Differential Depth Sensing Reduces Cancer Cell Proliferation <i>via</i> Rho-Rac-Regulated Invadopodia

Bone, which is composed of a porous matrix, is one of the principal secondary locations for cancer. However, little is known about the effect of this porous microenvironment in regulating cancer cell proliferation. Here, we examine how the depth of the pores can transduce a mechanical signal and reduce the proliferation of noncancer breast epithelial cells (MCF-10A) and malignant breast cancer cells (MDA-MB-231 and MCF-7) using micrometer-scale topographic features. Interestingly, cells extend actin-rich protrusions, such as invadopodia, to sense the depth of the matrix pore and activate actomyosin contractility to decrease MCF-10A proliferation. However, in MDA-MB-231, depth sensing inactivates Rho-Rac-regulated actomyosin contractility and phospho-ERK signaling. Inhibiting contractility on this porous matrix using blebbistatin further reduces MDA-MB-231 proliferation. Our findings support the notion of mechanically induced dormancy through depth sensing, where invadopodia-mediated depth sensing can inhibit the proliferation of noncancer and malignant breast cancer cells through differential regulation of actomyosin contractility

Effects of cytoskeleton, nucleus and viscosity on whole cell model mechanics.

<p>(a-c) The simulation results for the effect of the cytoskeleton filaments number density <i>N</i>_<i>fil</i> on elastic modulus (orange) and velocity (green) of small, medium and large size cells. (d) Influence of the cross-links <i>N</i>_<i>CL</i> to filaments <i>N</i>_<i>fil</i> density ratio on elastic modulus (orange) and on velocity (green). (e) Influence of elastic modulus on the velocity for the case when the stiffness is changed by varying filaments density <i>N</i>_<i>fil</i> (green) or cross-links density <i>N</i>_<i>CL</i> (orange). (f) Dependence of cell elastic modulus and velocity on nuclear-cytoplasmic ratio. (g) Effect of filaments number density representing chromatin inside the nucleus on cell velocity. (h) The impact of nuclear laminar properties varied using parameter in the nucleus membrane model on the cell velocity. (i) Effect of viscosity on cell velocity in microfluidic device. Error bars on all plots show standard deviation.</p

DPD parameters listed in the format of <i>a</i>/<i>γ</i>.

<p>Values shaded with yellow describe interactions with <i>R</i>_<i>c</i> = 0.5 for repulsive interaction while <i>R</i>_<i>c</i> = 1 for thermostat. For dark gray, <i>R</i>_<i>c</i> = 0.5. The parameters values have been obtained from simulations.</p

Non-tumorigenic breast epithelial cell (MCF-10A) and its model.

<p>a) Microscopy image of a cell with nucleus shown in blue. b) Three component cell model: cell membrane is shown in gray, nucleus is in green, the cytoskeleton is in orange with light blue, the connections between cytoskeleton and membranes are in black. Cytoskeleton network model is composed of long and stiff filaments (orange) connected by short cross-links (light blue).</p

List of major parameters with their values.

<p>The source of the parameter values is in the last column. If a value is taken from literature, the reference is given. Parameters, for which values were found through simulation in Sections Cytoskeleton model and Membrane model are marked with ^‡ and * symbols, respectively.</p

Microfluidic experiments and simulations.

<p>a) Microscopy image of section of the device. b) Microscopy of a MCF-10A cell squeezing between obstacles. (c-d) Simulation snapshots for a MCF-10A cell model squeezing between two diverging constrictions. Fluid particles are omitted. e) Comparison between experiments and simulations for the cell velocities, error bars represent standard deviation.</p

Micropipette aspiration experiments and simulations.

<p>a) A representative microscopy of a cell undergoing micropipette aspiration. b) Simulation snapshot of a cell during the micropipette aspiration. c) Comparison between experimental data and simulation for micropipette aspiration, where <i>L</i>_<i>n</i> is normalized indentation length and Δ<i>P</i> is aspiration pressure. The gray area represents standard deviation for experimental data, standard deviation bars for simulations are omitted as they are smaller than the symbols. d) Cell viscosity, <i>η</i>, as a function of dissipative force parameter <i>γ</i> and cutoff length <i>R</i>_<i>c</i> obtained from micropipette aspiration simulations.</p

Single Cell Analysis of Leukocyte Protease Activity Using Integrated Continuous-Flow Microfluidics

Leukocytes are the essential cells of the immune system that protect the human body against bacteria, viruses, and other foreign invaders. Secretory products of individual leukocytes, such as matrix metalloproteinases (MMPs) and a disintegrin and metalloproteinase (ADAMs), are critical for regulating the inflammatory response and mediating host defense. Conventional single cell analytical methods, such as flow cytometry for cellular surface biomarker studies, are insufficient for performing functional assays of the protease activity of individual leukocytes. Here, an integrated continuous-flow microfluidic assay is developed to effectively detect secretory protease activity of individual viable leukocytes. Leukocytes in blood are first washed on-chip with defined buffer to remove background activity, followed by encapsulating individual leukocytes with protease sensors in water-in-oil droplets and incubating for 1 h to measure protease secretion. With this design, single leukocyte protease profiles under naive and phorbol 12-myristate 13-acetate (PMA)-stimulated conditions are reliably measured. It is found that PMA treatment not only elevates the average protease activity level but also reduces the cellular heterogeneity in protease secretion, which is important in understanding immune capability and the disease condition of individual patients

Triple-State Liquid-Based Microfluidic Tactile Sensor with High Flexibility, Durability, and Sensitivity

We develop a novel triple-state liquid-based resistive microfluidic tactile sensor with high flexibility, durability, and sensitivity. It comprises a platinum-cured silicone microfluidic assembly filled with 2 μL liquid metallic alloy interfacing two screen-printed conductive electrodes on a polyethylene terephthalate (PET) film. This flexible tactile sensor is highly sensitive ((2–20) × 10^–3 kPa^–1) and capable of distinguishing compressive loads with an extremely large range of pressure (2 to 400 kPa) as well as bending loads. Owing to its unique and durable structure, the sensor can withstand numerous severe mechanical load, such as foot stomping and a car wheel rolling over it, without compromising its electrical signal stability and overall integrity. Also, our sensing device is highly deformable, wearable, and able to differentiate and quantify pressures exerted by distinct bodily actions, such as a finger touch or footstep pressure. As a proof-of-concept of the applicability of our tactile sensor, we demonstrate the measurements of localized dynamic foot pressure by embedding the sensor inside the shoes and high heels. This work highlights the potential of the liquid-based microfluidic tactile sensing platform in a wide range of applications and can facilitate the realization of functional liquid-state sensing device technology with superior mechanical flexibility, durability, and sensitivity

Contribution of CD80 and CD86 to Interaction Forces and Cytokine Secretion.

<p>(<b>A</b>) Interaction forces; (<b>B</b>) IL-2 and (<b>C</b>) IFN-γ production of DC:T-cell or B:T-cell conjugates in the presence of blocking antibodies against CD80 and/or CD86. DCs or B-cells were pre-pulsed with Ova peptides (+Ova, 10 ng/ml) before antibody blocking. All force measurements were conducted with contact time of 3 min. *p<0.01; unpaired <i>t</i>-test. For each condition, OT-I T-cells were isolated from >3 independent experiments. αCD80: blocking antibody targeting CD80; αCD86: blocking antibody targeting CD86; αCD80/86: αCD80 and αCD86 simultaneously; IgG: isotype controls for both αCD80 and αCD86.</p