November 19th, 2018
•We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell types.
Tags
Video correlati
Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology
High-throughput Quantitative Real-time RT-PCR Assay for Determining Expression Profiles of Types I and III Interferon Subtypes
Detection of Trypanosoma brucei Variant Surface Glycoprotein Switching by Magnetic Activated Cell Sorting and Flow Cytometry
Non-invasive In Vivo Fluorescence Optical Imaging of Inflammatory MMP Activity Using an Activatable Fluorescent Imaging Agent
Murine Lymphocyte Labeling by 64Cu-Antibody Receptor Targeting for In Vivo Cell Trafficking by PET/CT
Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry
Analysis of Lymphocyte Extravasation Using an In Vitro Model of the Human Blood-brain Barrier
Light-sheet Microscopy for Three-dimensional Visualization of Human Immune Cells
Label-free Neutrophil Enrichment from Patient-derived Airway Secretion Using Closed-loop Inertial Microfluidics
Real-time Imaging and Quantification of Fungal Biofilm Development Using a Two-Phase Recirculating Flow System