Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

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.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 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

    Article  PubMed  Google Scholar 

  3. Sjöstedt E, Zhong W, Fagerberg L, Karlsson M, Mitsios N, Adori C et al (2020) An atlas of the protein-coding genes in the human, pig, and mouse brain. Science 367(6482):eaay5947. https://doi.org/10.1126/science.aay5947

    Article  CAS  PubMed  Google Scholar 

  4. Zellner A, Müller SA, Lindner B, Beaufort N, Rozemuller AJ, Arzberger T et al (2022) Proteomic profiling in cerebral amyloid angiopathy reveals an overlap with CADASIL highlighting accumulation of HTRA1 and its substrates. Acta Neuropathol Commun 10(1):1–15

    Article  Google Scholar 

  5. Ferreira M, Ventorim R, Almeida E, Silveira S, Silveira W (2021) Protein abundance prediction through machine learning methods. J Mol Biol 433(22):167267

    Article  CAS  PubMed  Google Scholar 

  6. Wang F, Wei L (2022) Multi-scale deep learning for the imbalanced multi-label protein subcellular localization prediction based on immunohistochemistry images. Bioinformatics 38(9):2602–2611

    Article  CAS  PubMed  Google Scholar 

  7. Cong H, Liu H, Chen Y, Cao Y (2020) Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization. Med Biol Eng Compu 58:3017–3038

    Article  Google Scholar 

  8. Xue Z-Z, Li C, Luo Z-M, Wang S-S, Xu Y-Y (2022) Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers. BMC Bioinformatics 23(1):470. https://doi.org/10.1186/s12859-022-05015-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ouyang W, Winsnes CF, Hjelmare M, Cesnik AJ, Åkesson L, Xu H et al (2019) Analysis of the human protein atlas image classification competition. Nat Methods 16(12):1254–1261. https://doi.org/10.1038/s41592-019-0658-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wang G, Xue M-Q, Shen H-B, Xu Y-Y (2022) Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks. Briefings in Bioinformatics 23(2):bbab539. https://doi.org/10.1093/bib/bbab539

    Article  CAS  PubMed  Google Scholar 

  11. Xue M-Q, Zhu X-L, Wang G, Xu Y-Y (2022) DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features. Bioinformatics 38(3):827–833. https://doi.org/10.1093/bioinformatics/btab730

    Article  CAS  PubMed  Google Scholar 

  12. Giacopelli G, Migliore M, Tegolo D (2023) NeuronAlg: an innovative neuronal computational model for immunofluorescence image segmentation. Sensors 23(10):4598. https://doi.org/10.3390/s23104598

    Article  PubMed  PubMed Central  Google Scholar 

  13. Goubran M, Leuze C, Hsueh B, Aswendt M, Ye L, Tian Q et al (2019) Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI. Nat Commun 10(1):5504. https://doi.org/10.1038/s41467-019-13374-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li Y, Zhang Q, Zhou H, Li J, Li X, Li A (2023) Cerebrovascular segmentation from mesoscopic optical images using Swin Transformer. J Innov Opt Health Sci 2350009. https://doi.org/10.1142/S1793545823500098

  15. Digre A, Lindskog C (2021) The human protein atlas—spatial localization of the human proteome in health and disease. Protein Sci 30(1):218–233

    Article  CAS  PubMed  Google Scholar 

  16. Tyson AL, Margrie TW (2022) Mesoscale microscopy and image analysis tools for understanding the brain. Prog Biophys Mol Biol 168:81–93. https://doi.org/10.1016/j.pbiomolbio.2021.06.013

    Article  PubMed  PubMed Central  Google Scholar 

  17. Agarwal N, Xu X, Gopi M (2018) Geometry processing of conventionally produced mouse brain slice images. J Neurosci Methods 306:45–56. https://doi.org/10.1016/j.jneumeth.2018.04.008

    Article  PubMed  PubMed Central  Google Scholar 

  18. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36. https://doi.org/10.1016/S1361-8415(01)80026-8

    Article  CAS  PubMed  Google Scholar 

  19. Ni H, Tan C, Feng Z, Chen S, Zhang Z, Li W et al (2020) A robust image registration interface for large volume brain atlas. Sci Rep 10(1):2139. https://doi.org/10.1038/s41598-020-59042-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hirai R, Mori S, Suyari H, Tsuji H, Ishikawa H (2023) Optimizing 3DCT image registration for interfractional changes in carbon-ion prostate radiotherapy. Sci Rep 13(1):7448. https://doi.org/10.1038/s41598-023-34339-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen Z, Zheng Y, Gee JC (2023) TransMatch: a transformer-based multilevel dual-stream feature matching network for unsupervised deformable image registration. IEEE Trans Med Imaging 1–1. https://doi.org/10.1109/TMI.2023.3288136

