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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.

Titel:	Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.
Autor/in / Beteiligte Person:	Bao, LX ; Luo, ZM ; Zhu, XL ; Xu, YY
Link:	Full Text Finder (Volltext)
Zeitschrift:	Medical & biological engineering & computing, Jg. 62 (2024-04-01), Heft 4, S. 1105-1119
Veröffentlichung:	New York, NY : Springer ; <i>Original Publication</i>: Stevenage, Eng., Peregrinus., 2024
Medientyp:	academicJournal
ISSN:	1741-0444 (electronic)
DOI:	10.1007/s11517-023-02985-x
Schlagwort:	Animals Mice Fluorescent Antibody Technique Image Processing, Computer-Assisted Mammals Brain Machine Learning
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Med Biol Eng Comput] 2024 Apr; Vol. 62 (4), pp. 1105-1119. <i>Date of Electronic Publication: </i>2023 Dec 27. MeSH Terms: Brain* ; Machine Learning* ; Animals ; Mice ; Fluorescent Antibody Technique ; Image Processing, Computer-Assisted ; Mammals References: Ding J, Ji J, Rabow Z, Shen T, Folz J, Brydges CR et al (2021) A metabolome atlas of the aging mouse brain. Nat Commun 12(1):6021. https://doi.org/10.1038/s41467-021-26310-y. (PMID: 10.1038/s41467-021-26310-y346548188519999) ; Duan L, Liu J, Yin H, Wang W, Liu L, Shen J et al (2022) Dynamic changes in spatiotemporal transcriptome reveal maternal immune dysregulation of autism spectrum disorder. Comput Biol Med 151:106334. https://doi.org/10.1016/j.compbiomed.2022.106334. 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(PMID: 10.3390/jimaging7120254349407218707550) Grant Information: 2022A1515011436 Natural Science Foundation of Guangdong Province; 202102021087 Guangzhou Municipal Science and Technology Project Contributed Indexing: Keywords: Bioimage informatics; Cellular location; Immunofluorescence image; Mouse brain; Protein expression Entry Date(s): Date Created: 20231227 Date Completed: 20240320 Latest Revision: 20240320 Update Code: 20240320

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Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.