A Model for the Binding of Fluorescently Labeled Anti-Human CD4 Monoclonal Antibodies to CD4 Receptors on Human Lymphocytes

Expanding NIST Calibration of Fluorescent Microspheres for Flow Cytometry to More Fluorescence Channels and Smaller Particles

The National Institute of Standards and Technology (NIST), the National Institutes of Health (NIH) and other industry stakeholders have been working together to enable fluorescence intensities of flow cytometer calibration beads to be assigned quantitative equivalent reference fluorophore (ERF) values with high accuracy and precision. The ultimate goal of this effort is to accurately quantify the number of antibodies bound to individual living cells. The expansion of this effort to assign ERF values to more than 50 fluorescence channels and particles with diameters ranging from 10 μm down to 80 nm is reported here.

Stochastic Reaction-Diffusion Model of the Binding of Monoclonal Antibodies to CD4 Receptors on the Surface of T Cells

A stochastic reaction-diffusion model was developed to describe the binding of labeled monoclonal antibodies (mAbs) to CD4 receptors on the surface of T cells. The mAbs diffused to, adsorbed on, and underwent monovalent and bivalent binding to CD4 receptors on the cell surface. The model predicted the time-dependent nature of all populations involved in the labeling process. At large time, the populations reached equilibrium values, giving the number of antibodies bound to the T cell (ABC) defined as the sum of monovalently and bivalently bound mAbs. The predicted coefficient of variation (CV%) of the (ABC) values translated directly to a corresponding CV% of the measured mean fluorescence intensity (MFI). The predicted CV% was about 0.2% from the intrinsic fluctuations of the stochastic reaction process, about 5% after inclusion of the known fluctuations in the number of available CD4 receptors, and about 11% when fluctuations in bivalent binding affinity were included. The fluorescence detection process is expected to contribute approximately 7%. The abovementioned contributions to CV% sum up to approximately 13%. Work is underway to reconcile the predicted values and the measured values of 17% to 22%.

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches

Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, various microorganisms and parasites in the blood during acute blood infections. All of these rare diagnostic objects can be detected and identified very rapidly to save a patient's life. This review outlines the main techniques of visualization of rare objects in the blood flow, methods for extraction of such objects from the blood flow for further investigations and new approaches to identify the objects automatically with the modern deep learning methods.