Identification of Novel Microcystins Using High-Resolution MS and MSn with Python Code

Environ Sci Technol. 2022 Feb 1;56(3):1652-1663. doi: 10.1021/acs.est.1c04296. Epub 2022 Jan 12.


Cyanotoxins called microcystins (MCs) are highly toxic and can be present in drinking water sources. Determining the structure of MCs is paramount because of its effect on toxicity. Though over 300 MC congeners have been discovered, many remain unidentified. Herein, a method is described for the putative identification of MCs using liquid chromatography (LC) coupled with high-resolution (HR) Orbitrap mass spectrometry (MS) and a new bottom-up sequencing strategy. Maumee River water samples were collected during a harmful algal bloom and analyzed by LC-MS with simultaneous HRMS and MS/MS. Unidentified ions with characteristic MC fragments (135 and 213 m/z) were recognized as possible novel MC congeners. An innovative workflow was developed for the putative identification of these ions. Python code was written to generate the potential structures of unidentified MCs and to assign ions after the fragmentation for structural confirmation. The workflow enabled the putative identification of eight previously reported MCs for which standards are not available and two newly discovered congeners, MC-HarR and MC-E(OMe)R.

Keywords: MSn; Python software; high-resolution mass spectrometry; microcystins; structural identification.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Chromatography, Liquid
  • Fresh Water
  • Harmful Algal Bloom
  • Microcystins* / analysis
  • Tandem Mass Spectrometry*


  • Microcystins