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Lamoureux ES, Cheng Y, Islamzada E, Matthews K, Duffy SP, Ma H. Biophysical profiling of red blood cells from thin-film blood smears using deep learning. Heliyon 2024; 10:e35276. [PMID: 39170127 PMCID: PMC11336426 DOI: 10.1016/j.heliyon.2024.e35276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 07/23/2024] [Accepted: 07/25/2024] [Indexed: 08/23/2024] Open
Abstract
Microscopic inspection of thin-film blood smears is widely used to identify red blood cell (RBC) pathologies, including malaria parasitism and hemoglobinopathies, such as sickle cell disease and thalassemia. Emerging research indicates that non-pathologic changes in RBCs can also be detected in images, such as deformability and morphological changes resulting from the storage lesion. In transfusion medicine, cell deformability is a potential biomarker for the quality of donated RBCs. However, a major impediment to the clinical translation of this biomarker is the difficulty associated with performing this measurement. To address this challenge, we developed an approach for biophysical profiling of RBCs based on cell images in thin-film blood smears. We hypothesize that subtle cellular changes are evident in blood smear images, but this information is inaccessible to human expert labellers. To test this hypothesis, we developed a deep learning strategy to analyze Giemsa-stained blood smears to assess the subtle morphologies indicative of RBC deformability and storage-based degradation. Specifically, we prepared thin-film blood smears from 27 RBC samples (9 donors evaluated at 3 storage time points) and imaged them using high-resolution microscopy. Using this dataset, we trained a convolutional neural network to evaluate image-based morphological features related to cell deformability. The prediction of donor deformability is strongly correlated to the microfluidic scores and can be used to categorize images into specific deformability groups with high accuracy. We also used this model to evaluate differences in RBC morphology resulting from cold storage. Together, our results demonstrate that deep learning models can detect subtle cellular morphology differences resulting from deformability and cold storage. This result suggests the potential to assess donor blood quality from thin-film blood smears, which can be acquired ubiquitously in clinical workflows.
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Affiliation(s)
- Erik S. Lamoureux
- Department of Mechanical Engineering, University of British Columbia, Canada
- Centre for Blood Research, University of British Columbia, Canada
| | - You Cheng
- Department of Mechanical Engineering, University of British Columbia, Canada
- Centre for Blood Research, University of British Columbia, Canada
| | - Emel Islamzada
- Department of Mechanical Engineering, University of British Columbia, Canada
- Centre for Blood Research, University of British Columbia, Canada
| | - Kerryn Matthews
- Department of Mechanical Engineering, University of British Columbia, Canada
- Centre for Blood Research, University of British Columbia, Canada
| | - Simon P. Duffy
- Department of Mechanical Engineering, University of British Columbia, Canada
- Centre for Blood Research, University of British Columbia, Canada
- British Columbia Institute of Technology, Canada
| | - Hongshen Ma
- Department of Mechanical Engineering, University of British Columbia, Canada
- Centre for Blood Research, University of British Columbia, Canada
- School of Biomedical Engineering, University of British Columbia, Canada
- Vancouver Prostate Centre, Vancouver General Hospital, Canada
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Laengst E, Crettaz D, Tissot JD, Prudent M. The Effect of the Donor's and Recipient's Sex on Red Blood Cells Evaluated Using Transfusion Simulations. Cells 2023; 12:1454. [PMID: 37296575 PMCID: PMC10252512 DOI: 10.3390/cells12111454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/21/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023] Open
Abstract
The hypothesis of the potential impact of the sex of red blood cell (RBC) concentrate (RCC) donors, as well as the sex of the recipients, on the clinical outcome, is still under evaluation. Here, we have evaluated the sex impact on RBC properties using in vitro transfusion models. Using a "flask model", RBCs from RCCs (representing the donor)-at different storage lengths-were incubated in a sex-matched and sex-mismatched manner with fresh frozen plasma pools (representing the recipient) at 37 °C, with 5% of CO2 up to 48 h. Standard blood parameters, hemolysis, intracellular ATP, extracellular glucose and lactate were quantified during incubation. Additionally, a "plate model", coupling hemolysis analysis and morphological study, was carried out in similar conditions in 96-well plates. In both models, RBCs from both sexes hemolyzed significantly less in female-derived plasma. No metabolic or morphological differences were observed between sex-matched and -mismatched conditions, even though ATP was higher in female-derived RBCs during incubations. Female plasma reduced hemolysis of female- as well as male-derived RBCs, which may be related to a sex-dependent plasma composition and/or sex-related intrinsic RBC properties.
