Functional analysis and classification of phytoplankton based on data from an automated flow cytometer

Area of Science:

• Aquatic Ecology
• Analytical Chemistry
• Biotechnology

Background:

• Flow cytometry (FCM) analyzes phytoplankton in aquatic environments using light scatter and autofluorescence.
• Current FCM data analysis relies on manual gating, which can be subjective and time-consuming.
• A submersible FCM (CytoSub) enables in situ monitoring of phytoplankton dynamics.

Purpose of the Study:

• To develop an objective, non-manual method for discriminating phytoplankton clusters.
• To automate data analysis for large, complex datasets generated by FCM.
• To improve the accuracy of phytoplankton group separation, especially when optical fingerprints overlap.

Main Methods:

• Utilized optical fingerprints (light scatter, autofluorescence) from FCM.
• Developed a partitioning method based on particle optical fingerprints.
• Employed curve shape, length, and area as analytical descriptors.
• Validated the method using simulated data and phytoplankton cultures.

Main Results:

• Achieved objective discrimination of phytoplankton clusters.
• Demonstrated successful application on complex datasets with overlapping optical fingerprints.
• Showed promising results exceeding the accuracy of manual gating.

Conclusions:

• The developed automated method offers greater objectivity and consistency in phytoplankton analysis.
• This approach enhances the ability to accurately identify and quantify phytoplankton groups.
• Automated FCM data analysis is crucial for understanding aquatic microbial dynamics.