Soft matter mechanics of immune cell aggregates

Abstract

T-cells are a crucial subset of white blood cells that play a central role in the immune system. When T-cells bind antigens, it leads to cell activation and the induction of an immune response. If T-cells are activated by antigens in vivo or artificially in vitro, they form multicellular aggregates. The mechanical properties of such clusters provide valuable information on different T-cell activation pathways. Furthermore, the aggregate mechanics capture how T-cells are affected by mechanical forces and interact within larger conglomerates, such as lymph nodes and tumours. However, an understanding of collective T-cell adhesion and mechanics following cell activation is currently lacking. Probing the mechanics of fragile and microscopically small living samples is experimentally challenging. Here, the micropipette force sensor technique was used to stretch T-cell aggregates and directly measure their Young's modulus and ultimate tensile strength. A mechanistic model was developed to correlate how the stiffness of the mesoscale multicellular aggregate emerges from the mechanical response of the individual microscopic cells within the cluster. We show how the aggregate elasticity is affected by different activators and relate this to different activation pathways in the cells. Our soft matter mechanics study of multicellular T-cell aggregates contributes to our understanding of the biology behind immune cell activation.

Keywords: cellular aggregates; immune cell activation; immune cells; soft matter mechanics.

Figure 1.

In vitro activation of T-cells. (a) Overview of in vitro activators: anti-CD3 binds the T-cell receptors (TCRs) and triggers the downstream activation signalling. PMA and ionomycin together mimic part of this signalling cascade. W-7, a calmodulin inhibitor, interferes with some effects of calcium influx into T-cells. T-cell activation leads to integrin activation, integrin binding to intercellular adhesion molecules (ICAMs), and cellular aggregation. It also induces various other cellular changes, including the expression of T-cell activation markers (not shown). (b) Optical microscopy images of T-cell aggregates (held by two micropipettes) following activation with PMA and ionomycin (PMA+I, left); PMA, ionomycin, and W-7 (PMA+I + W-7, middle); and anti-CD3 (right). Scale bar 50 μm. (c) Expression levels of the early T-cell activation marker CD69 after stimulation with PMA+ionomycin, PMA+ionomycin + W-7, or anti-CD3 at 5 h post-induction or overnight (o/n). Presented as mean fluorescence intensity (MFI) as measured using a flow cytometer. (d) Expression levels of the late T-cell activation marker CD25 under the same conditions. The stars in (c)–(d) indicate statistical significance of Welch’s t‐test of the mean of a pair of groups with **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure 2.

Cell aggregate stretching using the micropipette force sensor. (a) Schematic sketch (not to scale) of the set-up with the straight and L-shaped micropipettes holding on to the T-cell aggregate with suction. The straight micropipette is connected to a linear motor and the L-shaped is calibrated and used as a force sensor. (b) Examples of optical microscopy images from before (top) and after (bottom) the stretching. The aggregate is modelled as a cylinder with an initial radius 𝑅₀ and length 𝐿₀. During the experiment, the straight micropipette is moved to the left at a constant speed (𝑥_S = 𝑣𝑡), causing the L-shaped force sensor to deflect (𝑥). This applies a Hookean force 𝐹 = 𝑘_p𝑥 onto the aggregate, which stretches, 𝐿(𝑡). Scale bar 50 μm. (c) Example of stress–strain graph from a typical stretching experiment. The Young’s modulus 𝐸 and ultimate tensile strength 𝜎_UTS are determined from the graph. The error bars are error propagations of the stress and strain using the standard deviation of 𝑘_p, 𝑅₀ and 𝐿₀ (see §6 for details).

Figure 3.

Mechanistic model. The cells are modelled as spheres with a radius of 𝑟_c and spring constant 𝑘_c. The aggregates with their modelled cell-spring structure are shown in (a) before and (b) during the stretching. Scale bar 50 μm. (c) Schematic drawing of the mechanistic model with parallel connections of series-connected springs acting as force chains through the aggregate. Each spring changes its length by Δ𝑙_c when the external force 𝐹 is applied.

Figure 4.

Effect of time, volume and aspiration. Young’s modulus of T-cell aggregates (activated with PMA and ionomycin) as a function of (a) time after activation, (b) cluster volume and (c) relative cluster volume outside of the micropipettes. Within error, the stiffness remains constant for all cases.

Figure 5.

Mechanical properties. (a) The Young’s modulus and (b) ultimate tensile strength of T-cell aggregates activated with PMA and ionomycin (i). The thick black line inside the box represents the median. The box spans the interquartile range (IQR), covering the middle 50% of the data. The error bars (whiskers) extend to the minimum and maximum values within 1.5 times the IQR, while points beyond this range are considered outliers.

Figure 6.

Effect of pre-stretching. The ratio between the Young’s modulus in the second (𝐸₂) and first (𝐸₁) stretching experiment as a function of maximum strain 𝜀₁ during the pre-stretching. The aggregates soften after the pre-stretching.

Figure 7.

Activation pathways. The Young’s modulus of T-cell aggregates activated with PMA+ionomycin compared with (a) aggregates activated also with W-7 and (b) only with anti-CD3. The addition of W-7 makes the aggregates stiffer, whereas anti-CD3 produces softer aggregates than PMA+ionomycin. The stars indicate **p < 0.01 and ***p < 0.001.