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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.
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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.
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Wang B, Zhang X, Han X, Hao B, Li Y, Guo X. TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics. Brief Bioinform 2024; 25:bbae055. [PMID: 38426320 PMCID: PMC10939423 DOI: 10.1093/bib/bbae055] [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: 10/19/2023] [Revised: 01/07/2024] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Protein subcellular localization (PSL) is very important in order to understand its functions, and its movement between subcellular niches within cells plays fundamental roles in biological process regulation. Mass spectrometry-based spatio-temporal proteomics technologies can help provide new insights of protein translocation, but bring the challenge in identifying reliable protein translocation events due to the noise interference and insufficient data mining. We propose a semi-supervised graph convolution network (GCN)-based framework termed TransGCN that infers protein translocation events from spatio-temporal proteomics. Based on expanded multiple distance features and joint graph representations of proteins, TransGCN utilizes the semi-supervised GCN to enable effective knowledge transfer from proteins with known PSLs for predicting protein localization and translocation. Our results demonstrate that TransGCN outperforms current state-of-the-art methods in identifying protein translocations, especially in coping with batch effects. It also exhibited excellent predictive accuracy in PSL prediction. TransGCN is freely available on GitHub at https://github.com/XuejiangGuo/TransGCN.
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Affiliation(s)
- Bing Wang
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Xiangzheng Zhang
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Xudong Han
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Bingjie Hao
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Yan Li
- Department of Clinical Laboratory, Sir Run Run Hospital, Nanjing Medical University, Nanjing 211100, China
| | - Xuejiang Guo
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
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Zou K, Wang S, Wang Z, Zou H, Yang F. Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence. SENSORS (BASEL, SWITZERLAND) 2023; 23:9014. [PMID: 38005402 PMCID: PMC10675401 DOI: 10.3390/s23229014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/29/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023]
Abstract
Protein is one of the primary biochemical macromolecular regulators in the compartmental cellular structure, and the subcellular locations of proteins can therefore provide information on the function of subcellular structures and physiological environments. Recently, data-driven systems have been developed to predict the subcellular location of proteins based on protein sequence, immunohistochemistry (IHC) images, or immunofluorescence (IF) images. However, the research on the fusion of multiple protein signals has received little attention. In this study, we developed a dual-signal computational protocol by incorporating IHC images into protein sequences to learn protein subcellular localization. Three major steps can be summarized as follows in this protocol: first, a benchmark database that includes 281 proteins sorted out from 4722 proteins of the Human Protein Atlas (HPA) and Swiss-Prot database, which is involved in the endoplasmic reticulum (ER), Golgi apparatus, cytosol, and nucleoplasm; second, discriminative feature operators were first employed to quantitate protein image-sequence samples that include IHC images and protein sequence; finally, the feature subspace of different protein signals is absorbed to construct multiple sub-classifiers via dimensionality reduction and binary relevance (BR), and multiple confidence derived from multiple sub-classifiers is adopted to decide subcellular location by the centralized voting mechanism at the decision layer. The experimental results indicated that the dual-signal model embedded IHC images and protein sequences outperformed the single-signal models with accuracy, precision, and recall of 75.41%, 80.38%, and 74.38%, respectively. It is enlightening for further research on protein subcellular location prediction under multi-signal fusion of protein.
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Affiliation(s)
- Kai Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Simeng Wang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Ziqian Wang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Hongliang Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Fan Yang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- Artificial Intelligence and Bioinformation Cognition Laboratory, Jiangxi Science and Technology Normal University, Nanchang 330038, China
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Wang B, Zhang X, Xu C, Han X, Wang Y, Situ C, Li Y, Guo X. DeepSP: A Deep Learning Framework for Spatial Proteomics. J Proteome Res 2023. [PMID: 37314414 DOI: 10.1021/acs.jproteome.2c00394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins across subcellular fractions provides us a high-throughput approach to predict unknown PSLs based on known PSLs. However, the accuracy of PSL annotations in spatial proteomics is limited by the performance of existing PSL predictors based on traditional machine learning algorithms. In this study, we present a novel deep learning framework named DeepSP for PSL prediction of an MS-based spatial proteomics data set. DeepSP constructs the new feature map of a difference matrix by capturing detailed changes between different subcellular fractions of protein occupancy profiles and uses the convolutional block attention module to improve the prediction performance of PSL. DeepSP achieved significant improvement in accuracy and robustness for PSL prediction in independent test sets and unknown PSL prediction compared to current state-of-the-art machine learning predictors. As an efficient and robust framework for PSL prediction, DeepSP is expected to facilitate spatial proteomics studies and contributes to the elucidation of protein functions and the regulation of biological processes.
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Affiliation(s)
- Bing Wang
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Xiangzheng Zhang
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Chen Xu
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Xudong Han
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Yue Wang
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Chenghao Situ
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
| | - Yan Li
- Department of Clinical Laboratory, Sir Run Run Hospital, Nanjing Medical University, Nanjing 211100, China
| | - Xuejiang Guo
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing 211166, China
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Ullah M, Hadi F, Song J, Yu DJ. PScL-2LSAESM: bioimage-based prediction of protein subcellular localization by integrating heterogeneous features with the two-level SAE-SM and mean ensemble method. Bioinformatics 2023; 39:6839969. [PMID: 36413068 PMCID: PMC9947927 DOI: 10.1093/bioinformatics/btac727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/02/2022] [Accepted: 11/21/2022] [Indexed: 11/23/2022] Open
Abstract
MOTIVATION Over the past decades, a variety of in silico methods have been developed to predict protein subcellular localization within cells. However, a common and major challenge in the design and development of such methods is how to effectively utilize the heterogeneous feature sets extracted from bioimages. In this regards, limited efforts have been undertaken. RESULTS We propose a new two-level stacked autoencoder network (termed 2L-SAE-SM) to improve its performance by integrating the heterogeneous feature sets. In particular, in the first level of 2L-SAE-SM, each optimal heterogeneous feature set is fed to train our designed stacked autoencoder network (SAE-SM). All the trained SAE-SMs in the first level can output the decision sets based on their respective optimal heterogeneous feature sets, known as 'intermediate decision' sets. Such intermediate decision sets are then ensembled using the mean ensemble method to generate the 'intermediate feature' set for the second-level SAE-SM. Using the proposed framework, we further develop a novel predictor, referred to as PScL-2LSAESM, to characterize image-based protein subcellular localization. Extensive benchmarking experiments on the latest benchmark training and independent test datasets collected from the human protein atlas databank demonstrate the effectiveness of the proposed 2L-SAE-SM framework for the integration of heterogeneous feature sets. Moreover, performance comparison of the proposed PScL-2LSAESM with current state-of-the-art methods further illustrates that PScL-2LSAESM clearly outperforms the existing state-of-the-art methods for the task of protein subcellular localization. AVAILABILITY AND IMPLEMENTATION https://github.com/csbio-njust-edu/PScL-2LSAESM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matee Ullah
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Fazal Hadi
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | | | - Dong-Jun Yu
- To whom correspondence should be addressed. or
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