TY - JOUR

T1 - Image analysis-based quantification of fungal sporulation by automatic conidia counting and gray value correlation

AU - Muskat, Linda C.

AU - Kerkhoff, Yannic

AU - Humbert, Pascal

AU - Nattkemper, Tim W.

AU - Eilenberg, Jørgen

AU - Patel, Anant V.

PY - 2021

Y1 - 2021

N2 - The present work describes a new computer-assisted image analysis method for the rapid, simple, objective and reproducible quantification of actively discharged fungal spores which can serve as a manual for laboratories working in this context. The method can be used with conventional laboratory equipment by using bright field microscopes, standard scanners and the open-source software ImageJ. Compared to other conidia quantification methods by computer-assisted image analysis, the presented method bears a higher potential to be applied for large-scale sample quantities. The key to make quantification faster is the calculation of the linear relationship between the gray value and the automatically counted number of conidia that has only to be performed once in the beginning of analysis. Afterwards, the gray value is used as single parameter for quantification. The fast, easy and objective determination of sporulation capacity enables facilitated quality control of fungal formulations designed for biological pest control. • Rapid, simple, objective and reproducible quantification of fungal sporulation suitable for large-scale sample quantities. • Requires conventional laboratory equipment and open-source software without technical or computational expertise. • The number of automatically counted conidia can be correlated with the gray value and after initial calculation of a linear fit, the gray value can be applied as single quantification parameter.

AB - The present work describes a new computer-assisted image analysis method for the rapid, simple, objective and reproducible quantification of actively discharged fungal spores which can serve as a manual for laboratories working in this context. The method can be used with conventional laboratory equipment by using bright field microscopes, standard scanners and the open-source software ImageJ. Compared to other conidia quantification methods by computer-assisted image analysis, the presented method bears a higher potential to be applied for large-scale sample quantities. The key to make quantification faster is the calculation of the linear relationship between the gray value and the automatically counted number of conidia that has only to be performed once in the beginning of analysis. Afterwards, the gray value is used as single parameter for quantification. The fast, easy and objective determination of sporulation capacity enables facilitated quality control of fungal formulations designed for biological pest control. • Rapid, simple, objective and reproducible quantification of fungal sporulation suitable for large-scale sample quantities. • Requires conventional laboratory equipment and open-source software without technical or computational expertise. • The number of automatically counted conidia can be correlated with the gray value and after initial calculation of a linear fit, the gray value can be applied as single quantification parameter.

KW - (Semi-)Automatic conidia counting

KW - Computer-assisted sporulation quantification

KW - Entomopathogenic fungi

KW - Quantification of fungal sporulation by (semi)-automatic counting of conidia and gray value correlation

U2 - 10.1016/j.mex.2021.101218

DO - 10.1016/j.mex.2021.101218

M3 - Journal article

C2 - 34434741

AN - SCOPUS:85099437963

VL - 8

JO - MethodsX

JF - MethodsX

SN - 2215-0161

M1 - 101218

ER -