Deep learning pipeline for automated cell profiling from cyclic imaging.
Cell segmentation
Cyclic microscopy
Imaging analysis
Machine learning
Software
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
09 10 2024
09 10 2024
Historique:
received:
12
12
2023
accepted:
27
09
2024
medline:
10
10
2024
pubmed:
10
10
2024
entrez:
9
10
2024
Statut:
epublish
Résumé
Cyclic fluorescence microscopy enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis of these datasets can be time-prohibitive due to the sheer volume of data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pipeline pre-processes raw image files, quickly corrects for translation errors between imaging cycles, and leverages a pre-trained neural network to segment individual cells and generate single-cell molecular profiles. We applied CycloNET to a dataset of 22 human samples from head and neck squamous cell carcinoma patients and trained a neural network to segment immune cells. CycloNET efficiently processed a large-scale dataset (17 fields of view per cycle and 13 staining cycles per specimen) in 10 min, delivering insights at the single-cell resolution and facilitating the identification of rare immune cell clusters. We expect that this rapid pipeline will serve as a powerful tool to understand complex biological systems at the cellular level, with the potential to facilitate breakthroughs in areas such as developmental biology, disease pathology, and personalized medicine.
Identifiants
pubmed: 39384907
doi: 10.1038/s41598-024-74597-w
pii: 10.1038/s41598-024-74597-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23600Subventions
Organisme : NCI NIH HHS
ID : U01CA279858
Pays : United States
Informations de copyright
© 2024. The Author(s).
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