[en] In many medical disciplines, red blood cells are discovered to be biomarkers since they “experience” various conditions in basically all organs of the body. Classical examples are diabetes and hypercholesterolemia. However, recently the red blood cell distribution width (RDW), is often referred to, as an unspecific parameter/marker (e.g., for cardiac events or in oncological studies). The measurement of RDW requires venous blood samples to perform the complete blood cell count (CBC). Here, we introduce Erysense, a lab-on-a-chip-based point-of-care device, to evaluate red blood cell flow properties. The capillary chip technology in combination with algorithms based on artificial neural networks allows the detection of very subtle changes in the red blood cell morphology. This flow-based method closely resembles in vivo conditions and blood sample volumes in the sub-microliter range are sufficient. We provide clinical examples for potential applications of Erysense as a diagnostic tool [here: neuroacanthocytosis syndromes (NAS)] and as cellular quality control for red blood cells [here: hemodiafiltration (HDF) and erythrocyte concentrate (EC) storage]. Due to the wide range of the applicable flow velocities (0.1–10 mm/s) different mechanical properties of the red blood cells can be addressed with Erysense providing the opportunity for differential diagnosis/judgments. Due to these versatile properties, we anticipate the value of Erysense for further diagnostic, prognostic, and theragnostic applications including but not limited to diabetes, iron deficiency, COVID-19, rheumatism, various red blood cell disorders and anemia, as well as inflammation-based diseases including sepsis.
Disciplines :
Sciences du vivant: Multidisciplinaire, généralités & autres
Alapan Y. Kim C. Adhikari A. Gray K. E. Gurkan-Cavusoglu E. Little J. A. et al. (2016). Sickle Cell Disease Biochip: a Functional Red Blood Cell Adhesion Assay for Monitoring Sickle Cell Disease. Translational Res. 173, 74–91. e8. 10.1016/j.trsl.2016.03.008
Barshtein G. Arbell D. Livshits L. Gural A. (2018). Is it Possible to Reverse the Storage-Induced Lesion of Red Blood Cells? Front. Physiol. 9, 914. 10.3389/fphys.2018.00914
Barshtein G. Manny N. Yedgar S. (2011). Circulatory Risk in the Transfusion of Red Blood Cells with Impaired Flow Properties Induced by Storage. Transfus. Med. Rev. 25, 24–35. 10.1016/j.tmrv.2010.08.004
Barshtein G. Pries A. R. Goldschmidt N. Zukerman A. Orbach A. Zelig O. et al. (2016). Deformability of Transfused Red Blood Cells Is a Potent Determinant of Transfusion-Induced Change in Recipient's Blood Flow. Microcirculation 23, 479–486. 10.1111/micc.12296
Bateman R. Sharpe M. Singer M. Ellis C. (2017). The Effect of Sepsis on the Erythrocyte. Ijms 18, 1932. 10.3390/ijms18091932
Bennett-Guerrero E. Veldman T. H. Doctor A. Telen M. J. Ortel T. L. Reid T. S. et al. (2007). Evolution of Adverse Changes in Stored RBCs. Proc. Natl. Acad. Sci. U.S.A. 104, 17063–17068. 10.1073/pnas.0708160104
Bernhardt I. Ellory J. C. (2003). Red Cell Membrane Transport in Health and Disease. Berlin and Heidelberg: Springer.
