4.4 Article

Utility of Atmospheric-Pressure Chemical Ionization and Photoionization Mass Spectrometry in Bottom-Up Proteomics

期刊

SEPARATIONS
卷 9, 期 2, 页码 -

出版社

MDPI
DOI: 10.3390/separations9020042

关键词

chemical ionization; peptides; photoionization; proteomics

资金

  1. Czech Science Foundation [20-09126S]

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This study systematically investigates the ionization efficiencies of peptides in ESI, APCI, and APPI and their applicability in proteomics. The results show that gas-phase ionization techniques, particularly APPI, are interesting alternatives for detecting peptides and providing complementary data in proteomics.
In a typical bottom-up proteomics workflow, proteins are enzymatically cleaved, and the resulting peptides are analyzed by HPLC with electrospray ionization (ESI) tandem mass spectrometry. This approach is practical and widely applied. It has, however, limitations mostly related to less efficient or even inefficient ionization of some peptides in ESI sources. Gas-phase ionization methods like atmospheric-pressure chemical ionization (APCI) or atmospheric-pressure photoionization (APPI) offer alternative ways of detecting various analytes. This work is a systematic study of the ionization efficiencies of peptides in ESI, APCI, and APPI and the applicability of the mentioned ionizations in proteomics. A set of peptide standards and bovine serum albumin digests were examined using a high-resolution mass spectrometer coupled to an ultra HPLC system. Since the ionization efficiency in APCI and APPI depends strongly on experimental conditions, the ion source settings and mobile phase compositions were optimized for each ionization technique. As expected, tryptic peptides were best detected using ESI. The numbers of chymotrypsin peptides successfully detected by ESI, APPI, and APCI were comparable. In the case of Glu-C digest, APPI detected the highest number of peptides. The results suggest that gas-phase ionization techniques, particularly APPI, are an interesting alternative for detecting peptides and delivering complementary data in proteomics.

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