Molecular detection of SARS-COV-2 in exhaled breath at the point-of-need.

The SARS-CoV-2 pandemic has highlighted the need for improved technologies to help control the spread of contagious pathogens. While rapid point-of-need testing plays a key role in strategies to rapidly identify and isolate infectious patients, current test approaches have significant shortcomings related to assay limitations and sample type. Direct quantification of viral shedding in exhaled particles may offer a better rapid testing approach, since SARS-CoV-2 is believed to spread mainly by aerosols. It assesses contagiousness directly, the sample is easy and comfortable to obtain, sampling can be standardized, and the limited sample volume lends itself to a fast and sensitive analysis. In view of these benefits, we developed and tested an approach where exhaled particles are efficiently sampled using inertial impaction in a micromachined silicon chip, followed by an RT-qPCR molecular assay to detect SARS-CoV-2 shedding. Our portable, silicon impactor allowed for the efficient capture (>85%) of respiratory particles down to 300 nm without the need for additional equipment. We demonstrate using both conventional off-chip and in-situ PCR directly on the silicon chip that sampling subjects' breath in less than a minute yields sufficient viral RNA to detect infections as early as standard sampling methods. A longitudinal study revealed clear differences in the temporal dynamics of viral load for nasopharyngeal swab, saliva, breath, and antigen tests. Overall, after an infection, the breath-based test remains positive during the first week but is the first to consistently report a negative result, putatively signalling the end of contagiousness and further emphasizing the potential of this tool to help manage the spread of airborne respiratory infections.

Fast compressive lens-free tomography for 3D biological cell culture imaging.

We present a compressive lens-free technique that performs tomographic imaging across a cubic millimeter-scale volume from highly sparse data. Compared with existing lens-free 3D microscopy systems, our method requires an order of magnitude fewer multi-angle illuminations for tomographic reconstruction, leading to a compact, cost-effective and scanning-free setup with a reduced data acquisition time to enable high-throughput 3D imaging of dynamic biological processes. We apply a fast proximal gradient algorithm with composite regularization to address the ill-posed tomographic inverse problem. Using simulated data, we show that the proposed method can achieve a reconstruction speed ∼10× faster than the state-of-the-art inverse problem approach in 3D lens-free microscopy. We experimentally validate the effectiveness of our method by imaging a resolution test chart and polystyrene beads, demonstrating its capability to resolve micron-size features in both lateral and axial directions. Furthermore, tomographic reconstruction results of neuronspheres and intestinal organoids reveal the potential of this 3D imaging technique for high-resolution and high-throughput biological applications.

Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks.

Lens-free holographic microscopy (LFHM) provides a cost-effective tool for large field-of-view imaging in various biomedical applications. However, due to the unit optical magnification, its spatial resolution is limited by the pixel size of the imager. Pixel super-resolution (PSR) technique tackles this problem by using a series of sub-pixel shifted low-resolution (LR) lens-free holograms to form the high-resolution (HR) hologram. Conventional iterative PSR methods require a large number of measurements and a time-consuming reconstruction process, limiting the throughput of LFHM in practice. Here we report a deep learning-based PSR approach to enhance the resolution of LFHM. Compared with the existing PSR methods, our neural network-based approach outputs the HR hologram in an end-to-end fashion and maintains consistency in resolution improvement with a reduced number of LR holograms. Moreover, by exploiting the resolution degradation model in the imaging process, the network can be trained with a data set synthesized from the LR hologram itself without resorting to the HR ground truth. We validated the effectiveness and the robustness of our method by imaging various types of samples using a single network trained on an entirely different data set. This deep learning-based PSR approach can significantly accelerate both the data acquisition and the HR hologram reconstruction processes, therefore providing a practical solution to fast, lens-free, super-resolution imaging.

A Micropatterned Multielectrode Shell for 3D Spatiotemporal Recording from Live Cells.

Microelectrode arrays (MEAs) have proved to be useful tools for characterizing electrically active cells such as cardiomyocytes and neurons. While there exist a number of integrated electronic chips for recording from small populations or even single cells, they rely primarily on the interface between the cells and 2D flat electrodes. Here, an approach that utilizes residual stress-based self-folding to create individually addressable multielectrode interfaces that wrap around the cell in 3D and function as an electrical shell-like recording device is described. These devices are optically transparent, allowing for simultaneous fluorescence imaging. Cell viability is maintained during and after electrode wrapping around the cel and chemicals can diffuse into and out of the self-folding devices. It is further shown that 3D spatiotemporal recordings are possible and that the action potentials recorded from cultured neonatal rat ventricular cardiomyocytes display significantly higher signal-to-noise ratios in comparison with signals recorded with planar extracellular electrodes. It is anticipated that this device can provide the foundation for the development of new-generation MEAs where dynamic electrode-cell interfacing and recording substitutes the traditional method using static electrodes.