Automated quantitative analysis of 3D morphology and mean corpuscular hemoglobin in human red blood cells stored in different periods

Abstract

Quantitative phase (QP) images of red blood cells (RBCs), which are obtained by off-axis digital holographic microscopy, can provide quantitative information about three-dimensional (3D) morphology of human RBCs and the characteristic properties such as mean corpuscular hemoglobin (MCH) and MCH surface density (MCHSD). In this paper, we investigate modifications of the 3D morphology and MCH in RBCs induced by the period of storage time for the purpose of classification of RBCs with different periods of storage by using off-axis digital holographic microscopy. The classification of RBCs based on the duration of storage is highly relevant because a long storage of blood before transfusion may alter the functionality of RBCs and, therefore, cause complications in patients. To analyze any changes in the 3D morphology and MCH of RBCs due to storage, we use data sets from RBC samples stored for 8, 13, 16, 23, 27, 30, 34, 37, 40, 47, and 57 days, respectively. The data sets consist of more than 3,300 blood cells in eleven classes, with more than 300 blood cells per class. The classes indicate the storage period of RBCs and are listed in chronological order. Using the RBCs donated by healthy persons, the off-axis digital holographic microscopy reconstructs several quantitative phase images of RBC samples stored for eleven different periods. We employ marker-controlled watershed transform to remove the background in the RBC quantitative phase images obtained by the off-axis digital holographic microscopy. More than 300 single RBCs are extracted from the segmented quantitative phase images for each class. Such a large number of RBC samples enable us to obtain statistical distributions of the characteristic properties of RBCs after a specific period of storage. Experimental results show that the 3D morphology of the RBCs, in contrast to MCH, is essentially related to the aging of the RBCs.

Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes

“…[13][14][15][16] Many efforts have been made in RBC studies to detect abnormalities in samples before transfusion to the patient to prevent future disorders caused by the malfunction of RBCs. [17][18][19] According to the above discussion, we believe the utilization of clustering techniques can provide us with reasonable results. Therefore, because of the irregularly shaped groups of RBC types distributed in feature space, the density-based clustering method can be effective in this case and for overlapped RBC data.…”

In vitro assays and clinical trials in red blood cell aging: Lost in translation

“…More recently, more detailed RBC analyses came into the game and have pictured out other storage lesions (see Table 1). These studies reported the presence of reversible [18,[27][28][29][30][31] and irreversible modifications [19,[23][24][25][26]32,33] during aging. Moreover, and especially since the 2008 Koch's paper [1], several authors have tried to define a storage limit other than the expiration date.…”

Multiphoton excited hemoglobin fluorescence and third harmonic generation for non-invasive microscopy of stored blood

“…Current protocols call for the destruction of these stored blood components after 42 days, based on guidelines from the Committee for Standardization in Haematology [15]. As RBCs age, they lose the important flexibility and deformability that enables them to squeeze through small capillaries to deliver oxygen to tissue; this capability cannot be regained after the transfusion occurs [7,8].…”

“…Previous optical imaging studies of RBC morphology have required breaching the storage bag; these efforts, like those described above, found irreversible changes to the morphology with increasing storage duration [13][14][15]. Nonlinear optical imaging of RBCs has been accomplished via several different methods including two-photon absorption (TPA) [2,16], TPEF [3,17], and THG [18,19].…”