1
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Recktenwald SM, Rashidi Y, Graham I, Arratia PE, Del Giudice F, Wagner C. Morphology, repulsion, and ordering of red blood cells in viscoelastic flows under confinement. SOFT MATTER 2024; 20:4950-4963. [PMID: 38873747 DOI: 10.1039/d4sm00446a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Red blood cells (RBC), the primary carriers of oxygen in the body, play a crucial role across several biomedical applications, while also being an essential model system of a deformable object in the microfluidics and soft matter fields. However, RBC behavior in viscoelastic liquids, which holds promise in enhancing microfluidic diagnostic applications, remains poorly studied. We here show that using viscoelastic polymer solutions as a suspending carrier causes changes in the clustering and shape of flowing RBC in microfluidic flows when compared to a standard Newtonian suspending liquid. Additionally, when the local RBC concentration increases to a point where hydrodynamic interactions take place, we observe the formation of equally-spaced RBC structures, resembling the viscoelasticity-driven ordered particles observed previously in the literature, thus providing the first experimental evidence of viscoelasticity-driven cell ordering. The observed RBC ordering, unaffected by polymer molecular architecture, persists as long as the surrounding medium exhibits shear-thinning, viscoelastic properties. Complementary numerical simulations reveal that viscoelasticity-induced repulsion between RBCs leads to equidistant structures, with shear-thinning modulating this effect. Our results open the way for the development of new biomedical technologies based on the use of viscoelastic liquids while also clarifying fundamental aspects related to multibody hydrodynamic interactions in viscoelastic microfluidic flows.
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Affiliation(s)
- Steffen M Recktenwald
- Dynamics of Fluids, Department of Experimental Physics, Saarland University, 66123 Saarbrücken, Germany.
- Micro/Bio/Nanofluidics Unit, Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Okinawa 904-0495, Japan
| | - Yazdan Rashidi
- Dynamics of Fluids, Department of Experimental Physics, Saarland University, 66123 Saarbrücken, Germany.
| | - Ian Graham
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paulo E Arratia
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Francesco Del Giudice
- Complex Fluid Research Group, Department of Chemical Engineering, Faculty of Science and Engineering, Swansea University, Swansea SA1 8EN, UK
| | - Christian Wagner
- Dynamics of Fluids, Department of Experimental Physics, Saarland University, 66123 Saarbrücken, Germany.
- Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg
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2
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Lo MCK, Siu DMD, Lee KCM, Wong JSJ, Yeung MCF, Hsin MKY, Ho JCM, Tsia KK. Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2307591. [PMID: 38864546 DOI: 10.1002/advs.202307591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 05/17/2024] [Indexed: 06/13/2024]
Abstract
Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.
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Affiliation(s)
- Michelle C K Lo
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 000000, Hong Kong
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
| | - Dickson M D Siu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 000000, Hong Kong
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 000000, Hong Kong
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
| | - Justin S J Wong
- Conzeb Limited, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
| | - Maximus C F Yeung
- Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, 000000, Hong Kong
| | - Michael K Y Hsin
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, 000000, Hong Kong
| | - James C M Ho
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, 000000, Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 000000, Hong Kong
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, New Territories, Hong Kong, 000000, Hong Kong
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3
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Hybel TE, Jensen SH, Rodrigues MA, Hybel TE, Pedersen MN, Qvick SH, Enemark MH, Bill M, Rosenberg CA, Ludvigsen M. Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia. Int J Mol Sci 2024; 25:6465. [PMID: 38928171 PMCID: PMC11203419 DOI: 10.3390/ijms25126465] [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: 05/17/2024] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the genetic transformation of hematopoietic stem cells (HSCs). LSCs hold prognostic value, but their molecular and immunophenotypic heterogeneity poses challenges: there is no single marker for identifying all LSCs across AML samples. We hypothesized that imaging flow cytometry (IFC) paired with artificial intelligence-driven image analysis could visually distinguish LSCs from HSCs based solely on morphology. Initially, a seven-color IFC panel was employed to immunophenotypically identify LSCs and HSCs in bone marrow samples from five AML patients and ten healthy donors, respectively. Next, we developed convolutional neural network (CNN) models for HSC-LSC discrimination using brightfield (BF), side scatter (SSC), and DNA images. Classification using only BF images achieved 86.96% accuracy, indicating significant morphological differences. Accuracy increased to 93.42% when combining BF with DNA images, highlighting differences in nuclear morphology, although DNA images alone were inadequate for accurate HSC-LSC discrimination. Model development using SSC images revealed minor granularity differences. Performance metrics varied substantially between AML patients, indicating considerable morphologic variations among LSCs. Overall, we demonstrate proof-of-concept results for accurate CNN-based HSC-LSC differentiation, instigating the development of a novel technique within AML monitoring.
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Affiliation(s)
- Trine Engelbrecht Hybel
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Sofie Hesselberg Jensen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | | | - Thomas Engelbrecht Hybel
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Maya Nautrup Pedersen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Signe Håkansson Qvick
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Marie Hairing Enemark
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Marie Bill
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Carina Agerbo Rosenberg
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Maja Ludvigsen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
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4
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Rosenberg CA, Rodrigues MA, Bill M, Ludvigsen M. Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data. Sci Rep 2024; 14:9349. [PMID: 38654058 PMCID: PMC11039460 DOI: 10.1038/s41598-024-59875-x] [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: 12/09/2023] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
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.
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Affiliation(s)
- Carina A Rosenberg
- Department of Hematology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, C115, 8200, Aarhus C, Denmark.
| | | | - Marie Bill
- Department of Hematology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, C115, 8200, Aarhus C, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Maja Ludvigsen
- Department of Hematology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 35, C115, 8200, Aarhus C, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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5
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Isiksacan Z, D’Alessandro A, McKenna DH, Tessier SN, Kucukal E, Gokaltun AA, William N, Sandlin RD, Bischof J, Mohandas N, Busch MP, Elbuken C, Gurkan UA, Toner M, Acker JP, Yarmush ML, Usta OB. Reply to Kaestner et al.: Pioneering quantitative platforms for stored red blood cell assessment open the door for precision transfusion medicine. Proc Natl Acad Sci U S A 2024; 121:e2320521121. [PMID: 38437566 PMCID: PMC10945785 DOI: 10.1073/pnas.2320521121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024] Open
Affiliation(s)
- Ziya Isiksacan
- Department of Surgery, Center for Engineering in Medicine and Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
| | - Angelo D’Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Aurora, CO80045
| | - David H. McKenna
- Division of Transfusion Medicine, Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN55455
| | - Shannon N. Tessier
- Department of Surgery, Center for Engineering in Medicine and Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
| | | | - A. Aslihan Gokaltun
- Department of Surgery, Center for Engineering in Medicine and Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
- Department of Chemical Engineering, Hacettepe University, Ankara06532, Turkey
| | - Nishaka William
- Laboratory Medicine and Pathology, University of Alberta, Edmonton, ABT6G 2R8, Canada
| | - Rebecca D. Sandlin
- Department of Surgery, Center for Engineering in Medicine and Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
| | - John Bischof
- Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN55455
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN55455
| | | | - Michael P. Busch
- Vitalant Research Institute, San Francisco, CA94105
- Department of Laboratory Medicine, University of California, San Francisco, CA94105
| | - Caglar Elbuken
- Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center, Bilkent University, Ankara06800, Turkey
- Faculty of Biochemistry and Molecular Medicine, Faculty of Medicine, University of Oulu, Oulu90014, Finland
- Valtion Teknillinen Tutkimuskeskus Technical Research Centre of Finland Ltd., Oulu90570, Finland
| | - Umut A. Gurkan
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH44106
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH44106
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH44106
| | - Mehmet Toner
- Department of Surgery, Center for Engineering in Medicine and Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
| | - Jason P. Acker
- Laboratory Medicine and Pathology, University of Alberta, Edmonton, ABT6G 2R8, Canada
- Innovation and Portfolio Management, Canadian Blood Services, Edmonton, ABT6G 2R8, Canada
| | - Martin L. Yarmush
- Department of Surgery, Center for Engineering in Medicine and Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ08854
| | - O. Berk Usta
- Department of Surgery, Center for Engineering in Medicine and Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
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6
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Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, Stanworth SJ. Machine learning in transfusion medicine: A scoping review. Transfusion 2024; 64:162-184. [PMID: 37950535 DOI: 10.1111/trf.17582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Suzanne Maynard
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Joseph Farrington
- Institute of Health Informatics, University College London, London, UK
| | - Samah Alimam
- Haematology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Hayley Evans
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London, UK
| | - Wai Keong Wong
- Director of Digital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Simon J Stanworth
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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7
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Barnes CM, Power AL, Barber DG, Tennant RK, Jones RT, Lee GR, Hatton J, Elliott A, Zaragoza-Castells J, Haley SM, Summers HD, Doan M, Carpenter AE, Rees P, Love J. Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry. THE NEW PHYTOLOGIST 2023; 240:1305-1326. [PMID: 37678361 PMCID: PMC10594409 DOI: 10.1111/nph.19186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/30/2023] [Indexed: 09/09/2023]
Abstract
Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.
