Image Analysis Protocol for Detecting and Counting Viable and Inviable Pollen Grains

Abstract

Estimating total pollen number and viability is labor intensive and time consuming. Nevertheless the information is crucial for a range of plant biologists from plant breeders to evolutionary biologists. Viability is determined by dye staining and counting colored (viable) and transparent (inviable) grains under a compound microscope. Existing protocols have been standardized to speed up total pollen counts but their success in determining viability is rather limited because they do not incorporate staining techniques. Some of the published protocols that determine viability do so based on statistical methods but focus on just one parameter such as pollen diameter requiring manual standardization to generate two distinct size distributions for viable and inviable pollen. We demonstrate a digital image processing protocol that can count viable and inviable pollen by distinguishing colored and transparent objects in the image space, saving valuable labor and time. Using pollen grains from two plants, Collinsia heterophylla and Brassica napus, we show that differences between viable and inviable pollen are best described by a complex measure, the grain shape. By measuring several parameters such as area, length, width, circularity and elongation, while retaining all the advantages of traditional staining process, our procedure increases the accuracy of viability estimates. The only drawback of our protocol is that it uses the NIS elements a software specific to Nikon instruments.

ESTIMATING NUMBERS OF OIL PALM (Elaeis guineensis) POLLEN GRAINS USING IMAGE ANALYSIS AND PROCESSING

“…The high accuracy of pollen number estimation, in this study was also reported in other studies with different plant families that have different pollen size and shape (Mudd and Arathi, 2012;MacInnis and Forrest, 2017). This flexibility may be beneficial for further application on various oil palm cultivars.…”

“…Pleura setae present in males weevils provide additional body surface which allowed the smaller debris to contaminate pollen samples (Dhileepan, 1992). Debris might form clusters with pollen which make it difficult for software to delineate individual pollen to give a correct count (Mudd and Arathi, 2012). However, for many biological studies involving counting of small biological particle, a moderate deviation (lower than 20%) is often negligible compared to noise generated by other factors (Geissman, 2013).…”

“…One possibility is to use an image processing tool and analysis program which offer a more accurate, user friendly, and consistent estimation (Bechar et al, 1997;Aronne et al, 2001;Fonseca et al, 2002;Costa and Yang, 2009;Geissman et al, 2013;Mudd and Arathi, 2012). This tool is equipped with a particular computer software to scan pollen images, which previously captured as raw images by a camera.…”

Removing lateral chromatic aberration in bright field optical microscopy

“…This factor has values that range from 0 (null roundness) to 1 (high roundness) [31]. For pollen grains, it is expected that cf > 0.6 [32,33]. Analysis of the dark pink cluster in our pollen sample resulted in cf = 0.55 ± 0.16 (mean ± standard deviation, mode = 0.62) for the original image, see Figs.…”

Quantitative methods in microscopy to assess pollen viability in different plant taxa

“…Consequently, due to the considerable efforts required, in general, only a few hundreds of grains per replicate are counted at best. Image analysis software was used for the automation of the tests based on Alexander’s and aniline blue staining (Mudd and Arathi 2012 ; Tello et al 2018 ). Nevertheless, those stains are suited for identifying aborted or sterile grains and not for studying the state of well-developed pollen (Alexander, 1969 ; Khatun and Flowers 1995 ).…”