1
|
Bao LX, Luo ZM, Zhu XL, Xu YY. Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images. Med Biol Eng Comput 2024; 62:1105-1119. [PMID: 38150111 DOI: 10.1007/s11517-023-02985-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 11/28/2023] [Indexed: 12/28/2023]
Abstract
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.
Collapse
Affiliation(s)
- Lin-Xia Bao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Zhuo-Ming Luo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Xi-Liang Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Ying-Ying Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China.
| |
Collapse
|