Measuring cell surface area and deformability of individual human red blood cells over blood storage using quantitative phase imaging

Park, Hyun Joo; Lee, Sang-Yun; Ji, Misuk; K, Kim; Son, YongHak; Jang, Seongsoo; Park, Yong Keun

doi:10.1038/srep34257

Cited by 88 publications

(66 citation statements)

References 66 publications

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“…Three-dimensional refractive index tomograms (37) and digital holographic microscopy images (38) have been used to measure temporal changes in morphological parameters of stored RBCs (39,40). Three-dimensional refractive index tomograms (37) and digital holographic microscopy images (38) have been used to measure temporal changes in morphological parameters of stored RBCs (39,40).…”

Section: Comparative Analysismentioning

confidence: 99%

Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry

et al. 2019

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Deleterious changes, collectively termed as storage lesions, alter the characteristics of red blood cell (RBC) morphology during in vitro storage. Due to gradual loss of cellular membrane, RBCs lose their original biconcave disk shape and begin a process of spherical deformation that is characterized by well-defined morphological criteria. At the spheroechinocyte stage, the structure of RBC is irreversibly damaged and lacks the elasticity necessary to efficiently deliver oxygen. Quantifying the prevalence of spheroechinocytes could provide an important morphological measure of the quality of stored blood products. Unlike the conventional RBC morphology characterization assay involving light microscopy, we introduce a label-free assay using imaging flow cytometry (IFC). The technique captures 100,000 images of a sample and calculates a relative measure of spheroechinocyte population in a fraction of the time required by the conventional method. A comparative method study, measuring a morphological index for 11 RCC units through storage, found that the two techniques measured similar trends with IFC reporting the metric at an average of 3.9% higher. We monitored 18 RCC units between Weeks 1 and 6 of storage and found that the spheroechinocyte population increased by an average of 26.2%. The large (3.5-64.1%) variation between the units' spheroechinocyte population percentage at Week 1 suggests a possible dependence of blood product quality on donor characteristics. Given our method's ability to rapidly monitor large samples and refine morphological characterization beyond conventional methods, we believe our technique offers good potential for studying the underlying relationships between RBC morphology and blood storage lesions.Key terms deformability; erythrocyte; imaging flow cytometry; red blood cell morphology; storage lesion CURRENTLY, blood banks and hospital laboratories are devoid of convenient, label-free techniques to measure the morphological distribution of red blood cells (RBCs) stored in vitro. Parameters such as membrane shape and deformability are critical for understanding an RBC's functional efficacy as an oxygen transporter (1). Microscopy-related assays, considered the gold standard for RBC morphology analysis, stain and smear samples prior to manual characterization. High-throughput assays that can measure RBCs within environments closely resembling in vivo conditions could obtain a more insightful assessment of the morphology distribution.RBCs are stored in vitro primarily to enhance the oxygen carrying capacity of vulnerable patients via transfusions (2). Blood banks extract RBCs from donated blood using customized separation techniques and suspend the cells in a preservative fluid to increase its shelf-life (3). During storage of this red cell concentrate (RCC), typically set at a maximum of 6 weeks, RBCs undergo a sequence of significant biochemical and biomechanical changes, which have been collectively termed hypothermic storage lesions (4,5). RBC storage lesions cause the formation of m...

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Section: Comparative Analysismentioning

confidence: 99%

Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry

et al. 2019

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“…QPI is based on the determination of optical path length changes that can be acquired with minimum efforts of sample preparation. Moreover, hemoglobin mass changes of red blood cells were proposed as parameters to quantify cell quality during storing (12) and image processing based methods were demonstrated to be capable to distinguish between different types of blood cells (13,14). Moreover, quantitative phase imaging allows the extraction of absolute biophysical parameters like volume, refractive index, and dry mass of suspended cells that are related to various cellular features and processes (8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

