AI-organoid Integrated Systems for Biomedical Studies and Applications

Overview

Journal Bioeng Transl Med

Date 2024 Mar 4

PMID 38435826

Authors

Sudhiksha Maramraju

Andrew Kowalczewski

Anirudh Kaza

Xiyuan Liu

Jathin Pranav Singaraju

Mark V Albert

Zhen Ma

Huaxiao Yang

Affiliations

Soon will be listed here.

Abstract

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

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References

Gupta H, Jin K, Nguyen H, McCann M, Unser M . CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction. IEEE Trans Med Imaging. 2018; 37(6):1440-1453. DOI: 10.1109/TMI.2018.2832656. View

Zhang Z, Xu Z, Yuan F, Jin K, Xiang M . Retinal Organoid Technology: Where Are We Now?. Int J Mol Sci. 2021; 22(19). PMC: 8549701. DOI: 10.3390/ijms221910244. View

Kelchtermans P, Bittremieux W, De Grave K, Degroeve S, Ramon J, Laukens K . Machine learning applications in proteomics research: how the past can boost the future. Proteomics. 2013; 14(4-5):353-66. DOI: 10.1002/pmic.201300289. View

Gritti N, Lim J, Anlas K, Pandya M, Aalderink G, Martinez-Ara G . MOrgAna: accessible quantitative analysis of organoids with machine learning. Development. 2021; 148(18). PMC: 8451065. DOI: 10.1242/dev.199611. View

Boutros M, Heigwer F, Laufer C . Microscopy-Based High-Content Screening. Cell. 2015; 163(6):1314-25. DOI: 10.1016/j.cell.2015.11.007. View

Teles D, Kim Y, Ronaldson-Bouchard K, Vunjak-Novakovic G . Machine Learning Techniques to Classify Healthy and Diseased Cardiomyocytes by Contractility Profile. ACS Biomater Sci Eng. 2021; 7(7):3043-3052. PMC: 9188827. DOI: 10.1021/acsbiomaterials.1c00418. View

Bray M, Singh S, Han H, Davis C, Borgeson B, Hartland C . Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc. 2016; 11(9):1757-74. PMC: 5223290. DOI: 10.1038/nprot.2016.105. View

Hinton G . Deep Learning-A Technology With the Potential to Transform Health Care. JAMA. 2018; 320(11):1101-1102. DOI: 10.1001/jama.2018.11100. View

Yakoub A, Sadek M . Development and Characterization of Human Cerebral Organoids: An Optimized Protocol. Cell Transplant. 2018; 27(3):393-406. PMC: 6038047. DOI: 10.1177/0963689717752946. View

10.

Ortolan D, Sharma R, Volkov A, Maminishkis A, Hotaling N, Huryn L . Single-cell-resolution map of human retinal pigment epithelium helps discover subpopulations with differential disease sensitivity. Proc Natl Acad Sci U S A. 2022; 119(19):e2117553119. PMC: 9171647. DOI: 10.1073/pnas.2117553119. View

11.

Keegan J, Slabaugh G, Arridge S, Ye X, Guo Y, Yu S . DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. IEEE Trans Med Imaging. 2018; 37(6):1310-1321. DOI: 10.1109/TMI.2017.2785879. View

12.

Susaki E, Tainaka K, Perrin D, Yukinaga H, Kuno A, Ueda H . Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging. Nat Protoc. 2015; 10(11):1709-27. DOI: 10.1038/nprot.2015.085. View

13.

Maramraju S, Kowalczewski A, Kaza A, Liu X, Singaraju J, Albert M . AI-organoid integrated systems for biomedical studies and applications. Bioeng Transl Med. 2024; 9(2):e10641. PMC: 10905559. DOI: 10.1002/btm2.10641. View

14.

Heylman C, Datta R, Sobrino A, George S, Gratton E . Supervised Machine Learning for Classification of the Electrophysiological Effects of Chronotropic Drugs on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes. PLoS One. 2015; 10(12):e0144572. PMC: 4690607. DOI: 10.1371/journal.pone.0144572. View

15.

Fan K, Zhang S, Zhang Y, Lu J, Holcombe M, Zhang X . A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction. Sci Rep. 2017; 7(1):13496. PMC: 5647349. DOI: 10.1038/s41598-017-13680-x. View

16.

Kusumoto D, Lachmann M, Kunihiro T, Yuasa S, Kishino Y, Kimura M . Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells. Stem Cell Reports. 2018; 10(6):1687-1695. PMC: 5989816. DOI: 10.1016/j.stemcr.2018.04.007. View

17.

Costamagna G, Comi G, Corti S . Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids. Int J Mol Sci. 2021; 22(5). PMC: 7961877. DOI: 10.3390/ijms22052659. View

18.

Maleki F, Muthukrishnan N, Ovens K, Reinhold C, Forghani R . Machine Learning Algorithm Validation: From Essentials to Advanced Applications and Implications for Regulatory Certification and Deployment. Neuroimaging Clin N Am. 2020; 30(4):433-445. DOI: 10.1016/j.nic.2020.08.004. View

19.

Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G . Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019; 18(6):463-477. PMC: 6552674. DOI: 10.1038/s41573-019-0024-5. View

20.

Di Lullo E, Kriegstein A . The use of brain organoids to investigate neural development and disease. Nat Rev Neurosci. 2017; 18(10):573-584. PMC: 5667942. DOI: 10.1038/nrn.2017.107. View