Wei Luo

Smartphone fluorescence microscopy has various applications in point-of-care (POC) testing and diagnostics, ranging from e.g., quantification of immunoassays, detection of microorganisms, to sensing of viruses. An important need in smartphone-based microscopy and sensing techniques is to improve the detection sensitivity to enable quantification of extremely low concentrations of target molecules. Here, we demonstrate a general strategy to enhance the detection sensitivity of a smartphone-based fluorescence microscope by using surface-enhanced fluorescence (SEF) created by a thin metal-film. In this plasmonic design, the samples are placed on a silver-coated glass slide with a thin spacer, and excited by a laser-diode from the backside through a glass hemisphere, generating surface plasmon polaritons. We optimized this mobile SEF system by tuning the metal-film thickness, spacer distance, excitation angle and polarization, and achieved ~10-fold enhancement in fluorescence intensity compared to a bare glass substrate, which enabled us to image single fluorescent particles as small as 50 nm in diameter and single quantum-dots. Furthermore, we quantified the detection limit of this platform by using DNA origami-based brightness standards, demonstrating that ~80 fluorophores per diffraction-limited spot can be readily detected by our mobile microscope, which opens up new opportunities for POC diagnostics and sensing applications in resource-limited-settings.

Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact and cost-effective automatic yeast analysis platform (AYAP), which can rapidly measure cell concentration and viability. AYAP is based on digital in-line holography and on-chip microscopy and rapidly images a large field-of-view of 22.5 mm². This lens-free microscope weighs 70 g and utilizes a partially-coherent illumination source and an opto-electronic image sensor chip. A touch-screen user interface based on a tablet-PC is developed to reconstruct the holographic shadows captured by the image sensor chip and use a support vector machine (SVM) model to automatically classify live and dead cells in a yeast sample stained with methylene blue. In order to quantify its accuracy, we varied the viability and concentration of the cells and compared AYAP's performance with a fluorescence exclusion staining based gold-standard using regression analysis. The results agree very well with this gold-standard method and no significant difference was observed between the two methods within a concentration range of 1.4 × 10⁵ to 1.4 × 10⁶ cells per mL, providing a dynamic range suitable for various applications. This lensfree computational imaging technology that is coupled with machine learning algorithms would be useful for cost-effective and rapid quantification of cell viability and density even in field and resource-poor settings.

To synthesize a holographic color image, one can sequentially take three holograms at different wavelengths, e.g., at red (R), green (G) and blue (B) parts of the spectrum and digitally merge them. To speed up the imaging process by a factor of three, a Bayer color sensor-chip can also be used to demultiplex three wavelengths that simultaneously illuminate the sample and digitally retrieve individual set of holograms using the known transmission spectra of the Bayer color filters. However, because the pixels of different channels (R, G, B) on a Bayer color sensor are not at the same physical location, conventional demosaicing techniques generate color artifacts in holographic imaging using simultaneous multi-wavelength illumination. Here we demonstrate that pixel super-resolution can be merged into the color de-multiplexing process to significantly suppress the artifacts in wavelength-multiplexed holographic color imaging. This new approach, termed Demosaiced Pixel Super-Resolution (D-PSR), generates color images that are similar in performance to sequential illumination at three wavelengths and therefore improves the speed of holographic color imaging by 3-fold. D-PSR method is broadly applicable to holographic microscopy applications, where high-resolution imaging and multi-wavelength illumination are desired.

To achieve high-resolution and wide field-of-view, digital holographic imaging techniques need to tackle two major challenges: phase recovery and spatial undersampling. Previously, these challenges were separately addressed using phase retrieval and pixel super-resolution algorithms, which utilize the diversity of different imaging parameters. Although existing holographic imaging methods can achieve large space-bandwidth-products by performing pixel super-resolution and phase retrieval sequentially, they require large amounts of data, which might be a limitation in high-speed or cost-effective imaging applications. Here we report a propagation phasor approach, which for the first time combines phase retrieval and pixel super-resolution into a unified mathematical framework and enables the synthesis of new holographic image reconstruction methods with significantly improved data efficiency. In this approach, twin image and spatial aliasing signals, along with other digital artifacts, are interpreted as noise terms that are modulated by phasors that analytically depend on the lateral displacement between hologram and sensor planes, sample-to-sensor distance, wavelength, and the illumination angle. Compared to previous holographic reconstruction techniques, this new framework results in five- to seven-fold reduced number of raw measurements, while still achieving a competitive resolution and space-bandwidth-product. We also demonstrated the success of this approach by imaging biological specimens including Papanicolaou and blood smears.