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Affiliation(s)
- Claire M. Barnes
- College of Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, UK
| | - Ann L. Power
- Biosciences, Faculty of Life and Health Sciences, University of Exeter, Exeter EX4 4QD, UK
| | - Daniel G. Barber
- Geography, Faculty of Environment, Science and Economics, University of Exeter, Exeter EX4 4RJ, UK
| | - Richard K. Tennant
- Geography, Faculty of Environment, Science and Economics, University of Exeter, Exeter EX4 4RJ, UK
| | | | - G. Rob Lee
- Biosciences, Faculty of Life and Health Sciences, University of Exeter, Exeter EX4 4QD, UK
| | - Jackie Hatton
- Geography, Faculty of Environment, Science and Economics, University of Exeter, Exeter EX4 4RJ, UK
| | - Angela Elliott
- Geography, Faculty of Environment, Science and Economics, University of Exeter, Exeter EX4 4RJ, UK
| | - Joana Zaragoza-Castells
- Geography, Faculty of Environment, Science and Economics, University of Exeter, Exeter EX4 4RJ, UK
| | - Stephen M. Haley
- Geography, Faculty of Environment, Science and Economics, University of Exeter, Exeter EX4 4RJ, UK
| | - Huw D. Summers
- College of Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, UK
| | - Minh Doan
- Bioimaging Analytics, GlaxoSmithKline, Collegeville, Upper Providence, PA 19426, United States
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA 02142, United States
| | - Paul Rees
- College of Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, UK
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA 02142, United States
| | - John Love
- Biosciences, Faculty of Life and Health Sciences, University of Exeter, Exeter EX4 4QD, UK
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8
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Pozzi P, Candeo A, Paiè P, Bragheri F, Bassi A. Artificial intelligence in imaging flow cytometry. FRONTIERS IN BIOINFORMATICS 2023; 3:1229052. [PMID: 37877042 PMCID: PMC10593470 DOI: 10.3389/fbinf.2023.1229052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/11/2023] [Indexed: 10/26/2023] Open
Affiliation(s)
- Paolo Pozzi
- Department of Physics, Politecnico di Milano, Milano, Italy
| | - Alessia Candeo
- Department of Physics, Politecnico di Milano, Milano, Italy
| | - Petra Paiè
- Department of Physics, Politecnico di Milano, Milano, Italy
| | - Francesca Bragheri
- Institute for Photonics and Nanotechnologies, Consiglio Nazionale delle Ricerche, Milano, Italy
| | - Andrea Bassi
- Department of Physics, Politecnico di Milano, Milano, Italy
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9
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Timonen VA, Kerkelä E, Impola U, Penna L, Partanen J, Kilpivaara O, Arvas M, Pitkänen E. DeepIFC: Virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning. Cytometry A 2023; 103:807-817. [PMID: 37276178 DOI: 10.1002/cyto.a.24770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 05/16/2023] [Accepted: 06/02/2023] [Indexed: 06/07/2023]
Abstract
Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.
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Affiliation(s)
- Veera A Timonen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Erja Kerkelä
- Advanced Cell Therapy Centre, Finnish Red Cross Blood Service, Vantaa, Finland
| | - Ulla Impola
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Leena Penna
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Outi Kilpivaara
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Medical and Clinical Genetics, Medicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HUSLAB Laboratory of Genetics, HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Mikko Arvas
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
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10
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Foy BH, Stefely JA, Bendapudi PK, Hasserjian RP, Al-Samkari H, Louissaint A, Fitzpatrick MJ, Hutchison B, Mow C, Collins J, Patel HR, Patel CH, Patel N, Ho SN, Kaufman RM, Dzik WH, Higgins JM, Makar RS. Computer vision quantitation of erythrocyte shape abnormalities provides diagnostic, prognostic, and mechanistic insight. Blood Adv 2023; 7:4621-4630. [PMID: 37146262 PMCID: PMC10448422 DOI: 10.1182/bloodadvances.2022008967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 04/07/2023] [Accepted: 04/10/2023] [Indexed: 05/07/2023] Open
Abstract
Examination of red blood cell (RBC) morphology in peripheral blood smears can help diagnose hematologic diseases, even in resource-limited settings, but this analysis remains subjective and semiquantitative with low throughput. Prior attempts to develop automated tools have been hampered by their poor reproducibility and limited clinical validation. Here, we present a novel, open-source machine-learning approach (denoted as RBC-diff) to quantify abnormal RBCs in peripheral smear images and generate an RBC morphology differential. RBC-diff cell counts showed high accuracy for single-cell classification (mean AUC, 0.93) and quantitation across smears (mean R2, 0.76 compared with experts, interexperts R2, 0.75). RBC-diff counts were concordant with the clinical morphology grading for 300 000+ images and recovered the expected pathophysiologic signals in diverse clinical cohorts. Criteria using RBC-diff counts distinguished thrombotic thrombocytopenic purpura and hemolytic uremic syndrome from other thrombotic microangiopathies, providing greater specificity than clinical morphology grading (72% vs 41%; P < .001) while maintaining high sensitivity (94% to 100%). Elevated RBC-diff schistocyte counts were associated with increased 6-month all-cause mortality in a cohort of 58 950 inpatients (9.5% mortality for schist. >1%, vs 4.7% for schist; <0.5%; P < .001) after controlling for comorbidities, demographics, clinical morphology grading, and blood count indices. RBC-diff also enabled the estimation of single-cell volume-morphology distributions, providing insight into the influence of morphology on routine blood count measures. Our codebase and expert-annotated images are included here to spur further advancement. These results illustrate that computer vision can enable rapid and accurate quantitation of RBC morphology, which may provide value in both clinical and research contexts.
