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

Flow Cytometry as a New Accessible Method to Evaluate Diagnostic Osmotic Changes in Patients with Red Blood Cell Membrane Defects

Hereditary spherocytosis (HS) is a membranopathy that impacts the vertical junctions between the cytoskeleton and the plasma membrane of erythrocytes. The gold standard method for diagnosing it is osmotic gradient ektacytometry (OGE). However, access to this technique is scarce. We have devised a straightforward approach utilizing flow cytometry to quantify variations in an osmotic gradient, relying on FSC-H/SSC-H patterns. We studied 14 patients (9 pediatric, 5 adults) and 54 healthy controls (16 pediatric, 38 adults). After assessing the behavior of the samples in several osmolar gradients we selected for the study the 176, 308, and 458 mOsm/kg levels as hypo-osmolar, iso-osmolar, and hyper-osmolar references. We then selected the iso-osmolar point for assessment to determine its efficacy in discriminating between patient and control groups using a receiver operating characteristic curve. In the pediatric group, the area under the curve (AUC) was 1.0, indicating 100% sensitivity and 93.3% specificity. Conversely, in the adult group, the AUC was 0.98, with 80% sensitivity and 90.9% specificity. We introduce a method that is easily replicable and demonstrates high sensitivity and specificity. This technique could prove valuable in the diagnosis of spherocytosis.

Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data

Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standardized objective technique. Over the past two decades, imaging flow cytometry (IFC) has proven effective in combining image-based morphometric analyses with high-parameter phenotyping. We have previously demonstrated the effectiveness of combining IFC with a feature-based machine learning algorithm to accurately identify and quantify rare binucleated erythroblasts (BNEs) in dyserythropoietic BM cells. However, a feature-based workflow poses challenges requiring software-specific expertise. Here we employ a Convolutional Neural Network (CNN) algorithm for BNE identification and differentiation from doublets and cells with irregular nuclear morphology in IFC data. We demonstrate that this simplified AI workflow, coupled with a powerful CNN algorithm, achieves comparable BNE quantification accuracy to manual and feature-based analysis with substantial time savings, eliminating workflow complexity. This streamlined approach holds significant clinical value, enhancing IFC accessibility for routine diagnostic purposes.

Transfusion Practice: Hemolysis Markers After In Vitro Infusion of Packed Red Blood Cells by the Gravitational Method in Peripheral Catheter

The objective of this study was to compare hemolysis marker levels after in vitro infusion of red blood cells (RBCs) according to storage time, infusion rate, and peripheral intravenous catheter size. This is an experimental study with randomly administered RBCs in quintuplicate, according to storage time shorter than and longer than 14 days, as well as infusion rate (50 mL/h and 100 mL/h) using catheters with calibers of 14-, 18-, and 20-gauge. Aliquots were collected from RBCs (V1), after equipment and catheter (V2) free-flow filling and after controlled infusion through the catheter (V3). The hemolytic markers analyzed were degree of hemolysis (%), hematocrit (Ht) (%), total hemoglobin (THb) (g/dL), free hemoglobin (FHb) (g/dL), potassium (K) (mmol/L), and lactate dehydrogenase (LDH) (U/L), considering a probability of error ≤5%. Sixty experiments were performed with the analysis of 180 aliquots. When RBCs aged <14 days were used, all catheters tended to increase THb, FHb, and K; while >14 days, RBCs presented increased FHb and degree of hemolysis with catheters of 18-gauge and THb levels at 14-gauge. Among the conditions analyzed, only 20-gauge catheters (the smallest) did not influence changes in hemolysis markers, regardless of RBC storage time.

Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control

Background

"Artificial intelligence" and "big data" increasingly take the step from just being interesting concepts to being relevant or even part of our lives. This general statement holds also true for transfusion medicine. Besides all advancements in transfusion medicine, there is not yet an established red blood cell quality measure, which is generally applied.

