Abstract Title

High-throughput microscope counting of cyanobacteria using “cellcount”, a newly developed analysis package in the R programming language

Start Date

23-5-2022 5:45 PM

End Date

23-5-2022 7:00 PM

Abstract

Molecular approaches and novel method validations require the precise enumeration of cyanobacteria to validate cyanobacteria density, typically done via microscopic counts which are considered time consuming and technically challenging. Cell counting software tools, such as ImageJ, can help decrease enumeration time, but may offer little flexibility in software modifications and may incorrectly quantify different morphotypes. Here we provide an overview of the development and uses of the draft package cellcount, from the programming language R. We used previously published code described in Pokrzywinski et al. 2019 as a blueprint for the development of new functions and overall organization. The result is an open-source package capable of being expanded and modified by novice and experienced R users. Here, we analyzed concentrations of several species to demonstrate cellcount versatility and potential limitations. In addition, we compared cellcount against standard enumeration practices and in vivo pigment fluorescence to demonstrate ease of use and rapid analysis while maintaining the same accuracy. With the formation of this high-throughput approach, researchers can utilize cellcount for many applications, such as qPCR standard curve development, the development of biomass standard curves, and validation of emerging quantification techniques.

This document is currently not available here.

Share

COinS
 
May 23rd, 5:45 PM May 23rd, 7:00 PM

High-throughput microscope counting of cyanobacteria using “cellcount”, a newly developed analysis package in the R programming language

Molecular approaches and novel method validations require the precise enumeration of cyanobacteria to validate cyanobacteria density, typically done via microscopic counts which are considered time consuming and technically challenging. Cell counting software tools, such as ImageJ, can help decrease enumeration time, but may offer little flexibility in software modifications and may incorrectly quantify different morphotypes. Here we provide an overview of the development and uses of the draft package cellcount, from the programming language R. We used previously published code described in Pokrzywinski et al. 2019 as a blueprint for the development of new functions and overall organization. The result is an open-source package capable of being expanded and modified by novice and experienced R users. Here, we analyzed concentrations of several species to demonstrate cellcount versatility and potential limitations. In addition, we compared cellcount against standard enumeration practices and in vivo pigment fluorescence to demonstrate ease of use and rapid analysis while maintaining the same accuracy. With the formation of this high-throughput approach, researchers can utilize cellcount for many applications, such as qPCR standard curve development, the development of biomass standard curves, and validation of emerging quantification techniques.