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Affiliation(s)
- Brody H. Foy
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
| | - Jonathan A. Stefely
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Blood Transfusion Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Pavan K. Bendapudi
- Blood Transfusion Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Division of Hematology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Robert P. Hasserjian
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Hanny Al-Samkari
- Division of Hematology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Abner Louissaint
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Megan J. Fitzpatrick
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Bailey Hutchison
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Christopher Mow
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Mass General Brigham Enterprise Research IS, Boston, MA
| | - Julia Collins
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Hasmukh R. Patel
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Chhaya H. Patel
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Nikita Patel
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Samantha N. Ho
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Richard M. Kaufman
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Walter H. Dzik
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Blood Transfusion Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - John M. Higgins
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
| | - Robert S. Makar
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Blood Transfusion Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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11
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Isiksacan Z, D’Alessandro A, Wolf SM, McKenna DH, Tessier SN, Kucukal E, Gokaltun AA, William N, Sandlin RD, Bischof J, Mohandas N, Busch MP, Elbuken C, Gurkan UA, Toner M, Acker JP, Yarmush ML, Usta OB. Assessment of stored red blood cells through lab-on-a-chip technologies for precision transfusion medicine. Proc Natl Acad Sci U S A 2023; 120:e2115616120. [PMID: 37494421 PMCID: PMC10410732 DOI: 10.1073/pnas.2115616120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023] Open
Abstract
Transfusion of red blood cells (RBCs) is one of the most valuable and widespread treatments in modern medicine. Lifesaving RBC transfusions are facilitated by the cold storage of RBC units in blood banks worldwide. Currently, RBC storage and subsequent transfusion practices are performed using simplistic workflows. More specifically, most blood banks follow the "first-in-first-out" principle to avoid wastage, whereas most healthcare providers prefer the "last-in-first-out" approach simply favoring chronologically younger RBCs. Neither approach addresses recent advances through -omics showing that stored RBC quality is highly variable depending on donor-, time-, and processing-specific factors. Thus, it is time to rethink our workflows in transfusion medicine taking advantage of novel technologies to perform RBC quality assessment. We imagine a future where lab-on-a-chip technologies utilize novel predictive markers of RBC quality identified by -omics and machine learning to usher in a new era of safer and precise transfusion medicine.
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Affiliation(s)
- Ziya Isiksacan
- Center for Engineering in Medicine and Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
| | - Angelo D’Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver – Anschutz Medical Campus, Aurora, CO80045
| | - Susan M. Wolf
- Law School, Medical School, Consortium on Law and Values in Health, Environment & the Life Sciences, University of Minnesota, Minneapolis, MN55455
| | - David H. McKenna
- Division of Transfusion Medicine, Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN55455
| | - Shannon N. Tessier
- Center for Engineering in Medicine and Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
| | | | - A. Aslihan Gokaltun
- Center for Engineering in Medicine and Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
- Department of Chemical Engineering, Hacettepe University, Ankara06532, Turkey
| | - Nishaka William
- Laboratory Medicine and Pathology, University of Alberta, Edmonton, ABT6G 2R8, Canada
| | - Rebecca D. Sandlin
- Center for Engineering in Medicine and Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
| | - John Bischof
- Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN55455
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN55455
| | | | - Michael P. Busch
- Vitalant Research Institute, San Francisco, CA94105
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA94105
| | - Caglar Elbuken
- Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center, Bilkent University, Ankara06800, Turkey
- Faculty of Biochemistry and Molecular Medicine, Faculty of Medicine, University of Oulu, 90014Oulu, Finland
- Valtion Teknillinen Tutkimuskeskus Technical Research Centre of Finland Ltd., 90570Oulu, Finland
| | - Umut A. Gurkan
- Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH44106
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH44106
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH44106
| | - Mehmet Toner
- Center for Engineering in Medicine and Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
| | - Jason P. Acker
- Laboratory Medicine and Pathology, University of Alberta, Edmonton, ABT6G 2R8, Canada
- Innovation and Portfolio Management, Canadian Blood Services, Edmonton, ABT6G 2R8, Canada
| | - Martin L. Yarmush
- Center for Engineering in Medicine and Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ08854
| | - O. Berk Usta
- Center for Engineering in Medicine and Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA02114
- Shriners Children’s, Boston, MA02114
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12
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Bergaglio T, Bhattacharya S, Thompson D, Nirmalraj PN. Label-Free Digital Holotomography Reveals Ibuprofen-Induced Morphological Changes to Red Blood Cells. ACS NANOSCIENCE AU 2023; 3:241-255. [PMID: 37360843 PMCID: PMC10288613 DOI: 10.1021/acsnanoscienceau.3c00004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/21/2023] [Accepted: 03/21/2023] [Indexed: 06/28/2023]
Abstract
Understanding the dose-dependent effect of over-the-counter drugs on red blood cells (RBCs) is crucial for hematology and digital pathology. Yet, it is challenging to continuously record the real-time, drug-induced shape changes of RBCs in a label-free manner. Here, we demonstrate digital holotomography (DHTM)-enabled real-time, label-free concentration-dependent and time-dependent monitoring of ibuprofen on RBCs from a healthy donor. The RBCs are segmented based on three-dimensional (3D) and four-dimensional (4D) refractive index tomograms, and their morphological and chemical parameters are retrieved with their shapes classified using machine learning. We directly observed the formation and motion of spicules on the RBC membrane when aqueous solutions of ibuprofen were drop-cast on wet blood, creating rough-membraned echinocyte forms. At low concentrations of 0.25-0.50 mM, the ibuprofen-induced morphological change was transient, but at high concentrations (1-3 mM) the spiculated RBC remained over a period of up to 1.5 h. Molecular simulations confirmed that aggregates of ibuprofen molecules at high concentrations significantly disrupted the RBC membrane structural integrity and lipid order but produced negligible effect at low ibuprofen concentrations. Control experiments on the effect of urea, hydrogen peroxide, and aqueous solutions on RBCs showed zero spicule formation. Our work clarifies the dose-dependent chemical effects on RBCs using label-free microscopes that can be deployed for the rapid detection of overdosage of over-the-counter and prescribed drugs.
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Affiliation(s)
- Talia Bergaglio
- Transport
at Nanoscale Interfaces Laboratory, Swiss
Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
- Graduate
School for Cellular and Biomedical Sciences, University of Bern, Bern CH-3012, Switzerland
| | - Shayon Bhattacharya
- Department
of Physics, Bernal Institute, University
of Limerick, Limerick V94T9PX, Ireland
| | - Damien Thompson
- Department
of Physics, Bernal Institute, University
of Limerick, Limerick V94T9PX, Ireland
| | - Peter Niraj Nirmalraj
- Transport
at Nanoscale Interfaces Laboratory, Swiss
Federal Laboratories for Materials Science and Technology, Dübendorf CH-8600, Switzerland
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13
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D'Alessandro A. Red Blood Cell Omics and Machine Learning in Transfusion Medicine: Singularity Is Near. Transfus Med Hemother 2023; 50:174-183. [PMID: 37434999 PMCID: PMC10331163 DOI: 10.1159/000529744] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/14/2023] [Indexed: 07/30/2023] Open
Abstract
Background Blood transfusion is a life-saving intervention for millions of recipients worldwide. Over the last 15 years, the advent of high-throughput, affordable omics technologies - including genomics, proteomics, lipidomics, and metabolomics - has allowed transfusion medicine to revisit the biology of blood donors, stored blood products, and transfusion recipients. Summary Omics approaches have shed light on the genetic and non-genetic factors (environmental or other exposures) impacting the quality of stored blood products and efficacy of transfusion events, based on the current Food and Drug Administration guidelines (e.g., hemolysis and post-transfusion recovery for stored red blood cells). As a treasure trove of data accumulates, the implementation of machine learning approaches promises to revolutionize the field of transfusion medicine, not only by advancing basic science. Indeed, computational strategies have already been used to perform high-content screenings of red blood cell morphology in microfluidic devices, generate in silico models of erythrocyte membrane to predict deformability and bending rigidity, or design systems biology maps of the red blood cell metabolome to drive the development of novel storage additives. Key Message In the near future, high-throughput testing of donor genomes via precision transfusion medicine arrays and metabolomics of all donated products will be able to inform the development and implementation of machine learning strategies that match, from vein to vein, donors, optimal processing strategies (additives, shelf life), and recipients, realizing the promise of personalized transfusion medicine.