Bessis M. (1974). Corpuscles. Berlin and Heidelberg: Springer. 10.1007/978-3-642-65657-6
Brust M. Aouane O. Thiébaud M. Flormann D. Verdier C. Kaestner L. et al. (2014). The Plasma Protein Fibrinogen Stabilizes Clusters of Red Blood Cells in Microcapillary Flows. Sci. Rep. 4, 4348. 10.1038/srep04348
Caruso C. Fay M. E. Park S. I. Sulchek T. A. Graham M. D. Lam W. A. (2021). Assessing the Physiologic Relevance of Red Blood Cell Deformability in Iron Deficiency Anemia. Blood 138, 4153. 10.1182/blood-2021-150591
Chng K. Z. Ng Y. C. Namgung B. Tan J. K. S. Park S. Tien S. L. et al. (2021). Assessment of Transient Changes in Oxygen Diffusion of Single Red Blood Cells Using a Microfluidic Analytical Platform. Commun. Biol. 4, 271. 10.1038/s42003-021-01793-z
D'Alessandro A. Kriebardis A. G. Rinalducci S. Antonelou M. H. Hansen K. C. Papassideri I. S. et al. (2015). An Update on Red Blood Cell Storage Lesions, as Gleaned through Biochemistry and Omics Technologies. Transfusion 55, 205–219. 10.1111/trf.12804
D'Apolito R. Taraballi F. Minardi S. Liu X. Caserta S. Cevenini A. et al. (2016). Microfluidic Interactions between Red Blood Cells and Drug Carriers by Image Analysis Techniques. Med. Eng. Phys. 38, 17–23. 10.1016/j.medengphy.2015.10.005
Danielczok J. G. Terriac E. Hertz L. Petkova-Kirova P. Lautenschläger F. Laschke M. W. et al. (2017). Red Blood Cell Passage of Small Capillaries Is Associated with Transient Ca2+-Mediated Adaptations. Front. Physiol. 8, 979. 10.3389/fphys.2017.00979
Darras A. Peikert K. Rabe A. Yaya F. Simionato G. John T. et al. (2021). Acanthocyte Sedimentation Rate as a Diagnostic Biomarker for Neuroacanthocytosis Syndromes: Experimental Evidence and Physical Justificationcation. Cells 10, 788. 10.3390/cells10040788
Della Pelle G. Kostevšek N. (2021). Nucleic Acid Delivery with Red-Blood-Cell-Based Carriers. Ijms 22, 5264. 10.3390/ijms22105264
Doan M. Sebastian J. A. Caicedo J. C. Siegert S. Roch A. Turner T. R. et al. (2020). Objective Assessment of Stored Blood Quality by Deep Learning. Proc. Natl. Acad. Sci. U.S.A. 117, 21381–21390. 10.1073/pnas.2001227117
Ertan N. Z. Bozfakioglu S. Ugurel E. Sinan M. Yalcin O. (2017). Alterations of Erythrocyte Rheology and Cellular Susceptibility in End Stage Renal Disease: Effects of Peritoneal Dialysis. Plos One 12, e0171371. 10.1371/journal.pone.0171371
Friend J. Yeo L. (2010). Fabrication of Microfluidic Devices Using Polydimethylsiloxane. Biomicrofluidics 4, 026502. 10.1063/1.3259624
García-Roa M. Del Carmen Vicente-Ayuso M. Bobes A. M. Pedraza A. C. González-Fernández A. Martín M. P. et al. (2017). Red Blood Cell Storage Time and Transfusion: Current Practice, Concerns and Future Perspectives. Blood Transfus. 15, 222–231. 10.2450/2017.0345-16
Georgatzakou H. T. Tzounakas V. L. Kriebardis A. G. Velentzas A. D. Kokkalis A. C. Antonelou M. H. et al. (2018). Short-term Effects of Hemodiafiltration versus Conventional Hemodialysis on Erythrocyte Performance. Can. J. Physiol. Pharmacol. 96, 249–257. 10.1139/cjpp-2017-0285
Giacomello A. Quaratino C. P. Zoppini A. (1997). Erythrocyte Sedimentation Rate within Rheumatic Disease Clinics. J. Rheumatol. 24, 2263–2265.
