Skip to main content
SpringerLink
Log in

Deep Learning Model for Predicting Airway Organoid Differentiation

  • Original Article
  • Published:
Tissue Engineering and Regenerative Medicine Aims and scope

Abstract

Background:

Organoids are self-organized three-dimensional culture systems and have the advantages of both in vitro and in vivo experiments. However, each organoid has a different degree of self-organization, and methods such as immunofluorescence staining are required for confirmation. Therefore, we established a system to select organoids with high tissue-specific similarity using deep learning without relying on staining by acquiring bright-field images in a non-destructive manner.

Methods:

We identified four biomarkers in RNA extracted from airway organoids. We also predicted biomarker expression by image-based analysis of organoids by convolution neural network, a deep learning method.

Results:

We predicted airway organoid-specific marker expression from bright-field images of organoids. Organoid differentiation was verified by immunofluorescence staining of the same organoid after predicting biomarker expression in bright-field images.

Conclusion:

Our study demonstrates the potential of imaging and deep learning to distinguish organoids with high human tissue similarity in disease research and drug screening.

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

Similar content being viewed by others

References

  1. Kitaeva KV, Rutland CS, Rizvanov AA, Solovyeva VV. Cell culture based in vitro test systems for anticancer drug screening. Front Bioeng Biotechnol. 2020;8:322.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Shanbhag A, Rabel S, Nauka E, Casadevall G, Shivanand P, Eichenbaum G, et al. Method for screening of solid dispersion formulations of low-solubility compounds–miniaturization and automation of solvent casting and dissolution testing. Int J Pharm. 2008;351:209–18.

    Article  CAS  PubMed  Google Scholar 

  3. Knouse KA, Lopez KE, Bachofner M, Amon A. Chromosome segregation fidelity in epithelia requires tissue architecture. Cell. 2018;175:200–211.e13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yamada KM, Cukierman E. Modeling tissue morphogenesis and cancer in 3D. Cell. 2007;130:601–10.

    Article  CAS  PubMed  Google Scholar 

  5. Tuveson D, Clevers H. Cancer modeling meets human organoid technology. Science. 2019;364:952–5.

    Article  CAS  PubMed  Google Scholar 

  6. Kim DH, Kim SW. Clinical applications of human nasal organoids. Clin Exp Otorhinolaryngol. 2022;15:201–2.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rossi G, Manfrin A, Lutolf MP. Progress and potential in organoid research. Nat Rev Genet. 2018;19:671–87.

    Article  CAS  PubMed  Google Scholar 

  8. Ou M, Li Q, Ling X, Yao J, Mo X. Cocktail formula and application prospects for oral and maxillofacial organoids. Tissue Eng Regen Med. 2022;19:913–25.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pryzhkova MV, Boers R, Jordan PW. Modeling human gonad development in Organoids. Tissue Eng Regen Med. 2022;19:1185–1206.

  10. Marklein RA, Lo Surdo JL, Bellayr IH, Godil SA, Puri RK, Bauer SR. High content imaging of early morphological signatures predicts long term mineralization capacity of human mesenchymal stem cells upon osteogenic induction. Stem Cells. 2016;34:935–47.

    Article  CAS  PubMed  Google Scholar 

  11. Matsuoka F, Takeuchi I, Agata H, Kagami H, Shiono H, Kiyota Y, et al. Morphology-based prediction of osteogenic differentiation potential of human mesenchymal stem cells. PLoS One. 2013;8:e55082.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Matsuoka F, Takeuchi I, Agata H, Kagami H, Shiono H, Kiyota Y, et al. Characterization of time-course morphological features for efficient prediction of osteogenic potential in human mesenchymal stem cells. Biotechnol Bioeng. 2014;111:1430–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sasaki H, Takeuchi I, Okada M, Sawada R, Kanie K, Kiyota Y, et al. Label-free morphology-based prediction of multiple differentiation potentials of human mesenchymal stem cells for early evaluation of intact cells. PLoS One. 2014;9:e93952.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Klinker MW, Marklein RA, Lo Surdo JL, Wei CH, Bauer SR. Morphological features of IFN-γ-stimulated mesenchymal stromal cells predict overall immunosuppressive capacity. Proc Natl Acad Sci U S A. 2017;114:E2598-607.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Marklein RA, Klinker MW, Drake KA, Polikowsky HG, Lessey-Morillon EC, Bauer SR. Morphological profiling using machine learning reveals emergent subpopulations of interferon-γ-stimulated mesenchymal stromal cells that predict immunosuppression. Cytotherapy. 2019;21:17–31.

