Data concerning the morphometric parameters of sheep red blood cells (RBCs) obtained using computer-assisted image analysis have not yet been investigated, and there are no data on any analyses of ovine RBC subpopulations based on their morphometric parameters. The aims of this study are to determine the values of RBC haematological and morphometric size and shape parameters, to form groups according to the obtained values of haematological parameters; to determine the differences in RBC morphometric parameters between the formed groups, and to determine RBC subpopulations and their respective proportions in the formed groups. Thirty-six blood samples were collected from the jugular vein of clinically healthy Lika pramenka sheep, aged between 2 and 5 years. Haematological parameters including haemoglobin (HGB), haematocrit (HTC), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), and RBC distribution width were analysed using a haematology analyser. Haematological parameters were categorized into two groups: those with lower values or values below the physiological range (Groups 1) and groups with higher values or values above the physiological range (Groups 2). Morphometric parameters of RBCs were determined from stained blood smears using SFORM, a computer-assisted program. Significantly higher values of RBC area, outline, convex, minimal and maximal radius, as well as length and breadth were established in Groups 2 compared to Groups 1 of HGB, HCT, MCV, MCH, and MCHC, respectively. Based on the morphometric parameters of RBCs, three RBC subpopulations were obtained using principal component and cluster analysis: ES 1—the smallest and most elongated RBCs, ES 2—the biggest and most rounded RBCs, and ES 3—average size and shape RBCs. Significantly higher proportions of ES 2 and ES 3 subpopulations, as well as a significantly lower proportion of ES 1 subpopulation, were established in Groups 2 compared to Groups 1 of HGB, HTC, MCV, and MCH, respectively. It can be concluded that ovine RBC subpopulations, based on their morphometric parameters, can be obtained by using computer-assisted image analysis of RBC morphometry and multivariate statistical methods, including principal component and cluster analysis. RBC morphometry, including classification into subpopulations, could serve as a basis for future possibilities in the diagnostic interpretation of anaemic syndromes in veterinary medicine, especially in normocytic, macrocytic, and microcytic anaemias in sheep.
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