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Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

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Published on: June 3, 2015

Facilitated gate setting by sequential dot plot scanning.

Susanne Günther¹, Susann Müller¹

¹Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, 04318, Germany.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|March 11, 2015

Summary

This summary is machine-generated.

Analyzing microbial communities with flow cytometry is challenging due to limited parameters. This study introduces sequential dot-plot scanning to improve data evaluation and identify rare subcommunities.

Keywords:

2D-dot plot evaluation Key terms: data analysis gate setting microbial communities

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Area of Science:

Microbiology
Biotechnology
Analytical Chemistry

Background:

Flow cytometry is a powerful tool for analyzing microbial communities.
Studying complex microbial communities often involves thousands of unknown organisms.
Traditional flow cytometry data analysis can be subjective and inaccurate due to limited parameters and overlapping events.

Purpose of the Study:

To develop a more objective and accurate method for evaluating flow cytometry data from microbial communities.
To facilitate the detection of rare subcommunities within complex microbial samples.

Main Methods:

Utilizing a single fluorescent parameter in conjunction with cell scatter characteristics for flow cytometry analysis.
Implementing a novel procedure involving sequential dot-plot scanning for facilitated gate setting.
Analyzing high numbers of cells to ensure comprehensive representation of all subcommunities.

Main Results:

The sequential dot-plot scanning procedure enhances the evaluation of flow cytometry data.
The method improves the ability to discriminate between different microbial subcommunities.
Rare subcommunities, previously difficult to detect, can be identified more reliably.

Conclusions:

Sequential dot-plot scanning offers a robust approach for analyzing complex microbial communities using flow cytometry.
This technique addresses limitations in data discrimination and subjective evaluation.
The procedure facilitates a more thorough understanding of microbial community composition and diversity.