Experiments

With the z-Movi® Cell Avidity Analyzer, research scientists can now investigate functional interaction properties upon immune cell target-binding that correspond to immune cell response. Cell avidity measurements are applicable to study several types of immune cells , including CAR-T cells, TCR T cells, NK cells and bispecific antibody-engaged T cells.

Identify the Goldilocks CAR: Cell avidity as a parameter for optimal CAR-antigen combinations

This study aimed to identify CAR T cells that bind high antigen-expressing targets but not low antigen-expressing cells.

Researchers validated low functioning and high functioning CAR T cells based on functional assays and used non-transduced T cells as a negative control (NC). T cell avidities were tested on adherent cells with low antigen expression, high antigen expression, or no antigen expression.

The cell avidity data correlated with a corresponding in vitro cell killing assay, showing antigen-dependent binding strengths and cell killing, respectively (Figure 1). The cell-killing results also demonstrated significantly higher toxicity induced by high functioning CAR T cells on low antigen-expressing healthy-like cells (Figure 2).

The z-Movi® can distinguish between T cell specificities to improve CAR T cell therapy. Cell avidity is a reliable readout for identifying optimal CARs that can minimize on-target/off-tumor activity by CAR T cells.

1 Avidity scores based on the average detaching forces of the CAR T-cell populations relative to the non-transduced T-cell control population.

2 Cytotoxic effect of high and low functionality CARs on target cells with different antigen expression levels.

1 Avidity scores based on the average detaching forces of the CAR T-cell populations relative to the non-transduced T-cell control population.

2 Cytotoxic effect of high and low functionality CARs on target cells with different antigen expression levels.

Data courtesy of Dr. Steven Albelda at University of Pennsylvania

Improve the functional correlation of TCR T
cells by replacing affinity with avidity

This study aimed to screen a panel of affinity-tuned TCR T cells and find the most potent candidates.

T cells with high antigen-affinity TCRs are, in general, functionally superior to those with low antigen-affinity. However, upon breaching an optimum, T-cell functionality diminishes, and negative T-cell regulation increases. The researchers selected three TCR constructs based on their affinity to evaluate T-cell avidities upon interaction with a melanoma cell line (Figure 3).

NY-ESO-1 peptide pulsing on QMα and DMβ resulted in marked avidity enhancement compared with the non-pulsed conditions  (Figure 4).  In contrast to affinity measurements, the avidity data correlated with T-cell function, explaining the diminishing results observed among T cells with the highest affinities.

The reverse relationship between affinities and avidities of QMα and DMβ can explain reduced functionalities of high-affinity TCR T cells. This outcome suggests that avidity is a better predictor of TCR T-cell functionality compared with affinity.

Avidity Curve-Na8 Peptide Pulsing WT
Avidity Curve-Na8 Peptide Pulsing DM
Avidity Curve-Na8 Peptide Pulsing QM

3 Panel of TCR-transduced T cells based on their affinity. WT (baseline), DMβ (medium affinity), and QMα (high affinity) were selected for the experiment.

4 Avidity curves showing the proportion of bound TCR T cells to non-pulsed or peptide-pulsed Na8 melanoma cells at increasing acoustic forces. Each plot represents measurements performed on WT (left), DMβ (center), or QMα (right). (rForce indicates relative force.)

3 Panel of TCR-transduced T cells based on their affinity levels. WT (baseline), DMβ (medium affinity) and QMα (high affinity) were selected for the experiment.

Avidity Curve-Na8 Peptide Pulsing WT
Avidity Curve-Na8 Peptide Pulsing DM
Avidity Curve-Na8 Peptide Pulsing QM

4 Avidity curves showing the proportion of bound non-pulsed or peptide-pulsed TCR T cells at increasing acoustic forces. Each plot represents measurements performed on WR (above), DMβ (center), or QMα (below). (rForce indicates relative force).

Data courtesy of Dr. Nathalie Rufer at the University of Lausanne.

Read more: Hebstein et al., (2013) Front. Immunol.

Use avidity to identify the best NK cell with optimal response

Glycostem researchers compared cell killing and cell avidity data of NK cells derived from two different donors (donor 1 and donor 2).

NK cells from donor 2 induced 3-fold higher cytotoxicity compared with  NK cells from donor 1 (74% and 24%, respectively). NK cells from donor 2 required higher forces than those from donor 1 to detach from target cells (Figure 5).