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Affiliation(s)
- Angelo D'Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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14
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Yang Y, He H, Wang J, Chen L, Xu Y, Ge C, Li S. Blood quality evaluation via on-chip classification of cell morphology using a deep learning algorithm. LAB ON A CHIP 2023; 23:2113-2121. [PMID: 36946151 DOI: 10.1039/d2lc01078j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
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.
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Affiliation(s)
- Yuping Yang
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education & Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
- Chongqing College of Electronic Engineering, Chongqing 401331, China
| | - Hong He
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education & Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
| | - Junju Wang
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education & Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
| | - Li Chen
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education & Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
| | - Yi Xu
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education & Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
| | - Chuang Ge
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Shunbo Li
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education & Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
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15
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Yang Y, Xu F, Chen J, Tao C, Li Y, Chen Q, Tang S, Lee HK, Shen W. Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review. Biosens Bioelectron 2023; 229:115233. [PMID: 36965381 DOI: 10.1016/j.bios.2023.115233] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/23/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023]
Abstract
Artificial intelligence (AI) has received great attention since the concept was proposed, and it has developed rapidly in recent years with applications in many fields. Meanwhile, newer iterations of smartphone hardware technologies which have excellent data processing capabilities have leveraged on AI capabilities. Based on the desirability for portable detection, researchers have been investigating intelligent analysis by combining smartphones with AI algorithms. Various examples of the application of AI algorithm-based smartphone detection and analysis have been developed. In this review, we give an overview of this field, with a particular focus on bioanalytical detection applications. The applications are presented in terms of hardware design, software algorithms, and specific application areas. We also discuss the existing limitations of AI-based smartphone detection and analytical approaches, and their future prospects. The take-home message of our review is that the application of AI in the field of detection analysis is restricted by the limitations of the smartphone's hardware as well as the model building of AI for detection targets with insufficient data. Nevertheless, at this juncture, while bioanalytical diagnostics and health monitoring have set the pace for AI-based smartphone applicability, the future should see the technology making greater inroads into other fields. In relation to the latter, it is likely that the ordinary or average person will play a greater participatory role.
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Affiliation(s)
- Yizhuo Yang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Fang Xu
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Jisen Chen
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Chunxu Tao
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Yunxin Li
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, 361021, Fujian Province, China
| | - Sheng Tang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
| | - Hian Kee Lee
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China; Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore.
| | - Wei Shen
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
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16
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Siu DMD, Lee KCM, Chung BMF, Wong JSJ, Zheng G, Tsia KK. Optofluidic imaging meets deep learning: from merging to emerging. LAB ON A CHIP 2023; 23:1011-1033. [PMID: 36601812 DOI: 10.1039/d2lc00813k] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.
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Affiliation(s)
- Dickson M D Siu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Bob M F Chung
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Justin S J Wong
- Conzeb Limited, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Guoan Zheng
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
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17
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Zinchenko V, Hugger J, Uhlmann V, Arendt D, Kreshuk A. MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy. eLife 2023; 12:80918. [PMID: 36795088 PMCID: PMC9934868 DOI: 10.7554/elife.80918] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 01/06/2023] [Indexed: 02/17/2023] Open
Abstract
Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes are now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii, it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organisation of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources.
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Affiliation(s)
- Valentyna Zinchenko
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL)HeidelbergGermany
| | - Johannes Hugger
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL)CambridgeUnited Kingdom
| | - Virginie Uhlmann
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL)CambridgeUnited Kingdom
| | - Detlev Arendt
- Developmental Biology Unit, European Molecular Biology Laboratory (EMBL)HeidelbergGermany
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL)HeidelbergGermany
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18
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Wu HY, Li ZG, Sun XK, Bai WM, Wang AD, Ma YC, Diao RH, Fan EY, Zhao F, Liu YQ, Hong YZ, Guo MH, Xue H, Liang WB. Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks. Sci Rep 2022; 12:19165. [PMID: 36357435 PMCID: PMC9647248 DOI: 10.1038/s41598-022-21215-2] [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: 05/04/2022] [Accepted: 09/23/2022] [Indexed: 11/11/2022] Open
Abstract
Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.
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Affiliation(s)
- Hong-yun Wu
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Zheng-gang Li
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Xin-kai Sun
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Wei-min Bai
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - An-di Wang
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Yu-chi Ma
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Ren-hua Diao
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Eng-yong Fan
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Fang Zhao
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Yun-qi Liu
- grid.499290.f0000 0004 6026 514XNanjing Foreign Language School, Nanjing, Jiangsu People’s Republic of China
| | - Yi-zhou Hong
- grid.499290.f0000 0004 6026 514XNanjing Foreign Language School, Nanjing, Jiangsu People’s Republic of China
| | - Ming-hua Guo
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Hui Xue
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Wen-biao Liang
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
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19
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Data augmentation with improved regularisation and sampling for imbalanced blood cell image classification. Sci Rep 2022; 12:18101. [PMID: 36302948 PMCID: PMC9613648 DOI: 10.1038/s41598-022-22882-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 10/20/2022] [Indexed: 12/30/2022] Open
Abstract
Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are more efficient. Deep learning and more specifically Convolutional Neural Networks have achieved state-of-the-art performance on various biomedical image classification problems. However, real-world data often suffers from the data imbalance problem, owing to which the trained classifier is biased towards the majority classes and does not perform well on the minority classes. This study presents an imbalanced blood cells classification method that utilises Wasserstein divergence GAN, mixup and novel nonlinear mixup for data augmentation to achieve oversampling of the minority classes. We also present a minority class focussed sampling strategy, which allows effective representation of minority class samples produced by all three data augmentation techniques and contributes to the classification performance. The method was evaluated on two publicly available datasets of immortalised human T-lymphocyte cells and Red Blood Cells. Classification performance evaluated using F1-score shows that our proposed approach outperforms existing methods on the same datasets.
