Highlights d A complex HER2 + breast cancer ecosystem is reconstituted and quantitatively described d The effects of the drug trastuzumab (Herceptin) are directly visualized ex vivo d Trastuzumab promotes long cancer-immune interactions and an ADCC immune response d Trastuzumab and CAFs have antagonist immunomodulation effects

In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip approach. To this end, we employ data collected on a microfluidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from ‘wild type’ donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative confirmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems.

Organ-on-chips are miniaturised devices aiming at replacing animal models for drug discovery, toxicology and studies of complex biological phenomena. The field of Organ-On-Chip has grown exponentially, and has led to the formation of companies providing commercial Organ-On-Chip devices. Yet, it may be surprising to learn that the majority of these commercial devices are made from Polydimethylsiloxane (PDMS), a silicone elastomer that is widely used in microfluidic prototyping, but which has been proven difficult to use in industrial settings and poses a number of challenges to experimentalists, including leaching of uncured oligomers and uncontrolled adsorption of small compounds. To alleviate these problems, we propose a new substrate for organ-on-chip devices: Polylactic Acid (PLA). PLA is a material derived from renewable resources, and compatible with high volume production technologies, such as microinjection moulding. PLA can be formed into sheets and prototyped into desired devices in the research lab. In this article we uncover the suitability of Polylactic acid as a substrate material for Microfluidic cell culture and Organ-on-a-chip applications. Surface properties, biocompatibility, small molecule adsorption and optical properties of PLA are investigated and compared with PDMS and other reference polymers.

No abstract

Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic device mimicking the slits of the spleen red pulp area and video data analysis are combined for the characterization of RBCs in RHHA. This microfluidic unit is designed to evaluate RBC deformability by maintaining them fixed in planar orientation, allowing the visual inspection of RBC’s capacity to restore their original shape after crossing microconstrictions. Then, two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%.