  22. Toki MI, Cecchi F, Hembrough T, Syrigos KN, Rimm DL (2017) Proof of the quantitative potential of immunofluorescence by mass spectrometry. Lab Invest 97(3):329–334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pécot T, Cuitiño MC, Johnson RH, Timmers C, Leone G (2022) Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images. PLoS Comput Biol 18(3):e1009949

    Article  PubMed  PubMed Central  Google Scholar 

  24. De León Rodríguez SG, Hernández Herrera P, Aguilar Flores C, Pérez Koldenkova V, Guerrero A, Mantilla A et al (2022) A machine learning workflow of multiplexed immunofluorescence images to interrogate activator and tolerogenic profiles of conventional type 1 dendritic cells infiltrating melanomas of disease-free and metastatic patients. J Oncol 2022:9775736

    Article  PubMed  PubMed Central  Google Scholar 

  25. Fang K, Li C, Wang J (2023) An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning. Brief Bioinform 24(3):bbad144. https://doi.org/10.1093/bib/bbad144

    Article  CAS  PubMed  Google Scholar 

  26. Yang Y, Tu Y, Lei H, Long W (2023) HAMIL: hierarchical aggregation-based multi-instance learning for microscopy image classification. Pattern Recogn 136:109245. https://doi.org/10.1016/j.patcog.2022.109245

    Article  Google Scholar 

  27. Abdi IY, Bartl M, Dakna M, Abdesselem H, Majbour N, Trenkwalder C et al (2023) Cross-sectional proteomic expression in Parkinson’s disease-related proteins in drug-naïve patients vs healthy controls with longitudinal clinical follow-up. Neurobiol Dis 177:105997. https://doi.org/10.1016/j.nbd.2023.105997

    Article  CAS  PubMed  Google Scholar 

  28. Uras I, Karayel-Basar M, Sahin B, Baykal AT (2023) Detection of early proteomic alterations in 5xFAD Alzheimer’s disease neonatal mouse model via MALDI-MSI. Alzheimers Dement 19(10):4572–4589

  29. Taguchi K, Watanabe Y, Tsujimura A, Tanaka M (2019) Expression of alpha-synuclein is regulated in a neuronal cell type-dependent manner. Anat Sci Int 94(1):11–22. https://doi.org/10.1007/s12565-018-0464-8

    Article  CAS  PubMed  Google Scholar 

  30. Zahid S, Oellerich M, Asif AR, Ahmed N (2014) Differential expression of proteins in brain regions of Alzheimer’s Disease Patients. Neurochem Res 39(1):208–215. https://doi.org/10.1007/s11064-013-1210-1

    Article  CAS  PubMed  Google Scholar 

  31. Feng Y, Zhang L, Mo J (2020) Deep manifold preserving autoencoder for classifying breast cancer histopathological images. IEEE/ACM Trans Comput Biol Bioinform 17(1):91–101. https://doi.org/10.1109/TCBB.2018.2858763

    Article  PubMed  Google Scholar 

  32. Karim MR, Beyan O, Zappa A, Costa IG, Rebholz-Schuhmann D, Cochez M et al (2021) Deep learning-based clustering approaches for bioinformatics. Brief Bioinform 22(1):393–415. https://doi.org/10.1093/bib/bbz170

    Article  PubMed  Google Scholar 

  33. Wang C-W, Ka S-M, Chen A (2014) Robust image registration of biological microscopic images. Sci Rep 4(1):1–12

    Google Scholar 

  34. Agarwal N, Xu X, Gopi M (2016) Robust registration of mouse brain slices with severe histological artifacts. Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing p. 1–8

  35. Kindle LM, Kakadiaris IA, Ju T, Carson JP (2011) A semiautomated approach for artefact removal in serial tissue cryosections. J Microsc 241(2):200–206

    Article  CAS  PubMed  Google Scholar 

  36. Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256. https://doi.org/10.1109/34.121791

    Article  Google Scholar 

  37. Sharma K, Schmitt S, Bergner CG, Tyanova S, Kannaiyan N, Manrique-Hoyos N et al (2015) Cell type- and brain region-resolved mouse brain proteome. Nat Neurosci 18(12):1819–1831

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wang D, Khosla A, Gargeya R, Irshad H, Beck AH (2016) Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:160605718

  39. Frade J, Pereira T, Morgado J, Silva F, Freitas C, Mendes J et al (2022) Multiple instance learning for lung pathophysiological findings detection using CT scans. Med Biol Eng Compu 60(6):1569–1584. https://doi.org/10.1007/s11517-022-02526-y

    Article  Google Scholar 

  40. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556

  41. Hou L, Samaras D, Kurc TM, Gao Y, Davis JE, Saltz JH (2016) Patch-based convolutional neural network for whole slide tissue image classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) p. 2424–2433

