label-free-identification-lymphocyte-subtypes-using-three-dimensional

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification ...

label-free-high-resolution-3d-imaging-machine-learning-analysis

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Accurate, label-free imaging of intestinal organoids is crucial for studying their morphology, growth dynamics, and responses to environmental stimuli. ...

Quantitative phase imaging (QPI) has emerged as one of the powerful imaging tools for the study of live cells in a non-invasive manner. In particular, multimodal approaches combining QPI and fluorescence microscopic techniques have been recently developed for superior spatiotemporal resolution as well as high molecular specificity. In this review, we briefly summarize recent advances in three-dimensional QPI combined with fluorescence techniques for the correlative study of cell pathophysiology. Through this review, biologists and clinicians can be provided with insights on this rapidly growing field of research and may find broader applications to investigate unrevealed nature in cell physiology and related diseases.

Combining Three-Dimensional Quantitative Phase Imaging and Fluorescence Microscopy for the Study of Cell Pathophysiology.

Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.

Deep learning-based label-free hematology analysis framework using optical diffraction tomography.

Optical diffraction tomography (ODT) enables the three-dimensional (3D) refractive index (RI) reconstruction. However, when the RI difference between a sample and a medium increases, the effects of light scattering become significant, preventing the acquisition of high-quality and accurate RI reconstructions. Herein, we present a method for high-fidelity ODT by introducing non-toxic RI matching media. Optimally reducing the RI contrast enhances the fidelity and accuracy of 3D RI reconstruction, enabling visualization of the morphology and intra-organization of live biological samples without producing toxic effects. We validate our method using various biological organisms, including C. albicans and C. elegans.

High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching.

Organoids, which are miniature in vitro versions of organs, possess significant potential for studying human diseases and elucidating their underlying mechanisms. Live imaging techniques play a crucial role in organoid research and contribute to elucidating the complex structure and dynamic biological phenomena of organoids. However, live, unlabeled high-resolution imaging of native organoids is challenging, primarily owing to the complexities of sample handling and optical scattering inherent in three-dimensional (3D) structures. Additionally, conventional imaging methods fail to capture the real-time dynamic processes of growing organoids. In this study, we introduce low-coherence holotomography as an advanced, label-free, quantitative imaging modality designed to overcome several technical obstacles for long-term live imaging of 3D organoids. We demonstrate the efficacy of low-coherence holotomography by capturing high-resolution morphological details and dynamic activities within mouse small intestinal organoids at subcellular resolution. Moreover, our approach facilitates the distinction between viable and nonviable organoids, significantly enhancing its utility in organoid-based research. This advancement underscores the critical role of live imaging in organoid studies, offering a more comprehensive understanding of these complex systems.

ArticleTotal : 2	Year
Publication title Cited by 12	2018
Publication title Cited by 1	2025

Article	Year
Combining Three-Dimensional Quantitative Phase Imaging and Fluorescence Microscopy for the Study of Cell Pathophysiology. The Yale journal of biology and medicine\| PubMed ID: 30258314	2018
Deep learning-based label-free hematology analysis framework using optical diffraction tomography. Heliyon\| PubMed ID: 37576294	2023
High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching. Biomedical optics express\| PubMed ID: 36589574	2022
Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids via low-coherence holotomography. Experimental & molecular medicine\| PubMed ID: 39349827	2024

Sumin Lee

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