Summary

We highlight the usefulness of big data in transfusion medicine. Furthermore, we emphasize in the example of quality control of red blood cell units the application of artificial intelligence.

Key Messages

A variety of concepts making use of big data and artificial intelligence are readily available but still await to be implemented into any clinical routine. For the quality control of red blood cell units, clinical validation is still required.

Blood quality evaluation via on-chip classification of cell morphology using a deep learning algorithm

The quality of red blood cells (RBCs) in stored blood has a direct impact on the recovery of patients treated by blood transfusion, which directly reflects the quality of blood. The traditional means for blood quality evaluation involve the use of reagents and multi-step and time-consuming operations. Here, a low-cost, multi-classification, label-free and high-precision method is developed, which combines microfluidic technology and a deep learning algorithm together to recognize and classify RBCs based on morphology. The microfluidic channel is designed to effectively and controllably solve the problem of cell overlap, which has a severe negative impact on the identification of cells. The object detection model in the deep learning algorithm is optimized and used to recognize multiple RBCs simultaneously in the whole field of view, so as to classify them into six morphological subcategories and count the numbers in each subgroup. The mean average precision of the developed object detection model reaches 89.24%. The blood quality can be evaluated by calculating the morphology index (MI) according to the numbers of cells in subgroups. The validation of the method is verified by evaluating three blood samples stored for 7 days, 21 days and 42 days, which have MIs of 84.53%, 73.33% and 24.34%, respectively, indicating good agreement with the actual blood quality. This method has the merits of cell identification in a wide channel, no need for single cell alignment as the image cytometry does and it is not only applicable to the quality evaluation of RBCs, but can also be used for general cell identifications with different morphologies.

Technologies for measuring red blood cell deformability

Human red blood cells (RBCs) are approximately 8 μm in diameter, but must repeatedly deform through capillaries as small as 2 μm in order to deliver oxygen to all parts of the body. The loss of this capability is associated with the pathology of many diseases, and is therefore a potential biomarker for disease status and treatment efficacy. Measuring RBC deformability is a difficult problem because of the minute forces (∼pN) that must be exerted on these cells, as well as the requirements for throughput and multiplexing. The development of technologies for measuring RBC deformability date back to the 1960s with the development of micropipette aspiration, ektacytometry, and the cell transit analyzer. In the past 10 years, significant progress has been made using microfluidics by leveraging the ability to precisely control fluid flow through microstructures at the size scale of individual RBCs. These technologies have now surpassed traditional methods in terms of sensitivity, throughput, consistency, and ease of use. As a result, these efforts are beginning to move beyond feasibility studies and into applications to enable biomedical discoveries. In this review, we provide an overview of both traditional and microfluidic techniques for measuring RBC deformability. We discuss the capabilities of each technique and compare their sensitivity, throughput, and robustness in measuring bulk and single-cell RBC deformability. Finally, we discuss how these tools could be used to measure changes in RBC deformability in the context of various applications including pathologies caused by malaria and hemoglobinopathies, as well as degradation during storage in blood bags prior to blood transfusions.

Use of antioxidant nanoparticles to reduce oxidative stress in blood storage

Oxidative damage by free radicals has a negative effect on blood quality during storage. Antioxidant nanoparticles can prevent oxidative stress. We use SOD-CAT-Alb-PEG-PLGA- nanoparticles to reduce the effects of oxidative stress in blood storage. Electrospray was employed to prepare nanoparticles. Nanoparticles entered the test bags and were kept for 35 days from the time of donation under standard conditions. On target days, experiments were performed on the samples taken. The examination included blood smear, red blood cells count, hemoglobin, hematocrit, K, Fe, glutathione peroxidase, glutathion reductase, glucose-6-phosphate dehydrogenase, prooxidant-antioxidant balance, malondialdehyde, and flow cytometric assay for phosphatidylserine. The repeated measures analysis was performed on samples every week. Morphological changes were less in the test group compared to the control. The quantitative hemolysis profile test showed significant changes in the test and control groups (p < 0.05) in consecutive weeks except for K and Fe. Oxidative stress parameters too showed a significant change during the target days of the examination (p < 0.05). Also, the phosphatidylserine expression was increased in control groups more than test in consecutive weeks (p < 0.05). It seems that the use of antioxidant nanoparticles improves the quality of stored red blood cells and can prevent posttransfusion complications and blood loss by reducing oxidative stress.

