iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays
Abstract While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporating phenomena such as fluid flow and dyna...
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-40522-4 |
| _version_ | 1827710097552310272 |
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| author | Meredith E. Fay Oluwamayokun Oshinowo Elizabeth Iffrig Kirby S. Fibben Christina Caruso Scott Hansen Jamie O. Musick José M. Valdez Sally S. Azer Robert G. Mannino Hyoann Choi Dan Y. Zhang Evelyn K. Williams Erica N. Evans Celeste K. Kanne Melissa L. Kemp Vivien A. Sheehan Marcus A. Carden Carolyn M. Bennett David K. Wood Wilbur A. Lam |
| author_facet | Meredith E. Fay Oluwamayokun Oshinowo Elizabeth Iffrig Kirby S. Fibben Christina Caruso Scott Hansen Jamie O. Musick José M. Valdez Sally S. Azer Robert G. Mannino Hyoann Choi Dan Y. Zhang Evelyn K. Williams Erica N. Evans Celeste K. Kanne Melissa L. Kemp Vivien A. Sheehan Marcus A. Carden Carolyn M. Bennett David K. Wood Wilbur A. Lam |
| author_sort | Meredith E. Fay |
| collection | DOAJ |
| description | Abstract While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporating phenomena such as fluid flow and dynamic cell adhesion. As such, experimentalists typically rely on error-prone and time-consuming manual analysis, resulting in lost resolution and missed opportunities for innovative metrics. We present a user-adaptable toolkit packaged into the open-source, standalone Interactive Cellular assay Labeled Observation and Tracking Software (iCLOTS). We benchmark cell adhesion, single-cell tracking, velocity profile, and multiscale microfluidic-centric applications with blood samples, the prototypical biofluid specimen. Moreover, machine learning algorithms characterize previously imperceptible data groupings from numerical outputs. Free to download/use, iCLOTS addresses a need for a field stymied by a lack of analytical tools for innovative, physiologically-relevant assays of any design, democratizing use of well-validated algorithms for all end-user biomedical researchers who would benefit from advanced computational methods. |
| first_indexed | 2024-03-10T17:33:24Z |
| format | Article |
| id | doaj.art-3240155307a64ee7b82312b788ad694a |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:33:24Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-3240155307a64ee7b82312b788ad694a2023-11-20T09:57:02ZengNature PortfolioNature Communications2041-17232023-08-0114111610.1038/s41467-023-40522-4iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assaysMeredith E. Fay0Oluwamayokun Oshinowo1Elizabeth Iffrig2Kirby S. Fibben3Christina Caruso4Scott Hansen5Jamie O. Musick6José M. Valdez7Sally S. Azer8Robert G. Mannino9Hyoann Choi10Dan Y. Zhang11Evelyn K. Williams12Erica N. Evans13Celeste K. Kanne14Melissa L. Kemp15Vivien A. Sheehan16Marcus A. Carden17Carolyn M. Bennett18David K. Wood19Wilbur A. Lam20The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityDepartment of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children’s Healthcare of Atlanta, Emory University School of MedicineDepartment of Biomedical Engineering, University of MinnesotaDepartment of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children’s Healthcare of Atlanta, Emory University School of MedicineDepartment of Biomedical Engineering, University of MinnesotaThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityParker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of TechnologyThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityDepartment of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children’s Healthcare of Atlanta, Emory University School of MedicineDepartment of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children’s Healthcare of Atlanta, Emory University School of MedicineThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityDepartment of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children’s Healthcare of Atlanta, Emory University School of MedicineDepartment of Epidemiology, Gillings School of Public Health, University of North CarolinaDepartment of Pediatrics, Division of Pediatric Hematology/Oncology, Aflac Cancer Center and Blood Disorders Service of Children’s Healthcare of Atlanta, Emory University School of MedicineDepartment of Biomedical Engineering, University of MinnesotaThe Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory UniversityAbstract While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurrent development of widely-accessible techniques to analyze time-dependent microscopy data incorporating phenomena such as fluid flow and dynamic cell adhesion. As such, experimentalists typically rely on error-prone and time-consuming manual analysis, resulting in lost resolution and missed opportunities for innovative metrics. We present a user-adaptable toolkit packaged into the open-source, standalone Interactive Cellular assay Labeled Observation and Tracking Software (iCLOTS). We benchmark cell adhesion, single-cell tracking, velocity profile, and multiscale microfluidic-centric applications with blood samples, the prototypical biofluid specimen. Moreover, machine learning algorithms characterize previously imperceptible data groupings from numerical outputs. Free to download/use, iCLOTS addresses a need for a field stymied by a lack of analytical tools for innovative, physiologically-relevant assays of any design, democratizing use of well-validated algorithms for all end-user biomedical researchers who would benefit from advanced computational methods.https://doi.org/10.1038/s41467-023-40522-4 |
| spellingShingle | Meredith E. Fay Oluwamayokun Oshinowo Elizabeth Iffrig Kirby S. Fibben Christina Caruso Scott Hansen Jamie O. Musick José M. Valdez Sally S. Azer Robert G. Mannino Hyoann Choi Dan Y. Zhang Evelyn K. Williams Erica N. Evans Celeste K. Kanne Melissa L. Kemp Vivien A. Sheehan Marcus A. Carden Carolyn M. Bennett David K. Wood Wilbur A. Lam iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays Nature Communications |
| title | iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays |
| title_full | iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays |
| title_fullStr | iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays |
| title_full_unstemmed | iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays |
| title_short | iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays |
| title_sort | iclots open source artificial intelligence enabled software for analyses of blood cells in microfluidic and microscopy based assays |
| url | https://doi.org/10.1038/s41467-023-40522-4 |
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