Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.
Bioimage informatics
Cellular location
Immunofluorescence image
Mouse brain
Protein expression
Journal
Medical & biological engineering & computing
ISSN: 1741-0444
Titre abrégé: Med Biol Eng Comput
Pays: United States
ID NLM: 7704869
Informations de publication
Date de publication:
Apr 2024
Apr 2024
Historique:
received:
30
03
2023
accepted:
28
11
2023
pubmed:
27
12
2023
medline:
27
12
2023
entrez:
27
12
2023
Statut:
ppublish
Résumé
Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.
Identifiants
pubmed: 38150111
doi: 10.1007/s11517-023-02985-x
pii: 10.1007/s11517-023-02985-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1105-1119Subventions
Organisme : Natural Science Foundation of Guangdong Province
ID : 2022A1515011436
Organisme : Guangzhou Municipal Science and Technology Project
ID : 202102021087
Informations de copyright
© 2023. International Federation for Medical and Biological Engineering.
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