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Lee I, Lee A, Shin S, Kumar S, Nam MH, Kang KW, Kim BS, Cho SD, Kim H, Han S, Park SH, Seo S, Jun HS. Use of a platform with lens-free shadow imaging technology to monitor natural killer cell activity. Biosens Bioelectron 2024; 261:116512. [PMID: 38908292 DOI: 10.1016/j.bios.2024.116512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 06/17/2024] [Indexed: 06/24/2024]
Abstract
Natural killer (NK) cells are a crucial component of the innate immune system. This study introduces Cellytics NK, a novel platform for rapid and precise measurement of NK cell activity. This platform combines an NK-specific activation stimulator cocktail (ASC) and lens-free shadow imaging technology (LSIT), using optoelectronic components. LSIT captures digital hologram images of resting and ASC-activated NK cells, while an algorithm evaluates cell size and cytoplasmic complexity using shadow parameters. The combined shadow parameter derived from the peak-to-peak distance and width standard deviation rapidly distinguishes active NK cells from inactive NK cells at the single-cell level within 30 s. Here, the feasibility of the system was demonstrated by assessing NK cells from healthy donors and immunocompromised cancer patients, demonstrating a significant difference in the innate immunity index (I3). Cancer patients showed a lower I3 value (161%) than healthy donors (326%). I3 was strongly correlated with NK cell activity measured using various markers such as interferon-gamma, tumor necrosis factor-alpha, perforin, granzyme B, and CD107a. This technology holds promise for advancing immune functional assays, offering rapid and accurate on-site analysis of NK cells, a crucial innate immune cell, with its compact and cost-effective optoelectronic setup, especially in the post-COVID-19 era.
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Affiliation(s)
- Inha Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, Republic of Korea
| | - Ahyeon Lee
- Department of Electronics and Information Engineering, Korea University, Sejong, 30019, Republic of Korea
| | - Sanghoon Shin
- Department of Electronics and Information Engineering, Korea University, Sejong, 30019, Republic of Korea
| | - Samir Kumar
- Department of Electronics and Information Engineering, Korea University, Sejong, 30019, Republic of Korea
| | - Myung-Hyun Nam
- Department of Laboratory Medicine, Anam Hospital, Korea University College of Medicine, Seoul, 02841, Republic of Korea
| | - Ka-Won Kang
- Department of Hematology, Anam Hospital, Korea University College of Medicine, Seoul, 02841, Republic of Korea
| | - Byung Soo Kim
- Department of Hematology, Anam Hospital, Korea University College of Medicine, Seoul, 02841, Republic of Korea
| | - Sung-Dong Cho
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Hawon Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Sunmi Han
- Metaimmunetech Inc., Sejong, 30019, Republic of Korea
| | - Su-Hyung Park
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Sungkyu Seo
- Department of Electronics and Information Engineering, Korea University, Sejong, 30019, Republic of Korea; Metaimmunetech Inc., Sejong, 30019, Republic of Korea.
| | - Hyun Sik Jun
- Department of Biotechnology and Bioinformatics, Korea University, Sejong, 30019, Republic of Korea; Metaimmunetech Inc., Sejong, 30019, Republic of Korea.
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Baik M, Shin S, Kumar S, Seo D, Lee I, Jun HS, Kang KW, Kim BS, Nam MH, Seo S. Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology. BIOSENSORS 2023; 13:993. [PMID: 38131753 PMCID: PMC10741567 DOI: 10.3390/bios13120993] [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: 09/30/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023]
Abstract
Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland-Altman analysis confirmed the model's reliability, with a mean bias of -2.29 and 95% limits of agreement between 18.49 and -23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise.
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Affiliation(s)
- Minyoung Baik
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea; (M.B.); (S.S.); (S.K.)
| | - Sanghoon Shin
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea; (M.B.); (S.S.); (S.K.)
| | - Samir Kumar
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea; (M.B.); (S.S.); (S.K.)
| | - Dongmin Seo
- Department of Electrical Engineering, Semyung University, Jecheon 27136, Republic of Korea;
| | - Inha Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea; (I.L.); (H.S.J.)
| | - Hyun Sik Jun
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea; (I.L.); (H.S.J.)
| | - Ka-Won Kang
- Department of Hematology, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea; (K.-W.K.); (B.S.K.)
| | - Byung Soo Kim
- Department of Hematology, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea; (K.-W.K.); (B.S.K.)
| | - Myung-Hyun Nam
- Department of Laboratory Medicine, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea
| | - Sungkyu Seo
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea; (M.B.); (S.S.); (S.K.)
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Kumar S, Ko T, Chae Y, Jang Y, Lee I, Lee A, Shin S, Nam MH, Kim BS, Jun HS, Seo S. Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays. BIOSENSORS 2023; 13:623. [PMID: 37366988 DOI: 10.3390/bios13060623] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/28/2023]
Abstract
Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, p = 0.008; Pearson correlation coefficient: 0.56, p = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.
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Affiliation(s)
- Samir Kumar
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
| | - Taewoo Ko
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
| | | | - Yuyeon Jang
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea
| | - Inha Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea
| | - Ahyeon Lee
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
| | - Sanghoon Shin
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
| | - Myung-Hyun Nam
- Department of Laboratory Medicine, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea
| | - Byung Soo Kim
- Department of Hematology, Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea
| | - Hyun Sik Jun
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea
| | - Sungkyu Seo
- Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea
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