An AI-based imaging flow cytometry approach to study erythrophagocytosis.
artificial intelligence
erythrocyte
erythrophagocytosis
imaging flow cytometry
neutrophil
Journal
Cytometry. Part A : the journal of the International Society for Analytical Cytology
ISSN: 1552-4930
Titre abrégé: Cytometry A
Pays: United States
ID NLM: 101235694
Informations de publication
Date de publication:
09 Sep 2024
09 Sep 2024
Historique:
revised:
03
07
2024
received:
16
02
2024
accepted:
26
07
2024
medline:
9
9
2024
pubmed:
9
9
2024
entrez:
9
9
2024
Statut:
aheadofprint
Résumé
Erythrophagocytosis is a process consisting of recognition, engulfment and digestion by phagocytes of antibody-coated or damaged erythrocytes. Understanding the dynamics that are behind erythrophagocytosis is fundamental to comprehend this cellular process under specific circumstances. Several techniques have been used to study phagocytosis. Among these, an interesting approach is the use of Imaging Flow Cytometry (IFC) to distinguish internalization and binding of cells or particles. However, this method requires laborious analysis. Here, we introduce a novel approach to analyze the phagocytosis process by combining Artificial Intelligence (AI) with IFC. Our study demonstrates that this approach is highly suitable to study erythrophagocytosis, categorizing internalized, bound and non-bound erythrocytes. Validation experiments showed that our pipeline performs with high accuracy and reproducibility.
Identifiants
pubmed: 39248056
doi: 10.1002/cyto.a.24894
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : European Union Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie: 860436 - EVIDENCE
Informations de copyright
© 2024 International Society for Advancement of Cytometry.
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