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Affiliation(s)
- Emmanuel Laengst
- Laboratoire de Recherche sur les Produits Sanguins, Transfusion Interrégionale CRS, 1066 Epalinges, Switzerland; (E.L.)
- Faculté de Biologie et de Médecine, University of Lausanne, 1011 Lausanne, Switzerland
| | - David Crettaz
- Laboratoire de Recherche sur les Produits Sanguins, Transfusion Interrégionale CRS, 1066 Epalinges, Switzerland; (E.L.)
| | - Jean-Daniel Tissot
- Laboratoire de Recherche sur les Produits Sanguins, Transfusion Interrégionale CRS, 1066 Epalinges, Switzerland; (E.L.)
| | - Michel Prudent
- Laboratoire de Recherche sur les Produits Sanguins, Transfusion Interrégionale CRS, 1066 Epalinges, Switzerland; (E.L.)
- Faculté de Biologie et de Médecine, University of Lausanne, 1011 Lausanne, Switzerland
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva, Switzerland
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Matthews K, Lamoureux ES, Myrand-Lapierre ME, Duffy SP, Ma H. Technologies for measuring red blood cell deformability. LAB ON A CHIP 2022; 22:1254-1274. [PMID: 35266475 DOI: 10.1039/d1lc01058a] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Kerryn Matthews
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Erik S Lamoureux
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Marie-Eve Myrand-Lapierre
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
| | - Simon P Duffy
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
- British Columbia Institute of Technology, Vancouver, BC, Canada
| | - Hongshen Ma
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
- Department of Urologic Science, University of British Columbia, Vancouver, BC, Canada
- Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, BC, Canada
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Lamoureux ES, Islamzada E, Wiens MVJ, Matthews K, Duffy SP, Ma H. Assessing red blood cell deformability from microscopy images using deep learning. LAB ON A CHIP 2021; 22:26-39. [PMID: 34874395 DOI: 10.1039/d1lc01006a] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Red blood cells (RBCs) must be highly deformable to transit through the microvasculature to deliver oxygen to tissues. The loss of RBC deformability resulting from pathology, natural aging, or storage in blood bags can impede the proper function of these cells. A variety of methods have been developed to measure RBC deformability, but these methods require specialized equipment, long measurement time, and highly skilled personnel. To address this challenge, we investigated whether a machine learning approach could be used to predict donor RBC deformability based on morphological features from single cell microscope images. We used the microfluidic ratchet device to sort RBCs based on deformability. Sorted cells are then imaged and used to train a deep learning model to classify RBC based image features related to cell deformability. This model correctly predicted deformability of individual RBCs with 81 ± 11% accuracy averaged across ten donors. Using this model to score the deformability of RBC samples was accurate to within 10.4 ± 6.8% of the value obtained using the microfluidic ratchet device. While machine learning methods are frequently developed to automate human image analysis, our study is remarkable in showing that deep learning of single cell microscopy images could be used to assess RBC deformability, a property not normally measurable by imaging. Measuring RBC deformability by imaging is also desirable because it can be performed rapidly using a standard microscopy system, potentially enabling RBC deformability studies to be performed as part of routine clinical assessments.
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Affiliation(s)
- Erik S Lamoureux
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Emel Islamzada
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Matthew V J Wiens
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Kerryn Matthews
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Simon P Duffy
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
- British Columbia Institute of Technology, Burnaby, BC, Canada
| | - Hongshen Ma
- Department of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC, V6T 1Z4, Canada.
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, BC, Canada
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