field-deployable-lens-free-imaging-platform-for-rapid-label-free

Field-Deployable Lens-Free Imaging Platform for Rapid Label-Free Analysis of Natural Killer Cell Activation

Natural killer (NK) cells are important effectors of innate immunity that eliminate virus-infected and malignant cells. Monitoring NK cell activity is ...

Korea University

Metaimmunetech Inc.

Endometriosis is a common gynecologic disorder characterized by chronic pelvic pain, dysmenorrhea, and infertility. Although this condition places significant financial burden on the healthcare system and negatively affects patient's quality of life, the pathophysiology of the disease remains unclear, and noninvasive diagnostic methods are insufficient. The object of this study was to identify potential biomarkers for endometriosis from peripheral blood. We hypothesized that serum biomarkers modified in endometriosis patients would be detected by multiplex cytokine panel, and identification of a combination of these biomarkers would improve diagnostic power. A total of 141 women, aged 15-52 years with regular menstruation, participated in this study. Twenty-one serum cytokines were detected using the commercially available MILLIPLEX MAP Human Cytokine/Chemokine Kit Panel IV. Among these cytokines, breast- and kidney-expressed chemokine (BRAK)/chemokine (C-X-C motif) ligand 14 (CXCL14) was significantly decreased, and proliferation-inducing ligand (APRIL)/tumor necrosis factor ligand superfamily member 13 (TNFSF13) was significantly increased in endometriosis group. APRIL/TNFSF13 and BRAK/CXCL14 alone or in combination, however, failed to show adequate sensitivity or specificity for the diagnosis of endometriosis. Combination of APRIL/TNFSF13 and BRAK/CXCL14 with serum CA-125 levels yielded significantly higher sensitivity (71.2%) for detecting endometriosis without compromising specificity (80.8%) than CA-125 alone in a logistic regression model (P = 0.050). In conclusion, we identified a biomarker combination that detects endometriosis better than CA125 alone. Therefore, we conclude that multiplex cytokine panel is an efficient method for detecting endometriosis, and analysis of additional cytokine panels may lead to identification of a novel biomarker combination with superior diagnostic power.

Identification of Serum Biomarkers for Diagnosis of Endometriosis Using Multiplex Immunoassays.

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,  = 0.008; Pearson correlation coefficient: 0.56,  = 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.

Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays.

Formononetin is one of the phytoestrogens that functions like a selective estrogen receptor modulator (SERM). In this study, we evaluated the effects of formononetin on endometriosis progression in vitro and in vivo.

Formononetin Inhibits Progression of Endometriosis via Regulation of p27, pSTAT3, and Progesterone Receptor: In Vitro and In Vivo Studies.

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.

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology.

This study aimed to evaluate the adverse effects of particulate matter (PM) exposure on endometrial cells and fertility and to identify possible underlying mechanisms. Thirteen women (aged 15-52 years) were included in this study. Enrolled patients underwent laparoscopic surgery at Gangnam Severance Hospital between 1 January and 31 December 2021. For in vivo experiments, 36 female and nine male C57BL/6 mice were randomly divided into control(vehicle), low-dose(10 mg/kg/d), and high-dose exposure groups(20 mg/kg/d). PM was inhaled nasally for four weeks and natural mating was performed. NIST® SRM® 1648a was used for PM exposure. qRT-PCR, western blotting and Masson's trichrome staining were performed. PM treatment in human endometrial stromal cells induced inflammation with significant upregulation of IL-1β, p-NF-kB, and p-c-Jun compared to those of controls. Additionally, PM treatment significantly increased apoptosis in human endometrial stromal cells by downregulating p-AKT and upregulating p-p53/p53, Cas-3, BAX/Bcl-2, p-AMPK, and p-ERK. After PM treatment, the relative expression of IL-1β, IL-6, TNF-α, p-NF-κB, p-c-Jun, and p-Nrf2/Nrf2 significantly increased in murine endometrium compared to those of the controls. Expression of apoptotic proteins p53, p27, and Cas-3, was also significantly elevated in murine endometrium of the PM exposure group compared to that of the controls. A significant increase in expression of procollagen Ⅰ, and Masson's trichrome staining scores in the murine endometrium was noted after PM treatment. PM treatment significantly decreased ERα expression. After natural mating, all 3 female mice in the control group gave birth to 25 offspring (mean 8.1), whereas in the low-dose PM treatment group, two of three female mice gave birth to nine offspring (mean 4.5). No pregnant mice or offspring was present in the high-dose PM treatment group. PM exposure induces adverse effects on the endometrium through aberrant activation of inflammatory and apoptotic pathways and is associated with detrimental effects on murine fertility.

Particulate matter exposure induces adverse effects on endometrium and fertility via aberrant inflammatory and apoptotic pathways in vitro and in vivo.

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 (I). Cancer patients showed a lower I value (161%) than healthy donors (326%). I 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.

Use of a platform with lens-free shadow imaging technology to monitor natural killer cell activity.

