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Volmert B, Kiselev A, Juhong A, Wang F, Riggs A, Kostina A, O'Hern C, Muniyandi P, Wasserman A, Huang A, Lewis-Israeli Y, Panda V, Bhattacharya S, Lauver A, Park S, Qiu Z, Zhou C, Aguirre A. A patterned human primitive heart organoid model generated by pluripotent stem cell self-organization. Nat Commun 2023; 14:8245. [PMID: 38086920 PMCID: PMC10716495 DOI: 10.1038/s41467-023-43999-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Pluripotent stem cell-derived organoids can recapitulate significant features of organ development in vitro. We hypothesized that creating human heart organoids by mimicking aspects of in utero gestation (e.g., addition of metabolic and hormonal factors) would lead to higher physiological and anatomical relevance. We find that heart organoids produced using this self-organization-driven developmental induction strategy are remarkably similar transcriptionally and morphologically to age-matched human embryonic hearts. We also show that they recapitulate several aspects of cardiac development, including large atrial and ventricular chambers, proepicardial organ formation, and retinoic acid-mediated anterior-posterior patterning, mimicking the developmental processes found in the post-heart tube stage primitive heart. Moreover, we provide proof-of-concept demonstration of the value of this system for disease modeling by exploring the effects of ondansetron, a drug administered to pregnant women and associated with congenital heart defects. These findings constitute a significant technical advance for synthetic heart development and provide a powerful tool for cardiac disease modeling.
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Affiliation(s)
- Brett Volmert
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Artem Kiselev
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- Division of Dermatology, Department of Medicine, College of Human Medicine, Michigan State University, East Lansing, MI, 48824, USA
| | - Aniwat Juhong
- Institute for Quantitative Health Science and Engineering, Division of Biomedical Devices, Michigan State University, East Lansing, MI, USA
- Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Fei Wang
- Department of Biomedical Engineering, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Ashlin Riggs
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Aleksandra Kostina
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Colin O'Hern
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Priyadharshni Muniyandi
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Aaron Wasserman
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Amanda Huang
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Yonatan Lewis-Israeli
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Vishal Panda
- Department of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, Division of Systems Biology, Michigan State University, East Lansing, MI, USA
| | - Sudin Bhattacharya
- Department of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, Division of Systems Biology, Michigan State University, East Lansing, MI, USA
| | - Adam Lauver
- Department of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
| | - Sangbum Park
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA
- Department of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
- Division of Dermatology, Department of Medicine, College of Human Medicine, Michigan State University, East Lansing, MI, 48824, USA
| | - Zhen Qiu
- Institute for Quantitative Health Science and Engineering, Division of Biomedical Devices, Michigan State University, East Lansing, MI, USA
- Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA
| | - Chao Zhou
- Department of Biomedical Engineering, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Aitor Aguirre
- Institute for Quantitative Health Science and Engineering, Division of Developmental and Stem Cell Biology, Michigan State University, East Lansing, MI, USA.
- Department of Biomedical Engineering, College of Engineering, Michigan State University, East Lansing, MI, USA.
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Juhong A, Li B, Liu Y, Yao CY, Yang CW, Agnew DW, Lei YL, Luker GD, Bumpers H, Huang X, Piyawattanametha W, Qiu Z. Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography (MSOT) imaging. J Biophotonics 2023; 16:e202300142. [PMID: 37382181 DOI: 10.1002/jbio.202300142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/14/2023] [Accepted: 06/23/2023] [Indexed: 06/30/2023]
Abstract
Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through-plane resolution volumetric MSOT is time-consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross-sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG-conjugated nanoworms particles (NWs-ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.
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Affiliation(s)
- Aniwat Juhong
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Bo Li
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Yifan Liu
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Cheng-You Yao
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Chia-Wei Yang
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Chemistry, Michigan State University, East Lansing, Michigan, USA
| | - Dalen W Agnew
- Department of Pathobiology and Diagnostic Investigation, College of Veterinary Medicine, Michigan State University, East Lansing, Michigan, USA
| | - Yu Leo Lei
- Department of Periodontics and Oral Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Gary D Luker
- Department of Radiology, Microbiology and Immunology, and Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Harvey Bumpers
- Department of Surgery, Michigan State University, East Lansing, Michigan, USA
| | - Xuefei Huang
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Chemistry, Michigan State University, East Lansing, Michigan, USA
| | - Wibool Piyawattanametha
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand
| | - Zhen Qiu
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA
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Juhong A, Li B, Yao CY, Yang CW, Agnew DW, Lei YL, Huang X, Piyawattanametha W, Qiu Z. Super-resolution and segmentation deep learning for breast cancer histopathology image analysis. Biomed Opt Express 2023; 14:18-36. [PMID: 36698665 PMCID: PMC9841988 DOI: 10.1364/boe.463839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 06/17/2023]
Abstract
Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images' size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model's results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.
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Affiliation(s)
- Aniwat Juhong
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48823, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA
| | - Bo Li
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48823, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA
| | - Cheng-You Yao
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Chia-Wei Yang
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA
- Department of Chemistry, Michigan State University, East Lansing, MI 48824, USA
| | - Dalen W. Agnew
- College of Veterinary Medicine, Michigan State University, East Lansing, MI 48824, USA
| | - Yu Leo Lei
- Department of Periodontics Oral Medicine, University of Michigan, Ann Arbor, MI 48104, USA
| | - Xuefei Huang
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Chemistry, Michigan State University, East Lansing, MI 48824, USA
| | - Wibool Piyawattanametha
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA
- Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok 10520, Thailand
| | - Zhen Qiu
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48823, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48823, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI 48824, USA
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Yang CW, Liu K, Yao CY, Li B, Juhong A, Qiu Z, Huang X. Indocyanine Green-Conjugated Superparamagnetic Iron Oxide Nanoworm for Multimodality Breast Cancer Imaging. ACS Appl Nano Mater 2022; 5:18912-18920. [PMID: 37635916 PMCID: PMC10448907 DOI: 10.1021/acsanm.2c04687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Breast cancer is the leading cause of cancer-associated deaths among women. Techniques for non-invasive breast cancer detection and imaging are urgently needed. Multimodality breast cancer imaging is attractive since it can integrate advantages from several modalities, enabling more accurate cancer detection. In order to accomplish this, indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoworm (NW-ICG) has been synthesized as a contrast agent. When evaluated in a spontaneous mouse breast cancer model, NW-ICG gave a large tumor to normal tissue contrasts in multiple imaging modalities including magnetic particle imaging, near-infrared fluorescence imaging, and photoacoustic imaging, providing more comprehensive detection and imaging of breast cancer. Thus, NW-ICGs are an attractive platform for non-invasive breast cancer diagnosis.
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Affiliation(s)
- Chia-Wei Yang
- Department of Chemistry and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Kunli Liu
- Department of Chemistry and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Cheng-You Yao
- Institute for Quantitative Health Science and Engineering and Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Bo Li
- Institute for Quantitative Health Science and Engineering and Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Aniwat Juhong
- Institute for Quantitative Health Science and Engineering and Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Zhen Qiu
- Institute for Quantitative Health Science and Engineering, Department of Electrical and Computer Engineering, and Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xuefei Huang
- Department of Chemistry, Institute for Quantitative Health Science and Engineering, and Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan 48824, United States
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