The data suggest that cell avidity is a reliable predictor of NK cell function and that cell avidity analyses can be applied to different types of immune cells.

5 Left: Cytotoxic effect of NK cells from donor 1 and donor 2 on the same population of target cells. Right: Avidity curve showing the proportion of target-bound NK cells upon application of a force ramp. (rForce indicates relative force).

Avidity - Curve Natural Killer

NK cells from donor 2 induced 3-fold higher cytotoxicity compared with  NK cells from donor 1 (74% and 24%, respectively). NK cells from donor 2 required higher forces than those from donor 1 to detach from target cells (Figure 5).

The data suggest that cell avidity is a reliable predictor of NK cell function and that cell avidity analyses can be applied to different types of immune cells.

Avidity - Curve Natural Killer

5 Above: Cytotoxic effect of NK cells from donor 1 (green) and donor 2 (green) on their target cells. Below: Avidity curve showing the proportion target-bound NK cells upon application of a force ramp. (rForce indicates relative force).

Data courtesy of Glycosystem

Evaluate the avidity of T cells engaged by bispecific antibodies

In a collaborative study, we compared cell avidities between T cells and target cancer cells in the presence of bispecific antibodies (BsAb) or control conditions (no antibodies or non-specific antibodies).

T cells with BsAb required stronger pulling forces to detach from the tumor cells when compared with the two controls (Figure 6).

Future cell avidity analyses of engineered BsAb, will enable researchers to discover and fine-tune optimal antibodies for T cell therapies.

6 Left: Avidity curve representing the average proportion of bound T cells (BsAb-engaged or control conditions) upon an applied force ramp. Right: Bar graph representing percentage of target-bound T cells at the minimal force (based on no BsAb negative control-detachment) as gated from the avidity curve. (rForce indicates relative force).

T cells with BsAb required stronger pulling forces to detach from the tumor cells when compared with the two controls (Figure 6).

Future cell avidity analyses of engineered BsAb, will enable researchers to discover and fine-tune optimal antibodies for T cell therapies.

6 Above: Avidity curve representing the average proportion of bound BsAb-engaged T cells upon increasing forces. Below: Bar graph representing percentage of target-bound T cells at the minimal force detaching the negative control (no BsAb) gated from the avidity curve. (rForce indicates relative force).

Cell–extracellular matrix interactions: Unraveling the kinetics and strength of cellular adhesion

The z-Movi® is a useful tool to obtain insights into cell adhesion processes. Here, Kamsma et al. used the z-Movi® to resolve the adhesion forces and kinetics between CD4+ T lymphocytes (CD4) and fibronectin.

They identified three interaction states between the cells: unbound, binding, and bound. Interaction strengths below 30 pN were defined for unbound cells, between 30 pN and 55 pN for transiently binding and crawling cells, and 55 pN and above for bound cells (Figure 7).

The researchers then investigated how these properties are influenced by interleukin-7 (IL7), the main regulatory cytokine of CD4 cells. The results demonstrated that while IL7 accelerated CD4 adhesion, it did not influence CD4 avidity (Figures 8 and 9).

7 Cumulative probability distribution plot of the rupture forces shown for unbound, binding, and bound cells, IL7-activated and non-activated.

8 Adhesion of CD4 to the fibronectin-functionalized glass, challenged with increasing concentrations of peptide inhibitors (RGD; plain line GRGDS; dashed  ine). This experiment was performed on three different blood donors (error bars denote SEM).

Fraction cells bound as a function of time, plotted for non-activated and IL-7-activated CD4 on glass functionalized or not with fibronectin.

Figures 1,2,3 were reprinted with permission from Cell Reports, 2018, 24 (11), pp 3008-3016. Copyright 2018 Elsevier.

Read more: Kamsma et al. (2018) Cell Reports

z-Movi® Cell Avidity Analyzer

Measure binding strengths between effector cells and their targets with the z-Movi® Cell Avidity Analyzer to accelerate the development of immunotherapeutic strategies.

z-Movi® is a unique instrument that enables you to identify the most potent effector cells by quantifying avidity for their target cell.
This new technology provides you with predictive, reproducible, and fast high-throughput results at the single-cell level without compromising cell viability. All within a compact little box that is safe and easy to use.

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