Georgia Tech engineers Catherine Rivet, Abby Hill and Melissa Kemp (left-right) display a diagram of the microfluidic device they used to assess T cells. The drawing illustrates the different channels corresponding to the eight time points, ranging from 30 seconds to seven minutes, where they collected signaling event measurements from the cells. Credit: Gary Meek
Manipulation of cells by a new microfluidic device may help
clinicians improve a promising cancer therapy that harnesses the body’s own
immune cells to fight such diseases as metastatic melanoma, non-Hodgkin’s
lymphoma, chronic lymphocytic leukemia, and neuroblastoma.
The therapy, known as adoptive T cell transfer, has shown
encouraging results in clinical trials. This treatment involves removing
disease-fighting immune cells called T cells from a cancer patient, multiplying
them in the laboratory and then infusing them back into the patient’s body to
attack the cancer. The effectiveness of this therapy, however, is limited by
the finite lifespan of T cells—after many divisions, these cells become
unresponsive and inactive.
Researchers at Georgia Tech and Emory Univ.
have addressed this limitation by developing a microfluidic device for sample
handling that allows a statistical model to be generated to evaluate cell
responsiveness and accurately predict cell “age” and quality. Being able to
assess the age and responsiveness of T cells—and therefore transfer only young
functional cells back into a cancer patient’s body—offers the potential to
improve the therapeutic outcome of several cancers.
“The statistical model, enabled by the data generated with
the microfluidic device, revealed an optimal combination of extracellular and
intracellular proteins that accurately predict T cell age,” said Melissa Kemp,
an assistant professor in the Wallace H. Coulter Department of Biomedical
Engineering at Georgia Tech and Emory Univ. “Knowing this information will help
facilitate the clinical development of appropriate T cell expansion and
Melissa Kemp, an assistant professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory Univ., shows the microfluidic device for sample handling that allowed a statistical model to be generated to evaluate T cell responsiveness and accurately predict cell age and quality. Credit: Gary Meek
Details on the microfluidic device and statistical model
were published in Molecular & Cellular Proteomics. This work was
supported by the National Institutes of Health, Georgia Cancer Coalition, and Georgia
Tech Integrative BioSystems Institute.
Currently, clinicians measure T cell age by using multiple
assays that rely on measurements from large cell populations. The measurements
determine if cells are exhibiting functions known to appear at different stages
in the life cycle of a T cell.
“Since no one measurement is a perfect predictor, it is
advantageous to concurrently sample multiple proteins at different time points,
which we can do with our microfluidic device,” explained Kemp, who is also a
Georgia Cancer Coalition Distinguished Professor. “The wealth of information we
get from our device for a small number of cells far exceeds a single
measurement from a population the same size by another assay type.”
For their study, Kemp, electrical engineering graduate
student Catherine Rivet and biomedical engineering undergraduate student Abby
Hill analyzed CD8+ T cells from healthy blood donors. They acquired information
from 25 static biomarkers and 48 dynamic signaling measurements and found a
combination of phenotypic markers and protein signaling dynamics—including Lck,
ERK, CD28, and CD27—to be the most useful in predicting cellular age.
To obtain biomarker and dynamic signaling event measurements,
the researchers ran the donor T cells through a microfluidic device designed in
collaboration with Hang Lu, an associate professor in the Georgia Tech
School of Chemical &
Biomolecular Engineering. After stimulating the cells, the device divided them
into different channels corresponding to eight different time points, ranging
from 30 seconds to seven minutes. Then they were divided again into populations
that were chemically treated to halt the biochemical reactions at snapshots in
time to build up a picture of the signaling events that occurred as the T cells
responded to antigen.
“While donor-to-donor variability is a confounding factor in
these types of experiments, the technological platform minimized the
experimental data variance and allowed stimulation time to be precisely
controlled,” said Lu.
Images of the microfluidic device developed at Georgia Tech to help researchers predict T cell age and quality in order to improve a type of cancer therapy called adoptive transfer of T cells. Credit: Gary Meek
With the donor T cell data, the researchers developed a
model to assess which biomarkers or dynamical signaling events best predicted
the quality of T cell function. The model found the most informative data in
predicting cellular age to be the initial changes in signaling dynamics.
“Although a combination of biomarker and dynamic signaling
data provided the optimal model, our results suggest that signaling information
alone can predict cellular age almost as well as the entire dataset,” noted
In the future, Kemp plans to use this approach of combining
multiple cell-based experiments on a microfluidic chip to integrate single-cell
information with population-averaged techniques, such as multiplexed
immunoassays or mass spectrometry.