Guckenberger A. Kihm A. John T. Wagner C. Gekle S. (2018). Numerical-experimental Observation of Shape Bistability of Red Blood Cells Flowing in a Microchannel. Soft Matter 14, 2032–2043. 10.1039/c7sm02272g
Himbert S. Qadri S. M. Sheffield W. P. Schubert P. D’Alessandro A. Rheinstädter M. C. (2021). Blood Bank Storage of Red Blood Cells Increases RBC Cytoplasmic Membrane Order and Bending Rigidity. Plos One 16, e0259267. 10.1371/journal.pone.0259267
Huisjes R. Bogdanova A. van Solinge W. W. Schiffelers R. M. Kaestner L. van Wijk R. (2018). Squeezing for Life - Properties of Red Blood Cell Deformability. Front. Physiol. 9, 656. 10.3389/fphys.2018.00656
Inglebert M. Locatelli L. Tsvirkun D. Sinha P. Maier J. A. Misbah C. et al. (2020). The Effect of Shear Stress Reduction on Endothelial Cells: A Microfluidic Study of the Actin Cytoskeleton. Biomicrofluidics 14, 024115. 10.1063/1.5143391
Irino K. Tajikawa T. Kohri S. Hatanaka Y. (2019). In Vitro evaluation on Influence of Dialysis Treatment on Erythrocyte Deformability and Hemolytic Property. Proc. Conf. Kansai Branch 2019.94, 513. 10.1299/jsmekansai.2019.94.513
Islamzada E. Matthews K. Guo Q. Santoso A. T. Duffy S. P. Scott M. D. et al. (2020). Deformability Based Sorting of Stored Red Blood Cells Reveals Donor-dependent Aging Curves. Lab. Chip 20, 226–235. 10.1039/c9lc01058k
Jung H. H. Danek A. Walker R. H. (2011). Neuroacanthocytosis Syndromes. Orphanet J. Rare Dis. 6, 68–69. 10.1186/1750-1172-6-68
Kaestner L. Bianchi P. (2020). Trends in the Development of Diagnostic Tools for Red Blood Cell-Related Diseases and Anemias. Front. Physiol. 11, 387. 10.3389/fphys.2020.00387
Kihm A. Kaestner L. Wagner C. Quint S. (2018). Classification of Red Blood Cell Shapes in Flow Using Outlier Tolerant Machine Learning. Plos Comput. Biol. 14, e1006278. 10.1371/journal.pcbi.1006278
Kim E. Park S. Hwang S. Moon I. Javidi B. (2022). Deep Learning-Based Phenotypic Assessment of Red Cell Storage Lesions for Safe Transfusions. IEEE J. Biomed. Health Inform. 26, 1318–1328. 10.1109/jbhi.2021.3104650
Kim-Shapiro D. B. Lee J. Gladwin M. T. (2011). Storage Lesion: Role of Red Blood Cell Breakdown. Transfusion 51, 844–851. 10.1111/j.1537-2995.2011.03100.x
Koch C. G. Sessler D. I. Duncan A. E. Mascha E. J. Li L. Yang D. et al. (2020). Effect of Red Blood Cell Storage Duration on Major Postoperative Complications in Cardiac Surgery: A Randomized Trial. J. Thorac. Cardiovasc. Surg. 160, 1505–1514. e3. 10.1016/j.jtcvs.2019.09.165
Kubánková M. Hohberger B. Hoffmanns J. Fürst J. Herrmann M. Guck J. et al. (2021). Physical Phenotype of Blood Cells Is Altered in COVID-19. Biophysical J. 120, 2838–2847. 10.1016/j.bpj.2021.05.025
Lamoureux E. S. Islamzada E. Wiens M. V. J. Matthews K. Duffy S. P. Ma H. (2022). Assessing Red Blood Cell Deformability from Microscopy Images Using Deep Learning. Lab. Chip 22, 26–39. 10.1039/d1lc01006a
