Description and classification of bivalve mollusks hemocytes: a computational approach

Abstract

The fractal formalism in combination with linear image analysis enables statistically significant description and classification of “irregular” (in terms of Euclidean geometry) shapes, such as, outlines of in vitro flattened cells. We developed an optimal model for classifying bivalve Spisula sachalinensis and Callista brevisiphonata immune cells, based on evaluating their linear and non-linear morphological features: size characteristics (area, perimeter), various parameters of cell bounding circle, convex hull, cell symmetry, roundness, and a number of fractal dimensions and lacunarities evaluating the spatial complexity of cells. Proposed classification model is based on Ward’s clustering method, loaded with highest multimodality index factors. This classification scheme groups cells into three morphological types, which can be distinguished both visually and by several linear and quasi-fractal parameters.

Bivalve Haemocyte Subpopulations: A Review

“…Recently, up to fourteen types of granules were identified in the clam Ruditapes philippinarum granulocytes (75) and up to twelve haemocyte subpopulations were identified in the oyster Crassostrea hongkongensis, the latter based on transcriptomic profile of single cell RNA-seq data (182). One of the most recent morphology classification of bivalve haemocytes was made with a new computational approach that combines fractal formalism with linear methods of image analysis (183,184); however, results are not easily comparable with classic haemocyte classification. The relative abundance of each cell type in the haemolymph of bivalve molluscs is variable, being affected by seasonal changes, temperature, size, sex, maturity, food availability and inter-individual variability (43,70,(185)(186)(187)(188)(189)(190).…”