Real-time red blood cell counting and osmolarity analysis using a photoacoustic-based microfluidic system

Counting the number of red blood cells (RBCs) in blood samples is a common clinical diagnostic procedure, but conventional methods are unable to provide the size and other physical properties of RBCs at the same time. In this work, we explore photoacoustic (PA) detection as a rapid label-free and noninvasive analysis technique that can potentially be used for single RBC characterization based on their photoabsorption properties. We have demonstrated an on-chip PA flow cytometry system using a simple microfluidic chip combined with a PA imaging system to count and characterize up to ∼60 RBCs per second. Compared with existing microfluidic-based RBC analysis methods, which typically use camera-captured image sequences to characterize cell morphology and deformation, the PA method discussed here requires only the processing of one-dimensional time-series data instead of two- or three-dimensional time-series data acquired by computer vision methods. Therefore, the PA method will have significantly lower computational requirements when large numbers of RBCs are to be analyzed. Moreover, we have demonstrated that the PA signals of RBCs flowing in a microfluidic device could be directly used to acquire the osmolarity conditions (in the range of 124 to 497 mOsm L-1) of the medium surrounding the RBCs. This finding suggests a potential extension of applicability to blood tests via PA-based biomedical detection.

An image-based flow cytometric approach to the assessment of the nucleus-to-cytoplasm ratio

The nucleus-to-cytoplasm ratio (N:C) can be used as one metric in histology for grading certain types of tumor malignancy. Current N:C assessment techniques are time-consuming and low throughput. Thus, in high-throughput clinical contexts, there is a need for a technique that can assess cell malignancy rapidly. In this study, we assess the N:C ratio of four different malignant cell lines (OCI-AML-5-blood cancer, CAKI-2-kidney cancer, HT-29-colon cancer, SK-BR-3-breast cancer) and a non-malignant cell line (MCF-10A -breast epithelium) using an imaging flow cytometer (IFC). Cells were stained with the DRAQ-5 nuclear dye to stain the cell nucleus. An Amnis ImageStreamX® IFC acquired brightfield/fluorescence images of cells and their nuclei, respectively. Masking and gating techniques were used to obtain the cell and nucleus diameters for 5284 OCI-AML-5 cells, 1096 CAKI-2 cells, 6302 HT-29 cells, 3159 SK-BR-3 cells, and 1109 MCF-10A cells. The N:C ratio was calculated as the ratio of the nucleus diameter to the total cell diameter. The average cell and nucleus diameters from IFC were 12.3 ± 1.2 μm and 9.0 ± 1.1 μm for OCI-AML5 cells, 24.5 ± 2.6 μm and 15.6 ± 2.1 μm for CAKI-2 cells, 16.2 ± 1.8 μm and 11.2 ± 1.3 μm for HT-29 cells, 18.0 ± 3.7 μm and 12.5 ± 2.1 μm for SK-BR-3 cells, and 19.4 ± 2.2 μm and 10.1 ± 1.8 μm for MCF-10A cells. Here we show a general N:C ratio of ~0.6-0.7 across varying malignant cell lines and a N:C ratio of ~0.5 for a non-malignant cell line. This study demonstrates the use of IFC to assess the N:C ratio of cancerous and non-cancerous cells, and the promise of its use in clinically relevant high-throughput detection scenarios to supplement current workflows used for cancer cell grading.

