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Identification of 14 stages of Drosophila egg chamber. Characteristics of different egg chamber stages. 

Identification of 14 stages of Drosophila egg chamber. Characteristics of different egg chamber stages. 

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The Drosophila egg chamber, whose development is divided into 14 stages, is a well-established model for developmental biology. However, visual stage determination can be a tedious, subjective and time-consuming task prone to errors. Our study presents an objective, reliable and repeatable automated method for quantifying cell features and classify...

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... Drosophila have two ovaries, each containing around 16 ovarioles. The ovariole is a string of 6 or 7 sequentially developing egg chambers. Each Drosophila egg chamber is a basic developmental unit of oogenesis. Furthermore, the egg chamber comprises 16 germ-line cells, one oocyte and 15 nurse cells, which are surrounded by a thin layer of somatic follicle cells 1 . Research on the egg chamber contributes significantly to scientific knowledge. The egg chamber follicle cell epithelium has been a well-established model system to study cell cycle regulation, cell differentiation 2–16 , cell polarity 17–20 , endocytosis 21–23 , exocytosis 24 , morphogenesis 19,25–28 , and cancer metastasis 29,30 . Germline-soma interactions provide developmental cues, especially for anterior-posterior and dorsal-ventral patterning 31–36 . More interestingly, egg chambers have distinctively separated germline and soma, which provides a unique and reliable system to study germline-soma ligand-receptor interaction 1,2,31,37 . The development of the egg chamber during Drosophila oogenesis is divided into 14 stages 1 . Visual identifica- tion of different stages requires significant training, including a thorough understanding of egg chamber growth progression. Even skilled scientists need to cautiously determine the stages based on general features ( Fig. 1) (Fig. 1) 1 and frequently refer to neighboring egg chambers to doubly confirm the determination of stages. However, in a single confocal image of egg chambers, there might be no neighboring egg chambers to help confirm stages. Moreover, even an expert sometimes has difficulty assigning correct stages to inter-stage transitional egg chambers. To address these problems, and to reduce the bias caused by human perceptual variation, an objective, reliable and standardized egg chamber stage identification method is greatly needed. DAPI (4 ′ ,6-diamidino-2-phenylindole) is widely used as a cell nucleus staining dye. In this study, we developed a toolbox that applies understanding of egg chamber morphology 1 extracted automatically from DAPI staining to determine egg chamber stages. We collected 172 confocal microscopy images from different stages to train and evaluate our automatic stage identification method. Egg chamber areas of DAPI images proved to be a stable feature for stage differentiation, while oocyte size played a critical role for late stage differentiation. Integrated with machine learning, our approach provides potential to discover hidden developmental mechanisms, and brings the Drosophila community tools for standardizing stage identification and objectively comparing results obtained from different labs. In this paper, we applied our method to successfully confirm the occasional appearance of Broad expression as early as stage 5, and also unambiguously demonstrated that egg chambers with germline Delta mutation entered midoogenesis, even though the follicle cells did not show the appropriate biomarker, Br 5 . Furthermore, we provided support for the previous understanding that follicle cell mitosis ceases at stage 6 1,38 . Our results clearly showed mitosis occurs in follicle cells of stage-5 egg chambers, while not in those of stage-6 egg chambers. Our findings clarified our understanding that stage 6 should be considered as an entering stage of endocycle, and stage 7 completes endocycle entry 2–6,9,10 . The collected DAPI images should be single cross sections in the middle plane, and represents the largest area size. Before image processing, particular stages were initially assigned to egg chambers by skilled biologists in our team based on standard descriptive guidance 1 (Fig. 1). (Fig. 1). The morphological features, including egg chamber size, ratio, orientation, oocyte size, follicle cell dis- tribution, blob-like chromosomes in polytene nuclei at stage 4, centripetal cell migration at stage 10B, were then extracted to train our machine learning method. Egg chamber size. The development of an organism is often accompanied by increase in size, and this is also the case in Drosophila egg chamber stage development. Since egg chamber size plays a critical role in stage determination, we developed an algorithm for automated egg chamber size measurement, whose workflow is described as follows: Image thresholding: Original images (Fig. 2A) (Fig. 2A) were read into Matlab, and the measurement ( μ m) of each pixel was recorded. Otsu’s method 39 was used to convert the original image into a binary image and initialize the seg- mentation process. Briefly, Otsu’s method applies a threshold that optimizes the separation of image foreground and background. This algorithm has been widely used in image segmentation pre-processing steps 40 . Conditions must be met for Otsu’s method to be effective:1) minimum variability in gray levels of foreground pixels and background pixels, respectively; 2) maximum gray level contrast between background and foreground pixels. In our study, the DAPI-stained images satisfy both conditions, as foreground pixels had much lighter color than the background pixels. We set the threshold at 20% of Otsu’s threshold to include all desired foreground objects. This percentage value was learned through experimentation with data. The Matlab function, graythresh, was used to compute Otsu’s threshold. Another Matlab function, im2bw, applied our threshold and converted the grayscale image to a binary image. The output binary image replaced all pixel values in the input image with luminance greater than the threshold with the value 1 (white, foreground, Fig. 2B) Fig. 2B) and the remaining pixels with the value 0 (black, background, Fig. 2B). Fig. 2B). Largest component extraction: Digital images are prone to noise that may originate from different parts of the image acquisition process. “Noise” is always a relative term and means “unwanted signals”. In our analysis of the binary image obtained in the previous step, we considered any foreground image element other than the egg chamber of interest as noise, including portions of other egg chambers and scattered spotty pixels (Fig. 2A). (Fig. 2A). To detect these noisy pixels and mitigate their negative effects on our analyses, we applied an adjusted average filter, which smoothed the image by replacing each pixel value with the ...

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