Unbiased Morphometric Assessment of Red Blood Cell Storage Lesion in the Presence of Shear-Induced Stomatocytes

Abstract

Introduction: Red blood cells (RBCs) undergo progressive biochemical and morphological changes during storage, collectively called storage lesion. The quality of red cell concentrates (RCCs) is typically assessed by quantifying hemolysis. An assessment of morphological changes, associated with low quality RBCs, could give an additional indication of the safety and efficacy of the concentrates. The current standard for determining morphological changes is a manual, laborious, and subjectively biased microscopic process that limits the number of cells that can be examined. When using alternative methods like flow cells, flow and shear-induced morphologies affecting especially stomatocyte morphologies must be taken into account. We already established an automated flow morphometric RBC analysis system as an alternative to manual microscopic evaluation. The goal of the present work is to obtain a robust, automated, morphology-related signal (lesion index) quantifying RBC storage lesion in a laminar flow channel under conditions similar to stasis that is not affected by shear-induced reversible morphology changes. Methods: We use a convolutional neural network (CNN) for high throughput classification of RBCs. We analyzed the morphological changes of 5 RCCs over a period of 12 weeks and classified RBC morphologies, including such that are degradation-induced and reversible. We introduce a lesion index to denote the percentage of irreversible spherical morphologies, known to reduce the post-transfusion survival of erythrocytes. We further addressed shear-induced stomatocyte morphologies in laminar flow and whether these affect CNN-based RBC classification. Results: Our flow morphometry system achieves a high-resolution classification comprising nine morphological classes with an excellent overall accuracy of 92% and F1 scores between 84% and 97%. We generate strong evidence that the morphological lesion index can predict the hemolysis level in RCCs during storage. The power of this new classification technique allowed it, for the first time, to detect and measure the lateral concentration gradient of stomatocytes in a conventional flow chamber. Importantly, we show that reversible shear rate-induced morphologies, typical for microfluidic systems, bear no influence on the lesion index. Conclusion: Flow morphometry combined with evaluation by a CNN allows to reliably assess RBC storage lesion and thus concentrate quality. Additionally, this method reduces the need for complex laboratory procedures.