Exploring dyserythropoiesis in patients with myelodysplastic syndrome by imaging flow cytometry and machine-learning assisted morphometrics

BACKGROUND

The hallmark of myelodysplastic syndrome (MDS) remains dysplasia in the bone marrow (BM). However, diagnosing MDS may be challenging and subject to inter-observer variability. Thus, there is an unmet need for novel objective, standardized and reproducible methods for evaluating dysplasia. Imaging flow cytometry (IFC) offers combined analyses of phenotypic and image-based morphometric parameters, for example, cell size and nuclearity. Hence, we hypothesized IFC to be a useful tool in MDS diagnostics.

METHODS

Using a different-from-normal approach, we investigated dyserythropoiesis by quantifying morphometric features in a median of 5953 erythroblasts (range: 489-68,503) from 14 MDS patients, 11 healthy donors, 6 non-MDS controls with increased erythropoiesis, and 6 patients with cytopenia.

RESULTS

First, we morphometrically confirmed normal erythroid maturation, as immunophenotypically defined erythroid precursors could be sequenced by significantly decreasing cell-, nuclear- and cytoplasm area. In MDS samples, we demonstrated cell size enlargement and increased fractions of macronormoblasts in late-stage erythroblasts (both p < .0001). Interestingly, cytopenic controls with high-risk mutational patterns displayed highly aberrant cell size morphometrics. Furthermore, assisted by machine learning algorithms, we reliably identified and enumerated true binucleated erythroblasts at a significantly higher frequency in two out of three erythroblast maturation stages in MDS patients compared to normal BM (both p = .0001).

CONCLUSION

We demonstrate proof-of-concept results of the applicability of automated IFC-based techniques to study and quantify morphometric changes in dyserythropoietic BM cells. We propose that IFC holds great promise as a powerful and objective tool in the complex setting of MDS diagnostics with the potential for minimizing inter-observer variability.

Emerging use of machine learning and advanced technologies to assess red cell quality

Improving blood product quality and patient outcomes is an accepted goal in transfusion medicine research. Thus, there is an urgent need to understand the potential adverse effects on red blood cells (RBCs) during pre-transfusion storage. Current assessment techniques of these degradation events, termed "storage lesions", are subjective, labor-intensive, and complex. Here we describe emerging technologies that assess the biochemical, biophysical, and morphological characteristics of RBC storage lesions. Of these emerging techniques, machine learning (ML) has shown potential to overcome the limitations of conventional RBC assessment methods. Our previous work has shown that neural networks can extract chronological progressions of morphological changes in RBCs during storage without human input. We hypothesize that, with broader training and testing of multivariate data (e.g., varying donor factors and manufacturing methods), ML can further our understanding of clinical transfusion outcomes in multiple patient groups.

Thrombospondin-1/CD47 signaling modulates transmembrane cation conductance, survival, and deformability of human red blood cells

BACKGROUND

Thrombospondin-1 (TSP-1), a Ca²⁺-binding trimeric glycoprotein secreted by multiple cell types, has been implicated in the pathophysiology of several clinical conditions. Signaling involving TSP-1, through its cognate receptor CD47, orchestrates a wide array of cellular functions including cytoskeletal organization, migration, cell-cell interaction, cell proliferation, autophagy, and apoptosis. In the present study, we investigated the impact of TSP-1/CD47 signaling on Ca²⁺ dynamics, survival, and deformability of human red blood cells (RBCs).

METHODS

Whole-cell patch-clamp was employed to examine transmembrane cation conductance. RBC intracellular Ca²⁺ levels and multiple indices of RBC cell death were determined using cytofluorometry analysis. RBC morphology and microvesiculation were examined using imaging flow cytometry. RBC deformability was measured using laser-assisted optical rotational cell analyzer.