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20
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Stolarek I, Samelak-Czajka A, Figlerowicz M, Jackowiak P. Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data. iScience 2022; 25:105142. [PMID: 36193047 PMCID: PMC9526149 DOI: 10.1016/j.isci.2022.105142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/29/2022] [Accepted: 09/09/2022] [Indexed: 11/27/2022] Open
Abstract
Recent advances in imaging flow cytometry (IFC) have revolutionized high-throughput multiparameter analyses at single-cell resolution. Although enabling the discovery of population heterogeneities and the detection of rare events, IFC generates hyperdimensional datasets that demand innovative analytical approaches. Current methods work in a supervised manner, utilize only limited information content, or require large annotated reference datasets. Dimensionality reduction algorithms, including uniform manifold approximation and projection (UMAP), have been successfully applied to analyze the large number of parameters generated in various high-throughput techniques. Here, we apply a workflow incorporating UMAP to analyze different IFC datasets. We demonstrate that it out-competes other popular dimensionality reduction methods in speed and accuracy. Moreover, it enables fast visualization, clustering, and tagging of unannotated objects in large-scale experiments. We anticipate that our workflow will be a robust method to address complex IFC datasets, either alone or as an upstream addition to the deep learning approaches. UMAP dimensionality reduction provides fast and accurate method of IFC data analysis UMAP yields improved object clustering and tagging of the multispectral IFC data PCA decomposition allows multispectral signals merging for direct image embedding
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Affiliation(s)
- Ireneusz Stolarek
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Anna Samelak-Czajka
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Marek Figlerowicz
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Paulina Jackowiak
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
- Corresponding author
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21
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Demagny J, Roussel C, Le Guyader M, Guiheneuf E, Harrivel V, Boyer T, Diouf M, Dussiot M, Demont Y, Garçon L. Combining imaging flow cytometry and machine learning for high-throughput schistocyte quantification: A SVM classifier development and external validation cohort. EBioMedicine 2022; 83:104209. [PMID: 35986949 PMCID: PMC9404284 DOI: 10.1016/j.ebiom.2022.104209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 11/29/2022] Open
Abstract
Background Schistocyte counts are a cornerstone of the diagnosis of thrombotic microangiopathy syndrome (TMA). Their manual quantification is complex and alternative automated methods suffer from pitfalls that limit their use. We report a method combining imaging flow cytometry (IFC) and artificial intelligence for the direct label-free and operator-independent quantification of schistocytes in whole blood. Methods We used 135,045 IFC images from blood acquisition among 14 patients to extract 188 features with IDEAS® software and 128 features from a convolutional neural network (CNN) with Keras framework in order to train a support vector machine (SVM) blood elements’ classifier used for schistocytes quantification. Finding Keras features showed better accuracy (94.03%, CI: 93.75-94.31%) than ideas features (91.54%, CI: 91.21-91.87%) in recognising whole-blood elements, and together they showed the best accuracy (95.64%, CI: 95.39-95.88%). We obtained an excellent correlation (0.93, CI: 0.90-0.96) between three haematologists and our method on a cohort of 102 patient samples. All patients with schistocytosis (>1% schistocytes) were detected with excellent specificity (91.3%, CI: 82.0-96.7%) and sensitivity (100%, CI: 89.4-100.0%). We confirmed these results with a similar specificity (91.1%, CI: 78.8-97.5%) and sensitivity (100%, CI: 88.1-100.0%) on a validation cohort (n=74) analysed in an independent healthcare centre. Simultaneous analysis of 16 samples in both study centres showed a very good correlation between the 2 imaging flow cytometers (Y=1.001x). Interpretation We demonstrate that IFC can represent a reliable tool for operator-independent schistocyte quantification with no pre-analytical processing which is of most importance in emergency situations such as TMA. Funding None.
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22
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Recktenwald SM, Lopes MGM, Peter S, Hof S, Simionato G, Peikert K, Hermann A, Danek A, van Bentum K, Eichler H, Wagner C, Quint S, Kaestner L. Erysense, a Lab-on-a-Chip-Based Point-of-Care Device to Evaluate Red Blood Cell Flow Properties With Multiple Clinical Applications. Front Physiol 2022; 13:884690. [PMID: 35574449 PMCID: PMC9091344 DOI: 10.3389/fphys.2022.884690] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/11/2022] [Indexed: 11/18/2022] Open
Abstract
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.
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Affiliation(s)
| | - Marcelle G. M. Lopes
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Cysmic GmbH, Saarbruecken, Germany
| | - Stephana Peter
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Theoretical Medicine and Biosciences, Saarland University, Saarbruecken, Germany
| | - Sebastian Hof
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Theoretical Medicine and Biosciences, Saarland University, Saarbruecken, Germany
| | - Greta Simionato
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Institute for Clinical and Experimental Surgery, Saarland University, Campus University Hospital, Homburg, Germany
| | - Kevin Peikert
- Translational Neurodegeneration Section “Albrecht-Kossel”, Department of Neurology, University Medical Center Rostock, University of Rostock, Rostock, Germany
| | - Andreas Hermann
- Translational Neurodegeneration Section “Albrecht-Kossel”, Department of Neurology, University Medical Center Rostock, University of Rostock, Rostock, Germany
- DZNE, Deutsches Zentrum für Neurodegenerative Erkrankungen, Research Site Rostock/Greifswald, Rostock, Germany
- Center for Transdisciplinary Neurosciences Rostock (CTNR), University Medical Center Rostock, University of Rostock, Rostock, Germany
| | - Adrian Danek
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-University, Munich, Germany
| | | | - Hermann Eichler
- Institute for Clinical Hemostaseology and Transfusion Medicine, Saarland University and Saarland University Hospital, Homburg, Germany
| | - Christian Wagner
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Department of Physics and Materials Science, University of Luxembourg, Luxembourg City, Luxembourg
| | - Stephan Quint
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Cysmic GmbH, Saarbruecken, Germany
| | - Lars Kaestner
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Theoretical Medicine and Biosciences, Saarland University, Saarbruecken, Germany
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23
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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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24
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Marin M, Peltier S, Hadjou Y, Georgeault S, Dussiot M, Roussel C, Hermine O, Roingeard P, Buffet PA, Amireault P. Storage-Induced Micro-Erythrocytes Can Be Quantified and Sorted by Flow Cytometry. Front Physiol 2022; 13:838138. [PMID: 35283784 PMCID: PMC8906515 DOI: 10.3389/fphys.2022.838138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Refrigerated storage of red cell concentrates before transfusion is associated with progressive alterations of red blood cells (RBC). Small RBC (type III echinocytes, sphero-echinocytes, and spherocytes) defined as storage-induced micro-erythrocytes (SME) appear during pretransfusion storage. SME accumulate with variable intensity from donor to donor, are cleared rapidly after transfusion, and their proportion correlates with transfusion recovery. They can be rapidly and objectively quantified using imaging flow cytometry (IFC). Quantifying SME using flow cytometry would further facilitate a physiologically relevant quality control of red cell concentrates. RBC stored in blood bank conditions were stained with a carboxyfluorescein succinimidyl ester (CFSE) dye and incubated at 37°C. CFSE intensity was assessed by flow cytometry and RBC morphology evaluated by IFC. We observed the accumulation of a CFSE high RBC subpopulation by flow cytometry that accounted for 3.3 and 47.2% at day 3 and 42 of storage, respectively. IFC brightfield images showed that this CFSE high subpopulation mostly contains SME while the CFSE low subpopulation mostly contains type I and II echinocytes and discocytes. Similar numbers of SME were quantified by IFC (based on projected surface area) and by flow cytometry (based on CFSE intensity). IFC and scanning electron microscopy showed that ≥95% pure subpopulations of CFSE high and CFSE low RBC were obtained by flow cytometry-based sorting. SME can now be quantified using a common fluorescent dye and a standard flow cytometer. The staining protocol enables specific sorting of SME, a useful tool to further characterize this RBC subpopulation targeted for premature clearance after transfusion.