Lelubre C. Vincent J.-L. (2013). Relationship between Red Cell Storage Duration and Outcomes in Adults Receiving Red Cell Transfusions: a Systematic Review. Crit. Care 17, R66. 10.1186/cc12600
Lizarralde Iragorri M. A. El Hoss S. Brousse V. Lefevre S. D. Dussiot M. Xu T. et al. (2018). A Microfluidic Approach to Study the Effect of Mechanical Stress on Erythrocytes in Sickle Cell Disease. Lab. Chip 18, 2975–2984. 10.1039/c8lc00637g
Luten M. Roerdinkholder-Stoelwinder B. Schaap N. P. M. de Grip W. J. Bos H. J. Bosman G. J. C. G. M. (2008). Survival of Red Blood Cells after Transfusion: a Comparison between Red Cells Concentrates of Different Storage Periods. Transfusion 48, 1478–1485. 10.1111/j.1537-2995.2008.01734.x
Matthews K. Myrand-Lapierre M.-E. Ang R. R. Duffy S. P. Scott M. D. Ma H. (2015). Microfluidic Deformability Analysis of the Red Cell Storage Lesion. J. Biomech. 48, 4065–4072. 10.1016/j.jbiomech.2015.10.002
McPhedran P. Hall R. B. (2005). Usefulness of Peripheral Blood Smears in Identifying the Causes of Anemia in Adults. Blood 106, 5565. 10.1182/blood.v106.11.5565.5565
Myrand-Lapierre M.-E. Deng X. Ang R. R. Matthews K. Santoso A. T. Ma H. (2015). Multiplexed Fluidic Plunger Mechanism for the Measurement of Red Blood Cell Deformability. Lab. Chip 15, 159–167. 10.1039/c4lc01100g
Offner P. J. Moore E. E. Biffl W. L. Johnson J. L. Silliman C. C. (2002). Increased Rate of Infection Associated with Transfusion of Old Blood after Severe Injury. Arch. Surg. 137, 711–717. 10.1001/archsurg.137.6.711
Park H.-S. Price H. Ceballos S. Chi J.-T. Wax A. (2021). Single Cell Analysis of Stored Red Blood Cells Using Ultra-high Throughput Holographic Cytometry. Cells 10, 2455. 10.3390/cells10092455
Peikert K. Danek A. Hermann A. (2018). Current State of Knowledge in Chorea-Acanthocytosis as Core Neuroacanthocytosis Syndrome. Eur. J. Med. Genet. 61, 699–705. 10.1016/j.ejmg.2017.12.007
Peikert K. Hermann A. Danek A. (2022). XK-associated McLeod Syndrome: Nonhematological Manifestations and Relation to VPS13A Disease. Transfus. Med. Hemother 49, 4–12. 10.1159/000521417
Piagnerelli M. Boudjeltia K. Z. Vanhaeverbeek M. Vincent J.-L. (2003). Red Blood Cell Rheology in Sepsis. Intensive Care Med. 29, 1052–1061. 10.1007/s00134-003-1783-2
Piety N. Z. Gifford S. C. Yang X. Shevkoplyas S. S. (2015). Quantifying Morphological Heterogeneity: a Study of More Than 1 000 000 Individual Stored Red Blood Cells. Vox Sang 109, 221–230. 10.1111/vox.12277
Piety N. Z. Stutz J. Yilmaz N. Xia H. Yoshida T. Shevkoplyas S. S. (2021). Microfluidic Capillary Networks Are More Sensitive Than Ektacytometry to the Decline of Red Blood Cell Deformability Induced by Storage. Sci. Rep. 11, 604. 10.1038/s41598-020-79710-3
Pretini V. Koenen M. H. Kaestner L. Fens M. H. A. M. Schiffelers R. M. Bartels M. et al. (2019). Red Blood Cells: Chasing Interactions. Front. Physiol. 10, 945. 10.3389/fphys.2019.00945
Pries A. R. Secomb T. W. Gaehtgens P. (1995). Structure and Hemodynamics of Microvascular Networks: Heterogeneity and Correlations. Am. J. Physiology-Heart Circulatory Physiol. 269, H1713–H1722. 10.1152/ajpheart.1995.269.5.h1713
