA-MS experiments.
  • Enables efficient quality control for complex single-cell proteomics datasets.
  • Provides interactive data visualization and generates shareable, publication-quality reports.

Conclusions:

  • DO-MS v2.0 enhances the potential of MS for accurate protein quantification.
  • The tool supports advanced DIA strategies, including single-cell and multiplexed approaches.
  • Improves data analysis workflows and reproducibility in proteomics research.