This study aimed to establish the optimal cut-off values for urinary cotinine and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL)to determine smoking status in Korean women over 20 years of age and to assess the correlation of these biomarkers with reproductive health, particularly menopausal age, in postmenopausal women. Utilizing data from the 7th edition of the Korea National Health and Nutrition Examination Survey (2016-2018), researchers included postmenopausal women aged 40-60 years who were within 5 years of menopause. Self-reported smoking status was aligned with biomarkers levels to calculate optimal cut-off values, classifying a total of 503 postmenopausal women into four groups: never smokers (cotinine <0.738 ng/mL, NNAL <1.595 pg/mL), secondhand smokers (SHSrs; cotinine 0.738-37.7 ng/mL, NNAL 1.595-12.35 pg/mL), light current smokers (cotinine 37.7-837 ng/mL, NNAL 12.35-91.55 pg/mg), and heavy current smokers (cotinine >837 ng/mL, NNAL >91.55 pg/mL). Differences in menopausal age were analyzed using Kaplan-Meier curves and log-rank tests. The results indicated significant differences in menopausal age between never smokers and heavy smokers (51.4 ± 3.9 vs. 49.6 ± 3.0 years, p = 0.001) as well as SHSrs and heavy smokers (51.4 ± 3.3 vs. 49.6 ± 3.0 years, p = 0.001) when applying urinary cotinine cutoff values. However, no significant differences in menopausal age were observed based on NNAL cutoffs. These findings suggest urinary cotinine levels correlated more strongly with menopausal age than using urine NNAL levels for defining smoking status among postmenopausal Korean women. Heavy current smokers, as identified by urinary cotinine levels, experienced menopause at an earlier age compared to never smokers and SHSrs.

Association between menopausal age and smoking status defined using urinary cotinine or tobacco-specific nitrosamine metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol: The Korea National Health and Nutrition Examination Survey 2016-2018.

Morphological changes in leukocytes are valuable markers for diseases and immune responses. In our earlier work, we presented Cellytics, a device that uses lens-free shadow imaging technology (LSIT) to monitor natural killer cell activity. Here, we present an improved Cellytics system that has been upgraded to a four-channel configuration to achieve higher throughput while maintaining robust reproducibility for rapid and cost-effective leukocyte analysis. The performance of this multi-channel Cellytics system was improved through refinements to the micro-pinhole chip. Etched pinholes provided better image resolution and clarity compared to drilled pinholes. To stimulate leukocytes, we used an activation stimulator cocktail (ASC) and quantified the resulting morphological changes using shadow-based metrics, including peak-to-peak distance (PPD) and maxima-to-minima standard deviation (MMD-SD). In addition, we developed a new leukocyte activation parameter (LAP) to specifically assess these activation-induced morphological changes. After ASC stimulation, leukocytes showed significantly increased PPD and LAP values and decreased MMD-SD compared to non-activated leukocytes. These results are consistent with the results of the flow cytometric analysis. These results emphasize the potential of Cellytics for the rapid and accurate assessment of leukocyte activation and provide a valuable tool for both clinical diagnostics and basic immunological research.

Multi-Channel Cellytics for Rapid and Cost-Effective Monitoring of Leukocyte Activation.

This study aimed to investigate the effects of dienogest on depressive symptoms and anxiety in patients with endometriosis using the Center for Epidemiologic Studies Depression (CES-D) and State-Trait Anxiety Inventory (STAI).

Association between depressive symptoms and dienogest in patients treated for endometriosis: Using the Center for Epidemiological Studies Depression (CES-D) and the State-Trait Anxiety Inventory (STAI).

Article	Year
Identification of Serum Biomarkers for Diagnosis of Endometriosis Using Multiplex Immunoassays. Reproductive sciences (Thousand Oaks, Calif.)\| PubMed ID: 32046464	2020
Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays. Biosensors\| PubMed ID: 37366988	2023
Formononetin Inhibits Progression of Endometriosis via Regulation of p27, pSTAT3, and Progesterone Receptor: In Vitro and In Vivo Studies. Nutrients\| PubMed ID: 37447325	2023
Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology. Biosensors\| PubMed ID: 38131753	2023
Particulate matter exposure induces adverse effects on endometrium and fertility via aberrant inflammatory and apoptotic pathways in vitro and in vivo. Chemosphere\| PubMed ID: 38810796	2024
Use of a platform with lens-free shadow imaging technology to monitor natural killer cell activity. Biosensors & bioelectronics\| PubMed ID: 38908292	2024
Association between menopausal age and smoking status defined using urinary cotinine or tobacco-specific nitrosamine metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol: The Korea Natio... Reproductive toxicology (Elmsford, N.Y.)\| PubMed ID: 39884397	2025
Multi-Channel Cellytics for Rapid and Cost-Effective Monitoring of Leukocyte Activation. Biosensors\| PubMed ID: 40136941	2025
Association between depressive symptoms and dienogest in patients treated for endometriosis: Using the Center for Epidemiological Studies Depression (CES-D) and the State-Trait Anxiety Inven... European journal of obstetrics, gynecology, and reproductive biology\| PubMed ID: 40555080	2025

Inha Lee

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