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Affiliation(s)
- Mickaël Marin
- INSERM, BIGR, Université de Paris and Université des Antilles, Paris, France.,Institut National de la Transfusion Sanguine, Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France
| | - Sandy Peltier
- INSERM, BIGR, Université de Paris and Université des Antilles, Paris, France.,Institut National de la Transfusion Sanguine, Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France
| | - Youcef Hadjou
- INSERM, BIGR, Université de Paris and Université des Antilles, Paris, France.,Institut National de la Transfusion Sanguine, Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France
| | - Sonia Georgeault
- Plateforme des Microscopies, Infrastructures de Recherche en Biologie Santé et Agronomie, Programme Pluriformation Analyse des Systèmes Biologiques, Tours, France
| | - Michaël Dussiot
- Laboratoire d'Excellence GR-Ex, Paris, France.,U1163, Laboratory of Cellular and Molecular Mechanisms of Hematological Disorders and Therapeutic Implications, INSERM, Université de Paris, Paris, France
| | - Camille Roussel
- INSERM, BIGR, Université de Paris and Université des Antilles, Paris, France.,Institut National de la Transfusion Sanguine, Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France.,AP-HP, Laboratoire d'Hématologie, Hôpital Necker-Enfants Malades, Paris, France
| | - Olivier Hermine
- Laboratoire d'Excellence GR-Ex, Paris, France.,U1163, Laboratory of Cellular and Molecular Mechanisms of Hematological Disorders and Therapeutic Implications, INSERM, Université de Paris, Paris, France.,Département d'Hématologie, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Philippe Roingeard
- Plateforme des Microscopies, Infrastructures de Recherche en Biologie Santé et Agronomie, Programme Pluriformation Analyse des Systèmes Biologiques, Tours, France.,U1259, Centre Hospitalier Régional Universitaire de Tours, Morphogenèse et Antigénicité du VIH et des Virus des Hépatites, INSERM, Université de Tours, Tours, France
| | - Pierre A Buffet
- INSERM, BIGR, Université de Paris and Université des Antilles, Paris, France.,Institut National de la Transfusion Sanguine, Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France.,AP-HP, Paris, France
| | - Pascal Amireault
- INSERM, BIGR, Université de Paris and Université des Antilles, Paris, France.,Institut National de la Transfusion Sanguine, Paris, France.,Laboratoire d'Excellence GR-Ex, Paris, France.,U1163, Laboratory of Cellular and Molecular Mechanisms of Hematological Disorders and Therapeutic Implications, INSERM, Université de Paris, Paris, France
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25
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AIM in Haematology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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26
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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: 13] [Impact Index Per Article: 4.3] [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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27
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Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood 2021; 138:1917-1927. [PMID: 34792573 PMCID: PMC8602932 DOI: 10.1182/blood.2020010568] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 07/04/2021] [Indexed: 12/15/2022] Open
Abstract
A data set of >170 000 microscopic images allows training neural networks for identification of BM cells with high accuracy. Neural networks outperform a feature-based approach to BM cell classification and can be analyzed with explainability and feature embedding methods.
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence–based approaches to BM cytomorphology.
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28
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Mochalova EN, Kotov IA, Lifanov DA, Chakraborti S, Nikitin MP. Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis. Biotechnol Bioeng 2021; 119:626-635. [PMID: 34750809 DOI: 10.1002/bit.27986] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 10/07/2021] [Accepted: 10/28/2021] [Indexed: 01/03/2023]
Abstract
Macrophages play an important role in the adaptive immune system. Their ability to neutralize cellular targets through Fc receptor-mediated phagocytosis has relied upon immunotherapy that has become of particular interest for the treatment of cancer and autoimmune diseases. A detailed investigation of phagocytosis is the key to the improvement of the therapeutic efficiency of existing medications and the creation of new ones. A promising method for studying the process is imaging flow cytometry (IFC) that acquires thousands of cell images per second in up to 12 optical channels and allows multiparametric fluorescent and morphological analysis of samples in the flow. However, conventional IFC data analysis approaches are based on a highly subjective manual choice of masks and other processing parameters that can lead to the loss of valuable information embedded in the original image. Here, we show the application of a Faster region-based convolutional neural network (CNN) for accurate quantitative analysis of phagocytosis using imaging flow cytometry data. Phagocytosis of erythrocytes by peritoneal macrophages was chosen as a model system. CNN performed automatic high-throughput processing of datasets and demonstrated impressive results in the identification and classification of macrophages and erythrocytes, despite the variety of shapes, sizes, intensities, and textures of cells in images. The developed procedure allows determining the number of phagocytosed cells, disregarding cases with a low probability of correct classification. We believe that CNN-based approaches will enable powerful in-depth investigation of a wide range of biological processes and will reveal the intricate nature of heterogeneous objects in images, leading to completely new capabilities in diagnostics and therapy.
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Affiliation(s)
- Elizaveta N Mochalova
- Nanobiotechnology Laboratory, Moscow Institute of Physics and Technology, Moscow, Russia.,Biophotonics Laboratory, Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia.,Nanobiomedicine Division, Sirius University of Science and Technology, Sochi, Russia
| | - Ivan A Kotov
- Nanobiotechnology Laboratory, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Dmitry A Lifanov
- Nanobiotechnology Laboratory, Moscow Institute of Physics and Technology, Moscow, Russia
| | | | - Maxim P Nikitin
- Nanobiotechnology Laboratory, Moscow Institute of Physics and Technology, Moscow, Russia.,Nanobiomedicine Division, Sirius University of Science and Technology, Sochi, Russia
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29
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Summers HD. Practical machine learning for disease diagnosis. CELL REPORTS METHODS 2021; 1:100103. [PMID: 35474900 PMCID: PMC9017117 DOI: 10.1016/j.crmeth.2021.100103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.
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Affiliation(s)
- Huw D. Summers
- Department of Biomedical Engineering, Swansea University, Swansea, UK
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30
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Otesteanu CF, Ugrinic M, Holzner G, Chang YT, Fassnacht C, Guenova E, Stavrakis S, deMello A, Claassen M. A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics. CELL REPORTS METHODS 2021; 1:100094. [PMID: 35474892 PMCID: PMC9017143 DOI: 10.1016/j.crmeth.2021.100094] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/21/2021] [Accepted: 09/14/2021] [Indexed: 12/21/2022]
Abstract
The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations.
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Affiliation(s)
- Corin F. Otesteanu
- Institute for Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Martina Ugrinic
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Gregor Holzner
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Yun-Tsan Chang
- Department of Dermatology, University Hospital Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Christina Fassnacht
- Department of Dermatology, University Hospital Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Emmanuella Guenova
- Department of Dermatology, University Hospital Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Andrew deMello
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Manfred Claassen
- Internal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany
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31
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Hallou A, Yevick HG, Dumitrascu B, Uhlmann V. Deep learning for bioimage analysis in developmental biology. Development 2021; 148:dev199616. [PMID: 34490888 PMCID: PMC8451066 DOI: 10.1242/dev.199616] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.