RESULTS

Exposure of RBCs to recombinant human TSP-1 significantly increased RBC intracellular Ca²⁺ levels. As judged by electrophysiology experiments, TSP-1 treatment elicited an amiloride-sensitive inward current alluding to a possible Ca²⁺ influx via non-selective cation channels. Exogenous TSP-1 promoted microparticle shedding as well as enhancing Ca²⁺- and nitric oxide-mediated RBC cell death. Monoclonal (mouse IgG1) antibody-mediated CD47 ligation using 1F7 recapitulated the cell death-inducing effects of TSP-1. Furthermore, TSP-1 treatment altered RBC cell shape and stiffness (maximum elongation index).

CONCLUSIONS

Taken together, our data unravel a new role for TSP-1/CD47 signaling in mediating Ca²⁺ influx into RBCs, a mechanism potentially contributing to their dysfunction in a variety of systemic diseases. Video abstract.

Objective assessment of stored blood quality by deep learning

Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans' assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

Donor-dependent aging of young and old red blood cell subpopulations: Metabolic and functional heterogeneity

BACKGROUND

Characteristics of red blood cells (RBCs) are influenced by donor variability. This study assessed quality and metabolomic variables of RBC subpopulations of varied biologic age in red blood cell concentrates (RCCs) from male and female donors to evaluate their contribution to the storage lesion.

STUDY DESIGN AND METHODS

Red blood cell concentrates from healthy male (n = 6) and female (n = 4) donors were Percoll separated into less dense ("young", Y-RCCs) and dense ("old", O-RCCs) subpopulations, which were assessed weekly for 28 days for changes in hemolysis, mean cell volume (MCV), hemoglobin concentration (MCHC), hemoglobin autofluorescence (HGB), morphology index (MI), oxygen affinity (p50), rigidity, intracellular reactive oxygen species (ROS), calcium ([Ca²⁺ ]), and mass spectrometry-based metabolomics.

RESULTS

Young RCCs having disc-to-discoid morphology showed higher MCV and MI, but lower MCHC, HGB, and rigidity than O-RCCs, having discoid-to-spheroid shape. By Day 14, Y-RCCs retained lower hemolysis and rigidity and higher p50 compared to O-RCCs. Donor sex analyses indicated that females had higher MCV, HGB, ROS, and [Ca²⁺ ] and lower hemolysis than male RBCs, in addition to having a decreased rate of change in hemolysis by Day 28. Metabolic profiling indicated a significant sex-related signature across all groups with increased markers of high membrane lipid remodeling and antioxidant capacity in Y-RCCs, whereas O-RCCs had increased markers of oxidative stress and decreased coping capability.

CONCLUSION

The structural, functional, and metabolic dissimilarities of Y-RCCs and O-RCCs from female and male donors demonstrate RCC heterogeneity, where RBCs from females contribute less to the storage lesion and age slower than males.

Determination of cell nucleus-to-cytoplasmic ratio using imaging flow cytometry and a combined ultrasound and photoacoustic technique: a comparison study

While the nucleus-to-cytoplasmic (N:C) ratio has traditionally been used for assessing cell malignancy, most N:C measurement techniques are time-consuming and performed on thin histological sections, which prohibit assessment of three-dimensional cell structure. A combined ultrahigh frequency ultrasound (US) and photoacoustic (PA) technique was used to assess the size and N:C ratio of cultured cancer cells in three dimensions (3D). The diameters of the cells and their stained nuclei were obtained by fitting the power spectrum of backscattered US pulses and emitted PA waves, respectively, to well-established theoretical models. For comparison, an imaging flow cytometer (IFC) was also used to determine the two-dimensional cell and nucleus sizes from large cell populations using brightfield and fluorescence images, respectively. An N:C ratio was calculated for each cell using the quotient of the measured nucleus diameter and the total cell diameter. The mean N:C ratios calculated using the sound-based approach were 0.68, 0.66, and 0.54 for MCF-7, PC-3, and MDA-MB-231 cells, respectively, and were in good agreement with the corresponding values of 0.68, 0.67, and 0.68 obtained using the IFC. The combined US and PA technique, which assesses cellular N:C ratio in 3D, has potential applications in the detection of circulating tumor cells in liquid biopsies.