Quint S. Christ A. F. Guckenberger A. Himbert S. Kaestner L. Gekle S. et al. (2017). 3D Tomography of Cells in Micro-channels. Appl. Phys. Lett. 111, 103701. 10.1063/1.4986392
Rabe A. Kihm A. Darras A. Peikert K. Simionato G. Dasanna A. K. et al. (2021). The Erythrocyte Sedimentation Rate and its Relation to Cell Shape and Rigidity of Red Blood Cells from Chorea-Acanthocytosis Patients in an Off-Label Treatment with Dasatinib. Biomolecules 11, 727. 10.3390/biom11050727
Recktenwald S. M. Graessel K. Maurer F. M. John T. Gekle S. Wagner C. (2022). Red Blood Cell Shape Transitions and Dynamics in Time-dependent Capillary Flows. Biophysical J. 121, 23–36. 10.1016/j.bpj.2021.12.009
Reichel F. Mauer J. Nawaz A. A. Gompper G. Guck J. Fedosov D. A. (2019). High-Throughput Microfluidic Characterization of Erythrocyte Shapes and Mechanical Variability. Biophysical J. 117, 14–24. 10.1016/j.bpj.2019.05.022
Rizzuto V. Mencattini A. Álvarez-González B. Di Giuseppe D. Martinelli E. Beneitez-Pastor D. et al. (2021). Combining Microfluidics with Machine Learning Algorithms for RBC Classification in Rare Hereditary Hemolytic Anemia. Sci. Rep. 11, 13553. 10.1038/s41598-021-92747-2
Roussel C. Morel A. Dussiot M. Marin M. Colard M. Fricot-Monsinjon A. et al. (2021). Rapid Clearance of Storage-Induced Microerythrocytes Alters Transfusion Recovery. Blood 137, 2285–2298. 10.1182/blood.2020008563
Secomb T. W. (2017). Blood Flow in the Microcirculation. Annu. Rev. Fluid Mech. 49, 443–461. 10.1146/annurev-fluid-010816-060302
Simionato G. Hinkelmann K. Chachanidze R. Bianchi P. Fermo E. van Wijk R. et al. (2021). Red Blood Cell Phenotyping from 3D Confocal Images Using Artificial Neural Networks. Plos Comput. Biol. 17, e1008934. 10.1371/journal.pcbi.1008934
Song W. Huang P. Wang J. Shen Y. Zhang J. Lu Z. et al. (2021). Red Blood Cell Classification Based on Attention Residual Feature Pyramid Network. Front. Med. 8, 741407. 10.3389/fmed.2021.741407
Storch A. Kornhass M. Schwarz J. (2005). Testing for Acanthocytosis. J. Neurol. 252, 84–90. 10.1007/s00415-005-0616-3
Tan J. K. S. Wei X. Wong P. A. Fang J. Kim S. Agrawal R. (2020). Altered Red Blood Cell Deformability-A Novel Hypothesis for Retinal Microangiopathy in Diabetic Retinopathy. Microcirculation 27, e12649. 10.1111/micc.12649
Thurlow J. S. Joshi M. Yan G. Norris K. C. Agodoa L. Y. Yuan C. M. et al. (2021). Global Epidemiology of End-Stage Kidney Disease and Disparities in Kidney Replacement Therapy. Am. J. Nephrol. 52, 98–107. 10.1159/000514550
Tinmouth A. Fergusson D. Yee I. C. Hébert P. C. Group A. I. the C. C. C. T. (2006). Clinical Consequences of Red Cell Storage in the Critically Ill. Transfusion 46, 2014–2027. 10.1111/j.1537-2995.2006.01026.x
Yoshida T. Prudent M. D'alessandro A. (2019). Red Blood Cell Storage Lesion: Causes and Potential Clinical Consequences. Blood Transfus. 17, 27–52. 10.2450/2019.0217-18