Undersampling and pixelation affect a number of imaging systems, limiting the resolution of the acquired images, which becomes particularly significant for wide-field microscopy applications. Various super-resolution techniques have been implemented to mitigate this resolution loss by utilizing sub-pixel displacements in the imaging system, achieved, for example, by shifting the illumination source, the sensor array and/or the sample, followed by digital synthesis of a smaller effective pixel by merging these sub-pixel-shifted low-resolution images. Herein, we introduce a new pixel super-resolution method that is based on wavelength scanning and demonstrate that as an alternative to physical shifting/displacements, wavelength diversity can be used to boost the resolution of a wide-field imaging system and significantly increase its space-bandwidth product. We confirmed the effectiveness of this new technique by improving the resolution of lens-free as well as lens-based microscopy systems and developed an iterative algorithm to generate high-resolution reconstructions of a specimen using undersampled diffraction patterns recorded at a few wavelengths covering a narrow spectrum (10–30 nm). When combined with a synthetic-aperture-based diffraction imaging technique, this wavelength-scanning super-resolution approach can achieve a half-pitch resolution of 250 nm, corresponding to a numerical aperture of ~1.0, across a large field of view (>20 mm²). We also demonstrated the effectiveness of this approach by imaging various biological samples, including blood and Papanicolaou smears. Compared with displacement-based super-resolution techniques, wavelength scanning brings uniform resolution improvement in all directions across a sensor array and requires significantly fewer measurements. This technique would broadly benefit wide-field imaging applications that demand larger space-bandwidth products.

As the primary imaging tool to assist the examination of pathological samples, the conventional light microscope suffers from limited throughput, relatively high cost, bulky size, lack of portability, and requirement for focus adjustment. All of these drawbacks partially limit the use of light microscopy tools in resource-limited settings. Lens-free on-chip microscopy can help to address these drawbacks and achieve high-throughput pathology slide imaging without using lenses or objectives. Here, we review the performance of this lens-free imaging platform by showing examples of its performance with various samples including normal and sickle-cell disease blood smears and human carcinoma of the breast. This lens-free computational microscopy platform is a promising tool that can serve high-throughput pathology needs especially in resource-poor settings.

We demonstrate a high-throughput charged particle analysis platform, which is based on lensfree on-chip microscopy for rapid ion track analysis using allyl diglycol carbonate, i.e., CR-39 plastic polymer as the sensing medium. By adopting a wide-area opto-electronic image sensor together with a source-shifting based pixel super-resolution technique, a large CR-39 sample volume (i.e., 4 cm × 4 cm × 0.1 cm) can be imaged in less than 1 min using a compact lensfree on-chip microscope, which detects partially coherent in-line holograms of the ion tracks recorded within the CR-39 detector. After the image capture, using highly parallelized reconstruction and ion track analysis algorithms running on graphics processing units, we reconstruct and analyze the entire volume of a CR-39 detector within ∼1.5 min. This significant reduction in the entire imaging and ion track analysis time not only increases our throughput but also allows us to perform time-resolved analysis of the etching process to monitor and optimize the growth of ion tracks during etching. This computational lensfree imaging platform can provide a much higher throughput and more cost-effective alternative to traditional lens-based scanning optical microscopes for ion track analysis using CR-39 and other passive high energy particle detectors.

Sizing individual nanoparticles and dispersions of nanoparticles provides invaluable information in applications such as nanomaterial synthesis, air and water quality monitoring, virology, and medical diagnostics. Several conventional nanoparticle sizing approaches exist; however, there remains a lack of high-throughput approaches that are suitable for low-resource and field settings, i.e., methods that are cost-effective, portable, and can measure widely varying particle sizes and concentrations. Here we fill this gap using an unconventional approach that combines holographic on-chip microscopy with vapor-condensed nanolens self-assembly inside a cost-effective hand-held device. By using this approach and capturing time-resolved in situ images of the particles, we optimize the nanolens formation process, resulting in significant signal enhancement for the label-free detection and sizing of individual deeply subwavelength particles (smaller than λ/10) over a 30 mm² sample field-of-view, with an accuracy of ±11 nm. These time-resolved measurements are significantly more reliable than a single measurement at a given time, which was previously used only for nanoparticle detection without sizing. We experimentally demonstrate the sizing of individual nanoparticles as well as viruses, monodisperse samples, and complex polydisperse mixtures, where the sample concentrations can span ∼5 orders-of-magnitude and particle sizes can range from 40 nm to millimeter-scale. We believe that this high-throughput and label-free nanoparticle sizing platform, together with its cost-effective and hand-held interface, will make highly advanced nanoscopic measurements readily accessible to researchers in developing countries and even to citizen-scientists, and might especially be valuable for environmental and biomedical applications as well as for higher education and training programs.