    Article  CAS  PubMed  Google Scholar 

  16. Bian X, Li G, Wang C, Liu W, Lin X, Chen Z, et al. A deep learning model for detection and tracking in high-throughput images of organoid. Comput Biol Med. 2021;134:104490.

    Article  PubMed  Google Scholar 

  17. Abdul L, Rajasekar S, Lin DSY, Venkatasubramania Raja S, Sotra A, Feng Y, et al. Deep-LUMEN assay—human lung epithelial spheroid classification from brightfield images using deep learning. Lab Chip. 2020;20:4623–31.

    Article  CAS  PubMed  Google Scholar 

  18. Kegeles E, Naumov A, Karpulevich EA, Volchkov P, Baranov P. Convolutional neural networks can predict retinal differentiation in retinal organoids. Front Cell Neurosci. 2020;14:171.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Schaub NJ, Hotaling NA, Manescu P, Padi S, Wan Q, Sharma R, et al. Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy. J Clin Invest. 2020;130:1010–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Waisman A, La Greca A, Möbbs AM, Scarafía MA, Santín Velazque NL, Neiman G, et al. Deep learning neural networks highly predict very early onset of pluripotent stem cell differentiation. Stem Cell Reports. 2019;12:845–59.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Monzel AS, Hemmer K, Kaoma T, Smits LM, Bolognin S, Lucarelli P, et al. Machine learning-assisted neurotoxicity prediction in human midbrain organoids. Parkinsonism Relat Disord. 2020;75:105–9.

    Article  PubMed  Google Scholar 

  22. Nagasato D, Tabuchi H, Masumoto H, Kusuyama T, Kawai Y, Ishitobi N, et al. Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning. Sci Rep. 2020;10:19369.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Loey M, Manogaran G, Khalifa NEM. A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Comput Appl. 2020;1–13.

  24. Koonsanit K, Thongvigitmanee S, Pongnapang N, Thajchayapong P. Image enhancement on digital x-ray images using N-CLAHE. 2017 10th Biomedical Engineering International Conference (BMEiCON)2017. pp. 1–4.

  25. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15:20170387.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Peng Z, Andersson K, Lindholm J, Bodin I, Pramana S, Pawitan Y, et al. Operator dependent choice of prostate cancer biopsy has limited impact on a gene signature analysis for the highly expressed genes IGFBP3 and F3 in prostate cancer epithelial cells. PLoS One. 2014;9:e109610.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Peng Z, Andersson K, Lindholm J, Dethlefsen O, Pramana S, Pawitan Y, et al. Improving the prediction of prostate Cancer overall survival by supplementing readily available Clinical Data with Gene expression levels of IGFBP3 and F3 in Formalin-Fixed paraffin embedded Core Needle Biopsy Material. PLoS One. 2016;11:e0145545.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF)(RS-2023-00209494, 2020R1A6A1A03047902, 2021M3F7A1083232, 2019M3A9H2032424), and by the Ministry of Trade, Industry & Energy (MOTIE, Korea)(20012378) and Korean Fund for Regenerative Medicine(KFRM) Grant funded by the Korea government (23C0121L1). The sponsors had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul, 06591, Republic of Korea

    Mi Hyun Lim, Sung Won Kim & Do Hyun Kim

  2. Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), 223, 5th Engineering Building 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk, 37673, Republic of Korea

    Seungmin Shin, Keonhyeok Park, Jaejung Park & Seungchul Lee

  3. Department of Surgery, College of Medicine, Taif University, Taif, Saudi Arabia

    Mohammed Abdullah Basurrah

  4. Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

    Seungchul Lee

Authors
  1. Mi Hyun Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seungmin Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keonhyeok Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jaejung Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sung Won Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohammed Abdullah Basurrah
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Seungchul Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Do Hyun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Seungchul Lee or Do Hyun Kim.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical statement

The study protocol was approved by the institutional review board of Seoul St. Mary’s Hospital of the Catholic University of Korea (IRB No. KC08TISS0341) and all methods were performed in accordance with the relevant guidelines and regulations.

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 (DOCX 65 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

Lim, M.H., Shin, S., Park, K. et al. Deep Learning Model for Predicting Airway Organoid Differentiation. Tissue Eng Regen Med 20, 1109–1117 (2023). https://doi.org/10.1007/s13770-023-00563-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13770-023-00563-8

Keywords

Search

Navigation