3D Cell Migration Chip (3DCM-Chip): A New Tool toward the Modeling of 3D Cellular Complex Systems.

3D hydrogel-based cell cultures provide models for studying cell behavior and can efficiently replicate the physiologic environment. Hydrogels can be tailored to mimic mechanical and biochemical properties of specific tissues and allow to produce gel-in-gel models. In this system, microspheres encapsulating cells are embedded in an outer hydrogel matrix, where cells are able to migrate. To enhance the efficiency of such studies, a lab-on-a-chip named 3D cell migration-chip (3DCM-chip) is designed, which offers substantial advantages over traditional methods. 3DCM-chip facilitates the analysis of biochemical and physical stimuli effects on cell migration/invasion in different cell types, including stem, normal, and tumor cells. 3DCM-chip provides a smart platform for developing more complex cell co-cultures systems. Herein the impact of human fibroblasts on MDA-MB 231 breast cancer cells' invasiveness is investigated. Moreover, how the presence of different cellular lines, including mesenchymal stem cells, normal human dermal fibroblasts, and human umbilical vein endothelial cells, affects the invasive behavior of cancer cells is investigated using 3DCM-chip. Therefore, predictive tumoroid models with a more complex network of interactions between cells and microenvironment are here produced. 3DCM-chip moves closer to the creation of in vitro systems that can potentially replicate key aspects of the physiological tumor microenvironment.

A Lab-on-a-Chip Based Automatic Platform for Continuous Nitrites Sensing in Aquaculture.

In aquaculture, the density of fish stock, use of feeding, and surrounding environmental conditions can easily result in an excessive concentration of harmful compounds that require continuous monitoring. Chemical sensors are available for most of these compounds, however, operative conditions and continuous monitoring in water make the development of sensors suitable for long and unattended deployments difficult. A possible solution is the development of engineered automatic labs where the uptake of sample and the contact with water is reduced and the use of a minimal quantity of reagents enables the implementation of reliable chemical assays. In this paper, a platform for automatic chemical assays is presented. The concept is demonstrated with the detection of nitrites based on the well-known colorimetric Griess reaction. The platform is centered around a lab-on-a-chip where reagents and water samples are mixed. The color of the reaction product is measured with low-cost optoelectronic components. Results show the feasibility of the approach with a minimum detectable concentration of about 0.1 mg/L which is below the tolerance level for aquaculture farms.

Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia.

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%.

NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy.

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.

Rapid Manufacturing of Multilayered Microfluidic Devices for Organ on a Chip Applications.

Microfabrication and Polydimethylsiloxane (PDMS) soft-lithography techniques became popular for microfluidic prototyping at the lab, but even after protocol optimization, fabrication is yet a long, laborious process and partly user-dependent. Furthermore, the time and money required for the master fabrication process, necessary at any design upgrade, is still elevated. Digital Manufacturing (DM) and Rapid-Prototyping (RP) for microfluidics applications arise as a solution to this and other limitations of photo and soft-lithography fabrication techniques. Particularly for this paper, we will focus on the use of subtractive DM techniques for Organ-on-a-Chip (OoC) applications. Main available thermoplastics for microfluidics are suggested as material choices for device fabrication. The aim of this review is to explore DM and RP technologies for fabrication of an OoC with an embedded membrane after the evaluation of the main limitations of PDMS soft-lithography strategy. Different material options are also reviewed, as well as various bonding strategies. Finally, a new functional OoC device is showed, defining protocols for its fabrication in Cyclic Olefin Polymer (COP) using two different RP technologies. Different cells are seeded in both sides of the membrane as a proof of concept to test the optical and fluidic properties of the device.

Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy.

Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from cell trajectories. We propose here a novel peer prediction strategy among cell trajectories, deciphering cell state (tumor vs. nontumor), tumor stage, and response to the anticancer drug etoposide, based on morphology and motility features, solving the strong heterogeneity of individual cell properties. The proposed approach first barcodes cell trajectories, then automatically selects the good ones for optimal model construction (good teacher and test sample selection), and finally extracts a collective response from the heterogeneous populations via cooperative learning approaches, discriminating with high accuracy prostate noncancer vs. cancer cells of high vs. low malignancy. Comparison with standard classification methods validates our approach, which therefore represents a promising tool for addressing clinically relevant issues in cancer diagnosis and therapy, e.g., detection of potentially metastatic cells and anticancer drug screening.

Polylactic is a Sustainable, Low Absorption, Low Autofluorescence Alternative to Other Plastics for Microfluidic and Organ-on-Chip Applications.

