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Recent advances in microfluidic technology and biomaterial science have augmented the use of organ-on-chip (OoC) technology to closely mimic the human pathophysiology. Thus, it is now established that the pericellular micro-environment plays a key role on cell behaviour, such as response to drug compounds. OoC technology have been shown to enable the fabrication of micro-physiological systems recapitulating the key features of the in vivo tissues including, mechanical forces (matrix stiffness, fluid shear stress, and compressive/tensile stress), gradients of key chemical substances (oxygen and nutrients), cell-cell biochemical communication, and cell-matrix interactions. Although a plethora of works describes the use of biomatrices in OoC applications, the specific role of cell-matrix interaction in guiding cell behaviour has been overlooked. This review wants to fill this gap, by systematically analysing the matrix characteristics that guide cell functions including differentiation, proliferation, migration, and homing. Furthermore, a comprehensive classification of biomatrices, along with their use for building tissue-specific OoC is here provided. Finally, a prospect on novel OoC platform development comprising 3D bioprinting and Artificial Intelligence technologies is proposed, providing a new panorama on the future of OoC for drug development.

Detecting circulating tumor cells (CTCs) is a challenge in cancer research. Their dissemination into the blood stream represents a crucial event in the formation of the metastases from the primary tumor. For this reason, targeting CTCs in human liquid biopsies is a warning event for cancer invasiveness, progression, and prognosis. In this regard, by means of the optically induced dielectrophoresis (ODEP) technique, we investigated the response to the electric field, at different frequencies, of human prostatic carcinoma PC3 cells, which mimic CTCs derived from prostate cancer, and human leukemia monocytic THP-1 cells, which simulate circulating monocytes. The obtained spectra of the cell motion descriptors represent the unique identification signature of each cell type.

The use of laser cutter machines to produce porous graphene films is an innovative method for a low-cost production of flexible electrodes for electronics and sensing applications. Here, laser-induced graphene (LIG) is used to produce the gate electrodes of EGFET sensors. LIG electrodes and LIG electrodes functionalized with ZnO and metalloporphyrin-coated ZnO are used as elements of the electronic tongue. The array is tested in a classical experiment aimed at identifying complex food matrices, such as fruit juices. The results demonstrate the feasibility of the approach and provide a solid basis for further array developments.

Summary: One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs. The bigger picture: One of the most challenging frontiers for the automatic understanding of biological systems is fluorescent label-free imaging in which the behavior changes of living being are characterized without cell staining. To this aim, we present here the NeuriTES platform that revisits standard paradigms of video analysis to detect unlabeled objects and correlate the analysis to phenotype evolution of the mechanisms under observation. Through the exploitation of adaptive algorithms and of transfer entropy measures, the platform assures regular cell detection and the possibility to extract reliable parameters related to the evolving cell system. As a proof-of-concept, NeuriTES is applied to two fascinating phenotype investigation scenarios, amyotrophic lateral sclerosis (ALS) disease mechanism and the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Directed graphs assist the biologists with a visual understanding of the mechanisms identified.