Figures Abstract Fast and quantitative analysis of animal phenotypes is one of the major challenges of current biology. Here we report the WormGender open-source software, which is designed for accurate quantification of sex ratio in Caenorhabditis elegans. The software functions include, i automatic recognition and counting of adult hermaphrodites and males, ii a manual inspection feature that enables manual correction of errors, and iii flexibility to use new training images to optimize the software for different imaging conditions.
We evaluated the performance of our software by comparing manual and automated assessment of sex ratio. Our data showed that the WormGender software provided overall accurate sex ratio measurements. We further demonstrated the usage of WormGender by quantifying the high incidence of male him phenotype in 27 mutant strains. Mutants of nine genes brc-1, C30G The source code is freely available together with a user manual and sample data at http: The source code and sample data are also available at http: July 13, ; Accepted: September 16, ; Published: September 30, Copyright: This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: Data are available at the open-access website http: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The authors have declared that no competing interests exist. Introduction One major challenge in modern biology is automation for high-throughput data acquisition and analysis. Caenorhabditis elegans is one of the model organisms widely used in genetic, developmental and neurobiological studies [ 1 ].
Most early studies relied on manual observation for phenotyping, thus limiting their scopes to qualitative and simple phenotypes [ 4 , 5 , 6 , 7 , 8 ].
Various imagining systems have been developed to enable fast acquisition and quantitative analysis of multiple C. One of the most comprehensive C. One of the C. Males are generally very rare in wild-type C.
They naturally arise as a result of sporadic chromosome-nondisjunction events during meiosis, and occur at a frequency of less than one male per hermaphrodites under standard laboratory conditions [ 21 ]. There are many known mutations causing the phenotype of high incidence of males him. They usually arise due to faulty chromosome segregation, sex determination, or dosage compensation [ 21 , 22 ]. Mutations leading to the him phenotype are frequently associated with genes whose human orthologs play a role in important processes such as cancer development [ 23 ].
Therefore, it is crucial to study sex ratio in C. However, there is no automatic phenotyping system for such an important phenotype. Here we present WormGender, an open-source software system designed for accurate and fast quantification of the him phenotype in C. We verified the performance of this software by comparing manual and automated counts of males and hermaphrodites, demonstrating that WormGender can provide accurate estimates of male percentage in a population.
WormGender can be downloaded from http: Methods Animals The following strains were used: Image acquisition For image acquisition, synchronized adult animals were transferred onto 6-well scanning plates modified NGM plates that do not contain peptone or cholesterol using the following method.
The liquid with animals was transferred to glass test tubes using Pasteur pipettes. The glass test tubes were placed on ice to let the animals settle at the bottom. Most of the liquid was then aspirated and the remaining liquid with worms was dropped with Pasteur pipettes onto scanning plates. The scanning plates were left air dry without lids for about 30 minutes. Image processing and analysis A large assembled image is created for each worm plate by stitching multiple tiled photos.
The assembled image is binarized by adaptive local thresholding [ 25 ]. Region extraction is then applied to detect worm objects Fig 1. Any invalid objects that are not worms are excluded from further image analysis. For example, tiny objects that are smaller than typical worms are screened out based on the bounding box size and area.
To measure worm length, a skeleton curve is created from the binary worm image. Worm thickness is obtained by dividing the worm area in pixel counts by the length of the skeleton curve in pixel counts. In addition to length and thickness, WormGender analyzes another two shape parameters, R1 and R2 Fig 1 , which are defined as the ratio of worm diameters D1 and D2 at two specific locations X1 and X2 on the skeleton curve at each end of the worm.
X1 and X2 are empirically determined. R2 is designated to be the larger value. R1 and R2 are used to detect the differences in tail shape between genders.