The processing of particles, cells, and droplets for reactions, analyses, labeling, and coating is an important aspect of many microfluidic workflows. However, performing multi-step processes is typically a laborious and time-consuming endeavor. By exploiting the laminar nature of flow within microchannels, such procedures can benefit in terms of both speed and simplicity. This can be achieved either by manipulating the flow streams around the objects of interest, particularly for the localized perfusion of cells, or by manipulating the objects themselves within the streams via a range of forces. Here, we review the variety of methods that have been employed for performing such "multilaminar flow" procedures on particles, cells, and droplets.

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%.

Over the most recent decades, the development of new biological platforms to study disease progression and drug efficacy has been of great interest due to the high increase in the rate of neurodegenerative diseases (NDDs). Therefore, blood–brain barrier (BBB) as an organ-on-a-chip (OoC) platform to mimic brain-barrier performance could offer a deeper understanding of NDDs as well as a very valuable tool for drug permeability testing for new treatments. A very attractive improvement of BBB-oC technology is the integration of detection systems to provide continuous monitoring of biomarkers in real time and a fully automated analysis of drug permeably, rendering more efficient platforms for commercialization. In this Perspective, an overview of the main BBB-oC configurations is introduced and a critical vision of the BBB-oC platforms integrating electronic read out systems is detailed, indicating the strengths and weaknesses of current devices, proposing the great potential for biosensors integration in BBB-oC. In this direction, we name potential biomarkers to monitor the evolution of NDDs related to the BBB and/or drug cytotoxicity using biosensor technology in BBB-oC.