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 ...

University of Cambridge

Two-dimensional (2D) nanomaterials, such as graphene-based materials and transition metal dichalcogenide (TMD) nanosheets, are promising materials for biomedical applications owing to their remarkable cytocompatibility and physicochemical properties. On the basis of their potent antibacterial properties, 2D materials have potential as antibacterial films, wherein the 2D nanosheets are immobilized on the surface and the bacteria may contact with the basal planes of 2D nanosheets dominantly rather than contact with the sharp edges of nanosheets. To address these points, in this study, we prepared an effective antibacterial surface consisting of representative 2D materials, i.e., graphene oxide (GO) and molybdenum disulfide (MoS), formed into nanosheets on a transparent substrate for real device applications. The antimicrobial properties of the GO-MoS nanocomposite surface toward the Gram-negative bacteria Escherichia coli were investigated, and the GO-MoS nanocomposite exhibited enhanced antimicrobial effects with increased glutathione oxidation capacity and partial conductivity. Furthermore, direct imaging of continuous morphological destruction in the individual bacterial cells having contacts with the GO-MoS nanocomposite surface was characterized by holotomographic (HT) microscopy, which could be used to detect the refractive index (RI) distribution of each voxel in bacterial cell and reconstruct the three-dimensional (3D) mapping images of bacteria. In this regard, the decreases in both the volume (67.2%) and the dry mass (78.8%) of bacterial cells that came in contact with the surface for 80 min were quantitatively measured, and releasing of intracellular components mediated by membrane and oxidative stress was observed. Our findings provided new insights into the antibacterial properties of 2D nanocomposite film with label-free tracing of bacterial cell which improve our understanding of antimicrobial activities and opened a window for the 2D nanocomposite as a practical antibacterial film in biomedical applications.

Antibacterial Activities of Graphene Oxide-Molybdenum Disulfide Nanocomposite Films.

Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning.

Emerging clinical interest in combining standard white light endoscopy with targeted near-infrared (NIR) fluorescent contrast agents for improved early cancer detection has created demand for multimodal imaging endoscopes. We used two spectrally resolving detector arrays (SRDAs) to realize a bimodal endoscope capable of simultaneous reflectance-based imaging in the visible spectral region and multiplexed fluorescence-based imaging in the NIR. The visible SRDA was composed of 16 spectral bands, with peak wavelengths in the range of 463 to 648 nm and full-width at half-maximum (FWHM) between 9 and 26 nm. The NIR SRDA was composed of 25 spectral bands, with peak wavelengths in the range 659 to 891 nm and FWHM 7 to 15 nm. The spectral endoscope design was based on a "babyscope" model using a commercially available imaging fiber bundle. We developed a spectral transmission model to select optical components and provide reference endmembers for linear spectral unmixing of the recorded image data. The technical characterization of the spectral endoscope is presented, including evaluation of the angular field-of-view, barrel distortion, spatial resolution and spectral fidelity, which showed encouraging performance. An agarose phantom containing oxygenated and deoxygenated blood with three fluorescent dyes was then imaged. After spectral unmixing, the different chemical components of the phantom could be successfully identified via majority decision with high signal-to-background ratio (>3). Imaging performance was further assessed in an ex vivo porcine esophagus model. Our preliminary imaging results demonstrate the capability to simultaneously resolve multiple biological components using a compact spectral endoscopy system.

Jonghee Yoon

Department of Physics

University of Cambridge

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

Jonghee Yoon

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Physics

University of Cambridge

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

Department of Physics