Self-assembling of large ordered DNA arrays using superhydrophobic patterned surfaces.

In this paper we present a simple and robust method to realize highly ordered arrays of stretched and suspended DNA molecules over the millimeter length scale. To this end we used an ad hoc designed superhydrophobic surface made of high aspect-ratio silicon pillars, where we deposited a droplet containing genomic DNA. A precise positioning of DNA strands was achieved by shaping the silicon pillars so that sharpened features resembling tips were included. Such features allowed us to accurately control the droplet de-wetting dynamics, pinning DNA strands in a well-defined position above pillars. The proposed technique has the potential to positively impact on the development of novel DNA chips for genetic analysis.

Infrared microspectroscopy of live cells in microfluidic devices (MD-IRMS): toward a powerful label-free cell-based assay.

Until nowadays most infrared microspectroscopy (IRMS) experiments on biological specimens (i.e., tissues or cells) have been routinely carried out on fixed or dried samples in order to circumvent water absorption problems. In this paper, we demonstrate the possibility to widen the range of in-vitro IRMS experiments to vibrational analysis of live cellular samples, thanks to the development of novel biocompatible IR-visible transparent microfluidic devices (MD). In order to highlight the biological relevance of IRMS in MD (MD-IRMS), we performed a systematic exploration of the biochemical alterations induced by different fixation protocols, ethanol 70% and formaldehyde solution 4%, as well as air-drying on U937 leukemic monocytes by comparing their IR vibrational features with the live U937 counterpart. Both fixation and air-drying procedures affected lipid composition and order as well as protein structure at a different extent while they both induced structural alterations in nucleic acids. Therefore, only IRMS of live cells can provide reliable information on both DNA and RNA structure and on their cellular dynamic. In summary, we show that MD-IRMS of live cells is feasible, reliable, and biologically relevant to be recognized as a label-free cell-based assay.

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. The originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning Convolutional Neural Network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. For each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called "motility style") which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories.

Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial Network.

The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial-temporal burden of video sequences storage. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of "social forces" from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.

The influence of spatial and temporal resolutions on the analysis of cell-cell interaction: a systematic study for time-lapse microscopy applications.

Cell-cell interactions are an observable manifestation of underlying complex biological processes occurring in response to diversified biochemical stimuli. Recent experiments with microfluidic devices and live cell imaging show that it is possible to characterize cell kinematics via computerized algorithms and unravel the effects of targeted therapies. We study the influence of spatial and temporal resolutions of time-lapse videos on motility and interaction descriptors with computational models that mimic the interaction dynamics among cells. We show that the experimental set-up of time-lapse microscopy has a direct impact on the cell tracking algorithm and on the derived numerical descriptors. We also show that, when comparing kinematic descriptors in two diverse experimental conditions, too low resolutions may alter the descriptors' discriminative power, and so the statistical significance of the difference between the two compared distributions. The conclusions derived from the computational models were experimentally confirmed by a series of video-microscopy acquisitions of co-cultures of unlabelled human cancer and immune cells embedded in 3D collagen gels within microfluidic devices. We argue that the experimental protocol of acquisition should be adapted to the specific kind of analysis involved and to the chosen descriptors in order to derive reliable conclusions and avoid biasing the interpretation of results.

Recent advances in superhydrophobic surfaces and their relevance to biology and medicine.

By mimicking naturally occurring superhydrophobic surfaces, scientists can now realize artificial surfaces on which droplets of a few microliters of water are forced to assume an almost spherical shape and an extremely high contact angle. In recent decades, these surfaces have attracted much attention due to their technological applications for anti-wetting and self-cleaning materials. Very recently, researchers have shifted their interest to investigate whether superhydrophobic surfaces can be exploited to study biological systems. This research effort has stimulated the design and realization of new devices that allow us to actively organize, visualize and manipulate matter at both the microscale and nanoscale levels. Such precise control opens up wide applications in biomedicine, as it allows us to directly manipulate objects at the typical length scale of cells and macromolecules. This progress report focuses on recent biological and medical applications of superhydrophobicity. Particular regard is paid to those applications that involve the detection, manipulation and study of extremely small quantities of molecules, and to those that allow high throughput cell and biomaterial screening.