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Affiliation(s)
- Adrien Hallou
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK
- Wellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Hannah G. Yevick
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA
| | - Bianca Dumitrascu
- Computer Laboratory, Cambridge, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Virginie Uhlmann
- European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK
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32
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Stirling DR, Carpenter AE, Cimini BA. CellProfiler Analyst 3.0: Accessible data exploration and machine learning for image analysis. Bioinformatics 2021; 37:3992-3994. [PMID: 34478488 DOI: 10.1093/bioinformatics/btab634] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 08/27/2021] [Accepted: 09/01/2021] [Indexed: 11/14/2022] Open
Abstract
Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualisation tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses. AVAILABILITY CellProfiler Analyst binaries for Windows and MacOS are freely available for download at https://cellprofileranalyst.org/. Source code is implemented in Python 3 and is available at https://github.com/CellProfiler/CellProfiler-Analyst/. A sample data set is available at https://cellprofileranalyst.org/examples, based on images freely available from the Broad Bioimage Benchmark Collection (BBBC).
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Affiliation(s)
- David R Stirling
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA
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Kim E, Park S, Hwang S, Moon I, Javidi B. Deep Learning-based Phenotypic Assessment of Red Cell Storage Lesions for Safe Transfusions. IEEE J Biomed Health Inform 2021; 26:1318-1328. [PMID: 34388103 DOI: 10.1109/jbhi.2021.3104650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This study presents a novel approach to automatically perform instant phenotypic assessment of red blood cell (RBC) storage lesion in phase images obtained by digital holographic microscopy. The proposed model combines a generative adversarial network (GAN) with marker-controlled watershed segmentation scheme. The GAN model performed RBC segmentations and classifications to develop ageing markers, and the watershed segmentation was used to completely separate overlapping RBCs. Our approach achieved good segmentation and classification accuracy with a Dices coefficient of 0.94 at a high throughput rate of about 152 cells per second. These results were compared with other deep neural network architectures. Moreover, our image-based deep learning models recognized the morphological changes that occur in RBCs during storage. Our deep learning-based classification results were in good agreement with previous findings on the changes in RBC markers (dominant shapes) affected by storage duration. We believe that our image-based deep learning models can be useful for automated assessment of RBC quality, storage lesions for safe transfusions, and diagnosis of RBC-related diseases.
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Doan M, Barnes C, McQuin C, Caicedo JC, Goodman A, Carpenter AE, Rees P. Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry. Nat Protoc 2021; 16:3572-3595. [PMID: 34145434 PMCID: PMC8506936 DOI: 10.1038/s41596-021-00549-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 03/29/2021] [Indexed: 11/08/2022]
Abstract
Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.
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Affiliation(s)
- Minh Doan
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Bioimaging Analytics, GlaxoSmithKline, Collegeville, PA, USA.
| | - Claire Barnes
- College of Engineering, Swansea University, Bay Campus, Swansea, UK
| | - Claire McQuin
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Juan C Caicedo
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Allen Goodman
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Paul Rees
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- College of Engineering, Swansea University, Bay Campus, Swansea, UK.
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Sebastian JA, Moore MJ, Berndl ESL, Kolios MC. An image-based flow cytometric approach to the assessment of the nucleus-to-cytoplasm ratio. PLoS One 2021; 16:e0253439. [PMID: 34166419 PMCID: PMC8224973 DOI: 10.1371/journal.pone.0253439] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 06/04/2021] [Indexed: 11/20/2022] Open
Abstract
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.
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Affiliation(s)
- Joseph A. Sebastian
- Department of Physics, Ryerson University, Toronto, Canada
- Institute of Biomedical Engineering, Science and Technology (iBEST), A Partnership Between Ryerson University and St. Michael’s Hospital, Toronto, Canada
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada
| | - Michael J. Moore
- Department of Physics, Ryerson University, Toronto, Canada
- Institute of Biomedical Engineering, Science and Technology (iBEST), A Partnership Between Ryerson University and St. Michael’s Hospital, Toronto, Canada
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada
| | - Elizabeth S. L. Berndl
- Department of Physics, Ryerson University, Toronto, Canada
- Institute of Biomedical Engineering, Science and Technology (iBEST), A Partnership Between Ryerson University and St. Michael’s Hospital, Toronto, Canada
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada
| | - Michael C. Kolios
- Department of Physics, Ryerson University, Toronto, Canada
- Institute of Biomedical Engineering, Science and Technology (iBEST), A Partnership Between Ryerson University and St. Michael’s Hospital, Toronto, Canada
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada
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Pratapa A, Doron M, Caicedo JC. Image-based cell phenotyping with deep learning. Curr Opin Chem Biol 2021; 65:9-17. [PMID: 34023800 DOI: 10.1016/j.cbpa.2021.04.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 04/10/2021] [Indexed: 12/25/2022]
Abstract
A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell phenotyping is the characterization and quantification of these observable cellular traits in images. Recently, cellular phenotyping has undergone a massive overhaul in terms of scale, resolution, and throughput, which is attributable to advances across electronic, optical, and chemical technologies for imaging cells. Coupled with the rapid acceleration of deep learning-based computational tools, these advances have opened up new avenues for innovation across a wide variety of high-throughput cell biology applications. Here, we review applications wherein deep learning is powering the recognition, profiling, and prediction of visual phenotypes to answer important biological questions. As the complexity and scale of imaging assays increase, deep learning offers computational solutions to elucidate the details of previously unexplored cellular phenotypes.
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Bardyn M, Allard J, Crettaz D, Rappaz B, Turcatti G, Tissot JD, Prudent M. Image- and Fluorescence-Based Test Shows Oxidant-Dependent Damages in Red Blood Cells and Enables Screening of Potential Protective Molecules. Int J Mol Sci 2021; 22:ijms22084293. [PMID: 33924276 PMCID: PMC8074894 DOI: 10.3390/ijms22084293] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 12/23/2022] Open
Abstract
An increase of oxygen saturation within blood bags and metabolic dysregulation occur during storage of red blood cells (RBCs). It leads to the gradual exhaustion of RBC antioxidant protective system and, consequently, to a deleterious state of oxidative stress that plays a major role in the apparition of the so-called storage lesions. The present study describes the use of a test (called TSOX) based on fluorescence and label-free morphology readouts to simply and quickly evaluate the oxidant and antioxidant properties of various compounds in controlled conditions. Here, TSOX was applied to RBCs treated with four antioxidants (ascorbic acid, uric acid, trolox and resveratrol) and three oxidants (AAPH, diamide and H2O2) at different concentrations. Two complementary readouts were chosen: first, where ROS generation was quantified using DCFH-DA fluorescent probe, and second, based on digital holographic microscopy that measures morphology alterations. All oxidants produced an increase of fluorescence, whereas H2O2 did not visibly impact the RBC morphology. Significant protection was observed in three out of four of the added molecules. Of note, resveratrol induced diamond-shape “Tirocytes”. The assay design was selected to be flexible, as well as compatible with high-throughput screening. In future experiments, the TSOX will serve to screen chemical libraries and probe molecules that could be added to the additive solution for RBCs storage.
