Publications

Affinity maturation is a complex dynamical process allowing the immune system to generate antibodies capable of recognizing antigens. We introduce a model for the evolution of the distribution of affinities across the antibody population in germinal centers. The model is amenable to detailed mathematical analysis and gives insight on the mechanisms through which antigen availability controls the rate of maturation and the expansion of the antibody population. It is also capable, upon maximum-likelihood inference of the parameters, to reproduce accurately the distributions of affinities of IgG-secreting cells we measure in mice immunized against Tetanus Toxoid under largely varying conditions (antigen dosage, delay between injections). Both model and experiments show that the average population affinity depends non-monotonically on the antigen dosage. We show that combining quantitative modeling and statistical inference is a concrete way to investigate biological processes underlying affinity maturation (such as selection permissiveness), hardly accessible through measurements.

Freezing of alum-based vaccines drastically alters their colloidal composition and leads to irreversible cluster formation. The loss of stability is well described, but the impact of frost damage on the functionality of the induced and secreted antibody repertoire has not been studied in detail. We therefore applied our single-cell measurement platform to extract the frequencies of Immunoglobulin G-secreting cells in combination with individual secretion rates and affinities. We showed that, frost-damaged or not, the tested vaccine was able to generate similar frequencies of total and antigen-affine IgG-secreting cells. Additionally, the frost-damaged vaccine stimulated a similar T-cell cytokine secretion pattern when compared to the regularly stored vaccine. However, frost-damaged vaccines induced no efficient affinity maturation and a complete collapse of the affinity distribution was observed. This study unveiled the impact of frost-damage to alum-based vaccines on the induced secreted antibody repertoire, and illustrated the power of functional single-antibody analysis.

Mining the antibody repertoire of plasma cells and plasmablasts could enable the discovery of useful antibodies for therapeutic or research purposes1. We present a method for high-throughput, single-cell screening of IgG-secreting primary cells to characterize antibody binding to soluble and membrane-bound antigens. CelliGO is a droplet microfluidics system that combines high-throughput screening for IgG activity, using fluorescence-based in-droplet single-cell bioassays2, with sequencing of paired antibody V genes, using in-droplet single-cell barcoded reverse transcription. We analyzed IgG repertoire diversity, clonal expansion and somatic hypermutation in cells from mice immunized with a vaccine target, a multifunctional enzyme or a membrane-bound cancer target. Immunization with these antigens yielded 100–1,000 IgG sequences per mouse. We generated 77 recombinant antibodies from the identified sequences and found that 93% recognized the soluble antigen and 14% the membrane antigen. The platform also allowed recovery of ~450–900 IgG sequences from ~2,200 IgG-secreting activated human memory B cells, activated ex vivo, demonstrating its versatility.

Cells can rapidly adapt to changing environments through nongenetic processes; however, the metabolic cost of such adaptation has never been considered. Here we demonstrate metabolic coupling in a remarkable, rapid adaptation process (1 in 1,000 cells adapt per hour) by simultaneously measuring metabolism and division of thousands of individual Saccharomyces cerevisiae cells using a droplet microfluidic system: droplets containing single cells are immobilized in a two-dimensional (2D) array, with osmotically induced changes in droplet volume being used to measure cell metabolism, while simultaneously imaging the cells to measure division. Following a severe challenge, most cells, while not dividing, continue to metabolize, displaying a remarkably wide diversity of metabolic trajectories from which adaptation events can be anticipated. Adaptation requires a characteristic amount of energy, indicating that it is an active process. The demonstration that metabolic trajectories predict a priori adaptation events provides evidence of tight energetic coupling between metabolism and regulatory reorganization in adaptation. This process allows S. cerevisiae to adapt on a physiological timescale, but related phenomena may also be important in other processes, such as cellular differentiation, cellular reprogramming, and the emergence of drug resistance in cancer.

