Network Component Analysis Can Identify Potential Axenisation Strategies Circumventing Antibiotic-Use for Phototrophic Eukaryotic Microalgae
Review article, 2026

Axenisation of phototrophic eukaryotic microalgae has been studied for over a century, with antibiotics commonly employed to achieve axenic cultures. However, this approach often yields inconsistent outcomes and contributes to the emergence of antibiotic-resistant microbes. A comprehensive analysis of previous reports on axenisation was necessary to identify alternate workflows tailored to each major microalgal group. Literature from scholarly databases was systematically recovered and network component analysis was performed to identify method-clusters commonly reported for the axenisation of diatoms, dinoflagellates, and green algae. Promising workflows circumventing the use of antibiotics appeared to be filtration ↔ washing ↔ micropicking for diatoms, and micropicking ↔ subculturing ↔ flow cytometry for dinoflagellates. No clear workflow could emerge for green algae although Streak plating ↔ Flowcytometry → Ultrasonication was considered despite these methods appearing in different clusters. Furthermore, the literature suggests that a combination of microscopy (e.g., epifluorescence), cell counting (e.g., agar plating), and sequencing (16S and/or 18S) was essential to confirm the final purity of the mother culture. More systematic and high-quality primary research is required to identify effective workflows for other microalgal divisions and fortify/contrast the ones proposed herein based on network component analysis.

microalgae

eukaryote

axenisation

single culture

phototrophs

Author

A. Iyer

University College Dublin

M. Monissen

RWTH Aachen University

Q. Teo

University College Dublin

Oskar Modin

Chalmers, Architecture and Civil Engineering, Water Environment Technology

R. Halim

University College Dublin

Environmental Microbiology Reports

17582229 (eISSN)

Vol. 18 1 e70290

Subject Categories (SSIF 2025)

Molecular Biology

Bioinformatics (Computational Biology)

Ecology

DOI

10.1111/1758-2229.70290

PubMed

41644132

More information

Latest update

2/16/2026