Organ-on-chip (OOC) devices are miniaturized devices replacing animal models in drug discovery and toxicology studies. The majority of OOC devices are made from polydimethylsiloxane (PDMS), an elastomer widely used in microfluidic prototyping, but posing a number of challenges to experimentalists, including leaching of uncured oligomers and uncontrolled absorption of small compounds. Here we assess the suitability of polylactic acid (PLA) as a replacement material to PDMS for microfluidic cell culture and OOC applications. We changed the wettability of PLA substrates and demonstrated the functionalization method to be stable over a time period of at least 9 months. We successfully cultured human cells on PLA substrates and devices, without coating. We demonstrated that PLA does not absorb small molecules, is transparent (92% transparency), and has low autofluorescence. As a proof of concept of its manufacturability, biocompatibility, and transparency, we performed a cell tracking experiment of prostate cancer cells in a PLA device for advanced cell culture.

A Camera Sensors-Based System to Study Drug Effects On In Vitro Motility: The Case of PC-3 Prostate Cancer Cells.

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.

Development of a Sensor Node for Remote Monitoring of Plants.

The appraisal of stress in plants is of great relevance in agriculture and any time the transport of living plants is involved. Wireless sensor networks (WSNs) are an optimal solution to simultaneously monitor a large number of plants in a mostly automatic way. A number of sensors are readily available to monitor indicators that are likely related to stress. The most common of them include the levels of total volatile compounds and CO2 together with common physical parameters such as temperature, relative humidity, and illumination, which are known to affect plants' behavior. Recent progress in microsensors and communication technologies, such as the LoRa protocol, makes it possible to design sensor nodes of high sensitivity where power consumption, transmitting distances, and costs are optimized. In this paper, the design of a WSN dedicated to plant stress monitoring is described. The nodes have been tested on European privet (Ligustrum Jonandrum) kept in completely different conditions in order to induce opposite level of stress. The results confirmed the relationship between the release of total Volatile Organic Compounds (VOCs) and the environmental conditions. A machine learning model based on recursive neural networks demonstrates that total VOCs can be estimated from the measure of the environmental parameters.

Learning Cancer-Related Drug Efficacy Exploiting Consensus in Coordinated Motility Within Cell Clusters.

OBJECTIVE: The ability of cells to collectively move is essential in various biological contexts including cancer metastasis. In this paper, we propose an automatic video analysis tool to correlate the cell movement inhibition with replication block induced by dose-dependent chemotherapy administration. METHODS: The novel approach combines individual and collective cell kinematic analysis performed over time-lapse microscopy video frames. Cells are first localized and tracked, and then kinematic descriptors are extracted for each track. Selective track identification is performed assuming diversified cell roles within the same cluster (spontaneously forming groups of cells), and finally individual results are grouped exploiting consensus of coordinated motility within cell clusters. RESULTS: Recognition performance of three different experimental conditions (no drug, 0.5-5 µM merged in the same condition, and 50 µM) reached an average accuracy value of 88% over 958 different tracks collected in 36 clusters of diverse dimensions in eight independent experiments. CONCLUSION: An extensive application of this methodology could give a different point of view of the cancer mechanisms.

Dissecting Effects of Anti-cancer Drugs and Cancer-Associated Fibroblasts by On-Chip Reconstitution of Immunocompetent Tumor Microenvironments.

A major challenge in cancer research is the complexity of the tumor microenvironment, which includes the host immunological setting. Inspired by the emerging technology of organ-on-chip, we achieved 3D co-cultures in microfluidic devices (integrating four cell populations: cancer, immune, endothelial, and fibroblasts) to reconstitute ex vivo a human tumor ecosystem (HER2+ breast cancer). We visualized and quantified the complex dynamics of this tumor-on-chip, in the absence or in the presence of the drug trastuzumab (Herceptin), a targeted antibody therapy directed against the HER2 receptor. We uncovered the capacity of the drug trastuzumab to specifically promote long cancer-immune interactions (>50 min), recapitulating an anti-tumoral ADCC (antibody-dependent cell-mediated cytotoxicity) immune response. Cancer-associated fibroblasts (CAFs) antagonized the effects of trastuzumab. These observations constitute a proof of concept that tumors-on-chip are powerful platforms to study ex vivo immunocompetent tumor microenvironments, to characterize ecosystem-level drug responses, and to dissect the roles of stromal components.

Organs on chip approach: a tool to evaluate cancer -immune cells interactions.

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.