(347) Comparing Approaches to Transmitted Light Image Analysis in Mouse Oocyte Masses

Sophia Meytin1; Gisela Cairo2; Olha Kholod1; Soni Lacefield2; Brittany A. Goods1

1. Thayer School of Engineering, Dartmouth College, Hanover, USA 

2. Geisel School of Medicine, Dartmouth College, Hanover, USA

Abstract Text: The integration of microscopy-generated images with other forms of data is a staple of biological research. This project explores a previously generated dataset of transmitted light microscopy images curated to quantify the impact of a knockout Moloney sarcoma oncogene (MOS) mutation in ovarian mouse models. MOS is highly expressed in oocytes undergoing meiotic division, and encodes a serine/threonine kinase protein which activates the MAP kinase cascade important for metaphase II arrest before undergoing selective proteolysis. However, absence of MOS expression has been noted to coincide with loss of meiotic arrest, which impedes fertilization, as well as the growth of germline tumors. Transmitted light images were generated on oocytes cultured from MOS-/- and wildtype mice, as well as wildtype oocytes activated with strontium chloride. Oocytes were cultured to develop masses and then imaged with transmitted light microscopy. The goal of this project was to perform comparative analyses between three analytical softwares—Cellpose, CellProfiler, and ImageJ—to determine the relative efficacy of these approaches in identifying mass boundaries within cells, allowing for the calculation of their size and abundance across experimental conditions. This workflow reveals that ImageJ enables the most accurate identification of mass boundaries regardless of image preprocessing or quality. Cellpose is able to identify the boundaries of masses with similar accuracy to ImageJ in cells where masses and nuclei are of comparable size, but struggles to separate differentially-sized masses from noise and introduces some artifact measurements at the end of the analysis pipeline. CellProfiler analysis is performed both through two modified pre-generated pipelines and through a custom pipeline; all three pipelines are able to finely preprocess and segment images based on value but perform poorly in the presence of stark value gradients, leading to the under- and over-segmentation of mass boundaries depending on gradient presence and location. The necessity of pre-generating data on mass diameter to run CellProfiler decreases the accessibility of this approach due to the inherently large variation in mass size within this dataset. This work has found that ImageJ is the most easily adaptable approach to analyzing transmitted light image datasets with high feature variability, despite slightly higher time costs. This work has broader implications for the analysis of highly heterogeneous oocyte mass transmitted light images, and suggests that refinement of ImageJ analysis approaches is worth the time investment.