  42. Wang X, Chen H, Gan C, Lin H, Dou Q, Tsougenis E et al (2020) Weakly supervised deep learning for whole slide lung cancer image analysis. IEEE Trans Cybern 50(9):3950–3962. https://doi.org/10.1109/TCYB.2019.2935141

    Article  PubMed  Google Scholar 

  43. Ilse M, Tomczak J, Welling M (2018) Attention-based deep multiple instance learning. International conference on machine learning: PMLR p. 2127–2136

  44. Su Z, Tavolara TE, Carreno-Galeano G, Lee SJ, Gurcan MN, Niazi MKK (2022) Attention2majority: weak multiple instance learning for regenerative kidney grading on whole slide images. Med Image Anal 79:102462. https://doi.org/10.1016/j.media.2022.102462

    Article  PubMed  PubMed Central  Google Scholar 

  45. Yao J, Zhu X, Jonnagaddala J, Hawkins N, Huang J (2020) Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Med Image Anal 65:101789. https://doi.org/10.1016/j.media.2020.101789

    Article  PubMed  Google Scholar 

  46. Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. 2017 IEEE International Conference on Computer Vision (ICCV) p. 2999–3007

  47. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) p. 770–778

  48. Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) p. 2261–2269

  49. Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. International conference on machine learning: PMLR p. 6105–6114

  50. Sharma H, Zerbe N, Klempert I, Hellwich O, Hufnagl P (2017) Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph 61:2–13

    Article  PubMed  Google Scholar 

  51. Xu Y-Y, Yang F, Zhang Y, Shen H-B (2015) Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning. Bioinformatics 31(7):1111–1119

    Article  CAS  PubMed  Google Scholar 

  52. Pluim JP, Maintz JB, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22(8):986–1004. https://doi.org/10.1109/TMI.2003.815867

    Article  PubMed  Google Scholar 

  53. Krstinic D, Braović M, Šerić L, Božić-Štulić D (2020) Multi-label classifier performance evaluation with confusion matrix. Comput Sci Inform Technol 10:1

    Google Scholar 

  54. Zhu X-L, Bao L-X, Xue M-Q, Xu Y-Y (2023) Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning. Brief Bioinform 24(1):bbac609. https://doi.org/10.1093/bib/bbac609

    Article  CAS  PubMed  Google Scholar 

  55. Stadler C, Rexhepaj E, Singan VR, Murphy RF, Pepperkok R, Uhlén M et al (2013) Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells. Nat Methods 10(4):315–323. https://doi.org/10.1038/nmeth.2377

    Article  CAS  PubMed  Google Scholar 

  56. Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H et al (2017) A subcellular map of the human proteome. Science 356(6340):eaal3321

    Article  PubMed  Google Scholar 

  57. Tu Y, Lei H, Shen H-B, Yang Y (2022) SIFLoc: a self-supervised pre-training method for enhancing the recognition of protein subcellular localization in immunofluorescence microscopic images. Briefings in Bioinformatics 23(2):bbab605. https://doi.org/10.1093/bib/bbab605

    Article  CAS  PubMed  Google Scholar 

  58. Long W, Yang Y, Shen H-B (2020) ImPLoc: a multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images. Bioinformatics 36(7):2244–2250. https://doi.org/10.1093/bioinformatics/btz909

    Article  CAS  PubMed  Google Scholar 

  59. Nanni L, Paci M, Brahnam S, Lumini A (2022) Feature transforms for image data augmentation. Neural Comput Appl 34(24):22345–22356. https://doi.org/10.1007/s00521-022-07645-z

    Article  Google Scholar 

  60. Nanni L, Paci M, Brahnam S, Lumini A (2021) Comparison of different image data augmentation approaches. J Imaging 7(12):254

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported in part by the Natural Science Foundation of Guangdong Province of China (grant number 2022A1515011436) and the Guangzhou Municipal Science and Technology Project (grant number 202102021087).

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China

    Lin-Xia Bao, Zhuo-Ming Luo, Xi-Liang Zhu & Ying-Ying Xu

  2. Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China

    Lin-Xia Bao, Zhuo-Ming Luo, Xi-Liang Zhu & Ying-Ying Xu

  3. Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China

    Lin-Xia Bao, Zhuo-Ming Luo, Xi-Liang Zhu & Ying-Ying Xu

Authors
  1. Lin-Xia Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhuo-Ming Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xi-Liang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying-Ying Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.Y. Xu designed the research. L.X. Bao and X.L. Zhu wrote the source code and performed the experiments. L.X. Bao, Z.M. Luo, and Y.Y. Xu wrote, edited, and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ying-Ying Xu.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 3109 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bao, LX., Luo, ZM., Zhu, XL. et al. Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images. Med Biol Eng Comput 62, 1105–1119 (2024). https://doi.org/10.1007/s11517-023-02985-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02985-x

Keywords

Search

Navigation