Quantitative phase imaging for cell culture quality control

Kastl¹,

Isbach²,

Dirksen

et al. 2017

Cytometry Pt A

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The potential of quantitative phase imaging (QPI) with digital holographic microscopy (DHM) for quantification of cell culture quality was explored. Label-free QPI of detached single cells in suspension was performed by Michelson interferometer-based self-interference DHM. Two pancreatic tumor cell lines were chosen as cellular model and analyzed for refractive index, volume, and dry mass under varying culture conditions. Firstly, adequate cell numbers for reliable statistics were identified. Then, to characterize the performance and reproducibility of the method, we compared results from independently repeated measurements and quantified the cellular response to osmolality changes of the cell culture medium. Finally, it was demonstrated that the evaluation of QPI images allows the extraction of absolute cell parameters which are related to cell layer confluence states. In summary, the results show that QPI enables label-free imaging cytometry, which provides novel complementary integral biophysical data sets for sophisticated quantification of cell culture quality with minimized sample preparation. © 2017 International Society for Advancement of Cytometry.

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“…Previously, ODT has been widely used to study various biological samples including red blood cells [15][16][17][18][19][20][21][22] however, we were unable to identify lymphocyte cell types due to their indistinguishable cellular morphology and biochemical characteristics.…”

Section: Identification Of Lymphocyte Cell Types Is Crucial For Undermentioning

confidence: 87%

“…Previously, ODT has been widely used to study various biological samples including red blood cells [15][16][17][18][19][20][21][22] , white blood cells (WBC) 23,24 , hepatocytes 25 , cancer cells 16,[26][27][28][29][30][31][32] , neurons 32,33 , bacteria 34,35 , phytoplankton 36 , and hair 37 . In our previous study, we reported that ODT enables the quantitative analysis of WBCs including lymphocytes and macrophages 23 .…”

mentioning

confidence: 99%

Label-free identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Yoon

Kim

et al. 2017

Preprint

Self Cite

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Identification of lymphocyte cell types is crucial for understanding their pathophysiologic roles in human diseases.Current methods for discriminating lymphocyte cell types primarily relies on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present label-free identification of non-activated lymphocyte subtypes using refractive index tomography. From the measurements of three-dimensional refractive index maps of individual lymphocytes, the morphological and biochemical properties of the lymphocytes are quantitatively retrieved. Machine learning methods establish an optimized classification model using the retrieved quantitative characteristics of the lymphocytes to identify lymphocyte subtypes at the individual cell level. We show that our approach enables label-free identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T lymphocytes) with high specificity and sensitivity. The present method will be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.Lymphocytes consisting of various cell types including B, helper (CD4+) T, cytotoxic (CD8+) T, and regulatory T lymphocytes, and play crucial roles in the adaptive immune system 1 . Each lymphocyte cell type has different functions: B lymphocytes produce antibodies, and T lymphocytes recognize a specific antigen and execute effector functions. The lymphocyte population and function are tightly regulated to defend the host against harmful invaders or abnormal conditions 1,2 . Disturbances in lymphocyte function and regulation are related to various diseases including cancers 3-5 , autoimmune diseases 6,7 , and virus infections 8,9 .. CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/107805 doi: bioRxiv preprint first posted online Feb. 11, 2017; To understand the roles of different types of lymphocytes, several methods based on labelling techniques have been developed to identify and discriminate lymphocyte cell types. Because different kinds of lymphocytes have very similar cellular morphology such as a large nucleus with small cytosolic regions and round shapes, conventional optical methods such as bright-field microscopy or phase contrast microscopy have limited ability in classifying lymphocyte cell types 10 .To overcome this, a specific surface membrane proteins, known as surface markers, are recognized and tagged with magnetic beads or fluorescence molecules via antigen-antibody binding. And then, specific types of lymphocytes are identified and separated by magnetic forces or fluorescence signals 11 . Targeting surface markers is a precise and efficient manner to determine the cell types; however, labelling methods have potential risks of altering cellular functions b...

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Measuring cell surface area and deformability of individual human red blood cells over blood storage using quantitative phase imaging

Cited by 88 publications

References 66 publications

Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry

Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry

Quantitative phase imaging for cell culture quality control

Label-free identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

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