Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.3390/cells12121559
Lizenz creative commons licence
Titel (primär) Development of an automated online flow cytometry method to quantify cell density and fingerprint bacterial communities
Autor López-Gálvez, J.; Schiessl, K.; Besmer, M.D.; Bruckmann, C.; Harms, H.; Müller, S.
Quelle Cells
Erscheinungsjahr 2023
Department UMB
Band/Volume 12
Heft 12
Seite von art. 1559
Sprache englisch
Topic T7 Bioeconomy
Keywords Automated online flow cytometry; fingerprinting; cell density determination; pattern analysis; process control
Abstract Cell density is an important factor in all microbiome research, where interactions are of interest. It is also the most important parameter for the operation and control of most biotechnological processes. In the past, cell density determination was often performed offline and manually, resulting in a delay between sampling and immediate data processing, preventing quick action. While there are now some online methods for rapid and automated cell density determination, they are unable to distinguish between the different cell types in bacterial communities. To address this gap, an online automated flow cytometry procedure is proposed for real-time high-resolution analysis of bacterial communities. On the one hand, it allows for the online automated calculation of cell concentrations and, on the other, for the differentiation between different cell subsets of a bacterial community. To achieve this, the OC-300 automation device (onCyt Microbiology, Zürich, Switzerland) was coupled with the flow cytometer CytoFLEX (Beckman Coulter, Brea, USA). The OC-300 performs the automatic sampling, dilution, fixation and 4′,6-diamidino-2-phenylindole (DAPI) staining of a bacterial sample before sending it to the CytoFLEX for measurement. It is demonstrated that this method can reproducibly measure both cell density and fingerprint-like patterns of bacterial communities, generating suitable data for powerful automated data analysis and interpretation pipelines. In particular, the automated, high-resolution partitioning of clustered data into cell subsets opens up the possibility of correlation analysis to identify the operational or abiotic/biotic causes of community disturbances or state changes, which can influence the interaction potential of organisms in microbiomes or even affect the performance of individual organisms.
dauerhafte UFZ-Verlinkung
López-Gálvez, J., Schiessl, K., Besmer, M.D., Bruckmann, C., Harms, H., Müller, S. (2023):
Development of an automated online flow cytometry method to quantify cell density and fingerprint bacterial communities
Cells 12 (12), art. 1559 10.3390/cells12121559