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Affiliation(s)
- Manon Bardyn
- Laboratoire de Recherche sur les Produits Sanguins, Transfusion Interrégionale CRS, CH-1066 Epalinges, Switzerland
| | - Jérôme Allard
- Laboratoire de Recherche sur les Produits Sanguins, Transfusion Interrégionale CRS, CH-1066 Epalinges, Switzerland
- Département de Génie Chimique, École Polytechnique de Montréal, Montréal, QC H3C 3A7, Canada
| | - David Crettaz
- Laboratoire de Recherche sur les Produits Sanguins, Transfusion Interrégionale CRS, CH-1066 Epalinges, Switzerland
| | - Benjamin Rappaz
- Biomolecular Screening Facility (BSF), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Gerardo Turcatti
- Biomolecular Screening Facility (BSF), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Jean-Daniel Tissot
- Faculté de Biologie et de Médecine, Université de Lausanne, CH-1011 Lausanne, Switzerland
| | - Michel Prudent
- Laboratoire de Recherche sur les Produits Sanguins, Transfusion Interrégionale CRS, CH-1066 Epalinges, Switzerland
- Faculté de Biologie et de Médecine, Université de Lausanne, CH-1011 Lausanne, Switzerland
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, Institute of Pharmaceutical Sciences of Western Switzerland, University of Lausanne, CH-1011 Lausanne, Switzerland
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Xiang RF, Quinn JG, Watson S, Kumar-Misir A, Cheng C. Application of unsupervised machine learning to identify areas of blood product wastage in transfusion medicine. Vox Sang 2021; 116:955-964. [PMID: 33634887 DOI: 10.1111/vox.13089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND Wastage of blood products can be a significant cost to blood banks. However, the cause of wastage is often complex and makes it difficult to determine wastage-associated factors. Machine learning techniques may be useful tools to investigate these complex associations. We investigated whether unsupervised machine learning can identify patterns associated with wastage in our blood bank. MATERIALS AND METHODS Data on red blood cells, platelets and frozen products were obtained from the laboratory information system of the Central Zone Blood Transfusion Services at Nova Scotia Health Authority. A total of 879 532 transactions were analysed by association rule mining, a type of machine learning algorithm. Associations with lift scores greater than 25 and with clinical relevance were flagged for further examination. RESULTS Association rule mining returned a total of 3355 associations related to wastage. Several notable associations were identified. For example, certain wards were associated with wastage due to thawing unused frozen products. Other examples included association between smaller blood banks and evening work shifts with product wastage due to excess time outside the laboratory or returning products with high temperatures. CONCLUSION This paper demonstrates the effective use of unsupervised machine learning for the purpose of investigating wastage in a large blood bank. The use of association rule mining was able to identify wastage factors, which can help guide quality improvement initiatives. This technique can be automated to provide rapid analysis of complex associations contributing to wastage and could be utilized in modern blood banks.
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Affiliation(s)
- Richard F Xiang
- Nova Scotia Health Authority, Dalhousie University, Halifax, NS, Canada
| | - Jason G Quinn
- Nova Scotia Health Authority, Dalhousie University, Halifax, NS, Canada
| | - Stephanie Watson
- Nova Scotia Health Authority, Dalhousie University, Halifax, NS, Canada
| | | | - Calvino Cheng
- Nova Scotia Health Authority, Dalhousie University, Halifax, NS, Canada
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Chen L, Liu Y, Xu H, Ma L, Wang Y, Wang F, Zhu J, Hu X, Yi K, Yang Y, Shen H, Zhou F, Gao X, Cheng Y, Bai L, Duan Y, Wang F, Zhu Y. Touchable cell biophysics property recognition platforms enable multifunctional blood smart health care. MICROSYSTEMS & NANOENGINEERING 2021; 7:103. [PMID: 34963817 PMCID: PMC8651774 DOI: 10.1038/s41378-021-00329-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/25/2021] [Accepted: 11/06/2021] [Indexed: 05/10/2023]
Abstract
As a crucial biophysical property, red blood cell (RBC) deformability is pathologically altered in numerous disease states, and biochemical and structural changes occur over time in stored samples of otherwise normal RBCs. However, there is still a gap in applying it further to point-of-care blood devices due to the large external equipment (high-resolution microscope and microfluidic pump), associated operational difficulties, and professional analysis. Herein, we revolutionarily propose a smart optofluidic system to provide a differential diagnosis for blood testing via precise cell biophysics property recognition both mechanically and morphologically. Deformation of the RBC population is caused by pressing the hydrogel via an integrated mechanical transfer device. The biophysical properties of the cell population are obtained by the designed smartphone algorithm. Artificial intelligence-based modeling of cell biophysics properties related to blood diseases and quality was developed for online testing. We currently achieve 100% diagnostic accuracy for five typical clinical blood diseases (90 megaloblastic anemia, 78 myelofibrosis, 84 iron deficiency anemia, 48 thrombotic thrombocytopenic purpura, and 48 thalassemias) via real-world prospective implementation; furthermore, personalized blood quality (for transfusion in cardiac surgery) monitoring is achieved with an accuracy of 96.9%. This work suggests a potential basis for next-generation blood smart health care devices.
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Affiliation(s)
- Longfei Chen
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Yantong Liu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Hongshan Xu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Linlu Ma
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yifan Wang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Fang Wang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Jiaomeng Zhu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Xuejia Hu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Kezhen Yi
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yi Yang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Hui Shen
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Xiaoqi Gao
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Yanxiang Cheng
- Remin Hospital of Wuhan University, Wuhan University, Wuhan, 430060 China
| | - Long Bai
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310002 China
| | - Yongwei Duan
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yimin Zhu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310002 China
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40
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Davids J, Ashrafian H. AIM in Haematology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_182-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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41
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Rosenberg CA, Bill M, Rodrigues MA, Hauerslev M, Kerndrup GB, Hokland P, Ludvigsen M. Exploring dyserythropoiesis in patients with myelodysplastic syndrome by imaging flow cytometry and machine-learning assisted morphometrics. CYTOMETRY PART B-CLINICAL CYTOMETRY 2020; 100:554-567. [PMID: 33285035 DOI: 10.1002/cyto.b.21975] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/19/2020] [Accepted: 11/19/2020] [Indexed: 12/16/2022]
Abstract
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.
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Affiliation(s)
| | - Marie Bill
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Mathias Hauerslev
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark
| | - Gitte B Kerndrup
- Department of Pathology, Aarhus University Hospital, Aarhus, Denmark
| | - Peter Hokland
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Maja Ludvigsen
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Sebastian JA, Kolios MC, Acker JP. Emerging use of machine learning and advanced technologies to assess red cell quality. Transfus Apher Sci 2020; 59:103020. [PMID: 33246838 DOI: 10.1016/j.transci.2020.103020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
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.
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Affiliation(s)
- Joseph A Sebastian
- Institute of Biomedical Engineering, University of Toronto, 164 College St., Toronto, Ontario, M5S 3G9, Canada; Translational Biology and Engineering Program, Ted Rogers Center for Heart Research, 661 University Avenue, Toronto, ON, M5G 1X8, Canada.
| | - Michael C Kolios
- Department of Physics, Ryerson University, 350 Victoria St., Toronto, Ontario, M5B 2K3, Canada; Institute of Biomedical Engineering, Science and Technology (iBEST), A Partnership Between Ryerson University and St. Michael's Hospital, 209 Victoria St, Toronto, Ontario, M5B 1T8, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria St., Toronto, Ontario, M5B 1T8, Canada.
| | - Jason P Acker
- Centre for Innovation, Canadian Blood Services, 8249-114 St., Edmonton, Alberta, T6G 2R8, Canada; Department of Laboratory Medicine and Pathology, University of Alberta, 8249-114 St., Edmonton, Alberta, T6G 2R8, Canada.
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