Bone is the most frequent metastasis site for breast cancer. As well as dramatically increasing disease burden, bone metastases are also an indicator of poor prognosis. One of the main challenges in investigating bone metastasis in breast cancer is engineering in vitro models that replicate the features of in vivo bone environments. Such in vitro models ideally enable the biology of the metastatic cells to mimic their in vivo behavior as closely as possible. Here, taking benefit of cutting-edge technologies both in microfabrication and cancer cell biology, we have developed an in vitro breast cancer bone-metastasis model. To do so we first 3D printed a bone scaffold that reproduces the trabecular architecture and that can be conditioned with osteoblast-like cells, a collagen matrix, and mineralized calcium. We thus demonstrated that this device offers an adequate soil to seed primary breast cancer bone metastatic cells. In particular, patient-derived xenografts being considered as a better approach than cell lines to achieve clinically relevant results, we demonstrate the ability of this biomimetic bone niche model to host patient-derived xenografted metastatic breast cancer cells. These patient-derived xenograft cells show a long-term survival in the bone model and maintain their cycling propensity, and exhibit the same modulated drug response as in vivo. This experimental system enables access to the idiosyncratic features of the bone microenvironment and cancer bone metastasis, which has implications for drug testing.

Within the last decade, there has been increasing interest in liquid and solid foams for several industrial uses. In the biomedical field, liquid foams can be used as delivery systems for dermatological treatments, for example, whereas solid foams are frequently used as scaffolds for tissue engineering and drug screening. Most of the foam functionalities are largely correlated to their mechanical properties and their structure, especially bubble/pore size, shape, and interconnectivity. However, the majority of conventional foaming fabrication techniques lack pore size control which can induce important inhomogeneities in the foams and subsequently decrease their performance. In this perspective, new advanced technologies have been introduced, such as microfluidics, which offers a highly controlled production, allowing for design customization of both liquid foams and solid foams obtained through liquid-templating. This short review explores both the fabrication and the characterization of foams, with a focus on solid polymer foams, and sheds the light on how microfluidics can overcome some existing limitations, playing a crucial role in their production for biomedical applications, especially as scaffolds in tissue engineering.

Liquid foams exhibiting long-term stability are a key-challenge in material design. Based on this perspective, new pyridinium polyfluorinated surfactants were synthesized from simple building blocks enabling unusually stable liquid foams. While the batch-generated foams were used for qualitative foaming evaluation, microfluidics allowed a quantitative insight into the aging effects of monodisperse foams.

Flexible dielectrics that harvest mechanical energy via electrostatic effects are excellent candidates as power sources for wearable electronics or autonomous sensors. The integration of a soft dielectric composite (polydimethylsiloxane PDMS-carbon black CB) into two mechanical energy harvesters is here presented. Both are based on a similar cantilever beam but work on different harvesting principles: variable capacitor and triboelectricity. We show that without an external bias the triboelectric beam harvests a net density power of 0.3 μW/cm2 under a sinusoidal acceleration of 3.9g at 40 Hz. In a variable capacitor configuration, a bias of 0.15 V/μm is required to get the same energy harvesting performance under the same working conditions. As variable capacitors’ harvesting performance are quadratically dependent on the applied bias, increasing the bias allows the system to harvest energy much more efficiently than the triboelectric one. The present results make CB/PDMS composites promising for autonomous portable multifunctional systems and intelligent sensors.

There is a large debate on the destabilization mechanism of emulsions. We present a simple technique using mechanical compression to destabilize oil-in-water emulsions. Upon compression of the emulsion, the continuous aqueous phase is squeezed out, while the dispersed oil phase progressively deforms from circular to honeycomb-like shapes. The films that separate the oil droplets are observed to thin and break at a critical oil/water ratio, leading to coalescence events. Electrostatic interactions and local droplet rearrangements do not determine film rupture. Instead, the destabilization occurs like an avalanche propagating through the system, starting at areas where the film thickness is smallest.

A new pressure sensor array, positioned on the bottom plate of a standard torsional rheometer is presented. It is built from a unique piezo-capacitive polymeric foam, and consists of twenty-five capacitive pressure sensors (of surface 4.5$\times$4.5 mm$^2$ each) built together in a 5$\times$5 regular array. The sensor array is used to obtain a local mapping of the normal stresses in complex fluids, which dramatically extends the capability of the rheometer. We demonstrate this with three examples. First, the pressure profile is reconstructed in a polymer solution, which enable the simultaneous measurement of the first and the second normal stress differences $N_1$ and $N_2$, with a precision of 2 Pa. In a second part, we show that negative normal stresses can also be detected. Finally, we focus on the normal stress fluctuations that extend both spatially and temporally ina shear-thickening suspension of cornstarch particles. We evidence the presence of local a unique heterogeneity rotating very regularly. In addition to their low-cost and high versatility, the sensors show here their potential to finely characterize the normal stresses in viscosimetric flows