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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.