Using clustering algorithms for identification of fish oocyte cohorts based on the characteristics of cytoplasmic structures

Abstract

In batch spawning fish, secondary growth oocytes (SGO) are recruited and spawned in successive cohorts, and multiple cohorts co-occur in spawning-capable females. So far, histological features such as the prevalence of cortical alveoli or yolk granules are conservatively used to distinguish oocytes in different developmental stages which do not necessarily correspond to different cohorts. In this way, valuable information about spawning dynamics remains unseen and consequently misleading conclusions might be drawn, especially for species with high spawning rates and increased overlapping among oocyte cohorts. We introduce a new method for grouping oocytes into different cohorts based on the application of the K-means clustering algorithm on the characteristics of cytoplasmic structures, such as the varying size and intensity of cortical alveoli and yolk granules in oocytes of different development. The method allowed the grouping of oocytes without the need of using oocyte diameter, and thus, a crucial histological bias dealing with the cutting angle and the orientation of reference points (e.g. nucleus) has been overcome. Using sardine, Sardina pilchardus, as a case study, the separation of cohorts provided new insight into the ovarian dynamics, indentifying successive recruitment of up to five oocyte cohorts between SGO recruitment and spawning. These results verified previous histological indications of the number of cohorts in sardine. Altogether, this method represents an improved tool to study species with complex ovarian dynamics.