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Characterization of Lentiviral Vectors

Physical and functional characterization of lentiviral vectors

Chimeric antigen receptor T (CAR-T) cell therapy is a cellular immunotherapy used to treat cancer.  Lentiviral vectors are used to generate CAR-T cells by providing stable expression of a transgene in a host cell.  Lentivirus is an eveloped, single-stranded RNA virus that is capable of infecting both dividing and non-dividing cells.  Measuring titer and other quality attributes is important for understanding functionality of the lentiviral particles and the potency of the resulting CAR-T therapy.  Different approaches to physical titer measurements can include particle counts, which measure all particles within a sample, capsid titer measurements, which estimate the number of viral particles with an intact capsid, and genomic measurements, which measure the relative amount of RNA within a sample.  The goal of this project is to elucidate how these measurements, which are quantifying different aspects within a sample, impact measurement of functional titers.  We are testing lentiviral preparations from four different sources and have found differences in both physical and functional titer results.

measuring label-free lentiviral transduction in cells using machine learning

The functional quality of lentiviral vectors (LVVs) varies significantly between sources, batches, and individuals.  Transgene function can also be difficult to analyze and usually requires the use of PCR assays, genome sequencing, and/or flow cytometry.  Furthermore, different aspects of lentiviral function and CAR-T function can confound measurements.  These include the ability of each lentivirus to enter and infect a cell, the number of lentivirus copies integrated per transduced cell (i.e., the vector copy number, VCN), the integration sites of each vector copy, the capability of the transduced cell to express the CAR on the cell surface, and the ability of the CAR-T to kill a cancer cell.  Furthermore, the functional results from in vitro measurements are difficult to compare to clinical outcomes thereby preventing straightforward assessment of therapy efficacy.  Flow cytometry is used to measure almost all critical quality attributes (CQAs) demonstrating overall therapy quality during CAR-T manufacturing and for therapy monitoring.  The goal of this project is to develop a label- and reporter-free method to detect, possibly quantify, lentiviral transduction using imaging cytometry and a neural network-based image classifier.  The underlying hypothesis is that transduction-induced protein translation will manifest as imaging artifacts that a neural network can use to discriminate transduced and non-transduced cells.

Two panel schematic showing approach to train a neural network to discriminate between lentivirus transduced and non-transduced for cell therapy production monitoring. Top, a fluorescent reporter is used as ground truth for transduction to teach the neural network brightfield imaging artifacts.  Bottom, model performance can be assessed by supplying only brightfield images.
Approach to neural network classification of lentivirus (LV) transduced and non-transduced single cells using imaging flow cytometry data.  Top, successful transduction with a LV for fluorescent protein expression provides ground truth for neural network classification based on cellular brightfield images.  Bottom, model performance can be assessed without considering fluorescent protein expression, which is typically absent for LV-transduced cell therapies.
Credit: NIST

Return to the Quantitative Flow Cytometry Measurements Program page.

Created February 9, 2025, Updated April 15, 2025