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Electrospray Ionization (ESI) Mass Spectrometry01:12

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Dynamic Additive Scanning for Precise Control in Electrospray Ionization Mass Spectrometry.

I-Ting Wu1, Decibel P Elpa1, Hsien-Ning Chien1

  • 1Department of Chemistry, National Tsing Hua University 101, Section 2, Kuang-Fu Rd., Hsinchu 300044, Taiwan.

ACS Measurement Science Au
|April 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a dynamic scanning system for electrospray ionization mass spectrometry (ESI-MS) optimization. It allows for precise tuning of additives to enhance signals for small molecules and proteins, improving analytical information.

Keywords:
automationcharge stateelectrospray ionizationmass spectrometryoptimizationproteinscan

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Area of Science:

  • Analytical Chemistry
  • Mass Spectrometry
  • Biochemistry

Background:

  • Electrospray ionization mass spectrometry (ESI-MS) methods require optimization of additives like acids, bases, and solvents.
  • Current optimization relies on iterative methods or literature, often using a single condition.
  • Varying additive concentrations can yield different analytical insights for diverse analytes.

Purpose of the Study:

  • To develop a precise MS optimization system for dynamic scanning of acid-base and additive concentrations in ESI-MS.
  • To demonstrate enhanced signal intensity for low-molecular-weight analytes.
  • To show manipulation of protein charge state distributions (CSDs) for structural insights.

Main Methods:

  • A novel MS optimization system was developed using off-the-shelf components and Python code.
  • The system enables dynamic scanning of acid-base and additive concentrations during ESI-MS.
  • Online additive scans were coupled with ESI-MS to streamline optimization.

    • Main Results:

    • Significant signal enhancement was observed for amino acids, glutathione, and phospholipids.
    • Enhancement factors reached up to ~44.7 for glutathione.
    • Protein CSDs were manipulated, with increased organic solvent concentrations shifting CSDs to lower charge states.

    Conclusions:

    • The developed system precisely optimizes ESI-MS conditions, enhancing signal intensity for various analytes.
    • Dynamic scanning of additives provides valuable information on protein structure vulnerability.
    • This approach streamlines ESI-MS method development, reducing the need for sequential analyses.