Sub-picomolar lateral flow antigen detection with two-wavelength imaging of composite nanoparticles.

Lateral flow tests, commonly based on metal plasmonic nanoparticles, are rapid, robust, and low-cost. However, improvements in analytical sensitivity are required to allow detection of low-abundance biomarkers, for example detection of low antigen concentrations for earlier or asymptomatic diagnosis of infectious diseases. Efforts to improve sensitivity often require changes to the assay. Here, we developed optical methods to improve the sensitivity of absorption-based lateral flow tests, requiring no assay modifications to existing tests. We experimentally compared five different lock-in and subtraction-based methods, exploiting the narrow plasmonic peak of gold nanoparticles for background removal by imaging at different light wavelengths. A statistical framework and three fitting models were used to compare limits of detection, giving a 2.0-5.4-fold improvement. We then demonstrated the broad applicability of the method to an ultrasensitive assay, designing 530 nm composite nanoparticles to increase the particle volume, and therefore light absorption per particle, whilst retaining the plasmonic peak to allow background removal and without adding any assay steps. This multifaceted, modular approach gave a combined 58-fold improvement in the fundamental limit of detection using a biotin-avidin model over 50 nm gold nanoparticles with single-wavelength imaging. Applying to a sandwich assay for the detection of HIV capsid protein gave a limit of detection of 170 fM. Additionally, we developed an open-source software tool for performing the detection limit analysis used in this work.

Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer.

We report a field-portable and cost-effective imaging flow cytometer that uses deep learning and holography to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL h-1. This flow cytometer uses lens free color holographic imaging to capture and reconstruct phase and intensity images of microscopic objects in a continuously flowing sample, and automatically identifies Giardia lamblia cysts in real-time without the use of any labels or fluorophores. The imaging flow cytometer is housed in an environmentally-sealed enclosure with dimensions of 19 cm × 19 cm × 16 cm and weighs 1.6 kg. We demonstrate that this portable imaging flow cytometer coupled to a laptop computer can detect and quantify, in real-time, low levels of Giardia contamination (e.g., <10 cysts per 50 mL) in both freshwater and seawater samples. The field-portable and label-free nature of this method has the potential to allow rapid and automated screening of drinking water supplies in resource limited settings in order to detect waterborne parasites and monitor the integrity of the filters used for water treatment.

Smartphone-based turbidity reader.

Water quality is undergoing significant deterioration due to bacteria, pollutants and other harmful particles, damaging aquatic life and lowering the quality of drinking water. It is, therefore, important to be able to rapidly and accurately measure water quality in a cost-effective manner using e.g., a turbidimeter. Turbidimeters typically use different illumination angles to measure the scattering and transmittance of light through a sample and translate these readings into a measurement based on the standard nephelometric turbidity unit (NTU). Traditional turbidimeters have high sensitivity and specificity, but they are not field-portable and require electricity to operate in field settings. Here we present a field-portable and cost effective turbidimeter that is based on a smartphone. This mobile turbidimeter contains an opto-mechanical attachment coupled to the rear camera of the smartphone, which contains two white light-emitting-diodes to illuminate the water sample, optical fibers to transmit the light collected from the sample to the camera, an external lens for image formation, and diffusers for uniform illumination of the sample. Including the smartphone, this cost-effective device weighs only ~350 g. In our mobile turbidimeter design, we combined two illumination approaches: transmittance, in which the optical fibers were placed directly below the sample cuvette at 180° with respect to the light source, and nephelometry in which the optical fibers were placed on the sides of the sample cuvette at a 90° angle with respect to the to the light source. Images of the end facets of these fiber optic cables were captured using the smart phone and processed using a custom written image processing algorithm to automatically quantify the turbidity of each sample. Using transmittance and nephelometric readings, our mobile turbidimeter achieved accurate measurements over a large dynamic range, from 0.3 NTU to 2000 NTU. The accurate performance of our smartphone-based turbidimeter was also confirmed with various water samples collected in Los Angeles (USA), bacteria spiked water samples, as well as diesel fuel contaminated water samples. Having a detection limit of ~0.3 NTU, this cost-effective smartphone-based turbidimeter can be a useful analytical tool for screening of water quality in resource limited settings.

Computational On-Chip Imaging of Nanoparticles and Biomolecules using Ultraviolet Light.

Significant progress in characterization of nanoparticles and biomolecules was enabled by the development of advanced imaging equipment with extreme spatial-resolution and sensitivity. To perform some of these analyses outside of well-resourced laboratories, it is necessary to create robust and cost-effective alternatives to existing high-end laboratory-bound imaging and sensing equipment. Towards this aim, we have designed a holographic on-chip microscope operating at an ultraviolet illumination wavelength (UV) of 266 nm. The increased forward scattering from nanoscale objects at this short wavelength has enabled us to detect individual sub-30 nm nanoparticles over a large field-of-view of >16 mm2 using an on-chip imaging platform, where the sample is placed at ≤0.5 mm away from the active area of an opto-electronic sensor-array, without any lenses in between. The strong absorption of this UV wavelength by biomolecules including nucleic acids and proteins has further enabled high-contrast imaging of nanoscopic aggregates of biomolecules, e.g., of enzyme Cu/Zn-superoxide dismutase, abnormal aggregation of which is linked to amyotrophic lateral sclerosis (ALS) - a fatal neurodegenerative disease. This UV-based wide-field computational imaging platform could be valuable for numerous applications in biomedical sciences and environmental monitoring, including disease diagnostics, viral load measurements as well as air- and water-quality assessment.

Air quality monitoring using mobile microscopy and machine learning.

Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 µm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency-approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.

Quantitative Fluorescence Sensing Through Highly Autofluorescent, Scattering, and Absorbing Media Using Mobile Microscopy.

Compact and cost-effective systems for in vivo fluorescence and near-infrared imaging in combination with activatable reporters embedded inside the skin to sample interstitial fluid or blood can enable a variety of biomedical applications. However, the strong autofluorescence of human skin creates an obstacle for fluorescence-based sensing. Here we introduce a method for quantitative fluorescence sensing through highly autofluorescent, scattering, and absorbing media. For this, we created a compact and cost-effective fluorescence microscope weighing <40 g and used it to measure various concentrations of a fluorescent dye embedded inside a tissue phantom, which was designed to mimic the optical characteristics of human skin. We used an elliptical Gaussian beam excitation to digitally separate tissue autofluorescence from target fluorescence, although they severely overlap in both space and optical spectrum. Using ∼10-fold less excitation intensity than the safety limit for skin radiation exposure, we successfully quantified the density of the embedded fluorophores by imaging the skin phantom surface and achieved a detection limit of ∼5 × 10(5) and ∼2.5 × 10(7) fluorophores within ∼0.01 µL sample volume that is positioned 0.5 and 2 mm below the phantom surface, corresponding to a concentration of 105.9 pg/mL and 5.3 ng/mL, respectively. We also confirmed that this approach can track the spatial misalignments of the mobile microscope with respect to the embedded target fluorescent volume. This wearable microscopy platform might be useful for designing implantable biochemical sensors with the capability of spatial multiplexing to continuously monitor a panel of biomarkers and chronic conditions even at patients' home.

Propagation phasor approach for holographic image reconstruction.

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.

Pixel super-resolution using wavelength scanning.

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.

Enhanced light collection in fluorescence microscopy using self-assembled micro-reflectors.

In fluorescence microscopy, the signal-to-noise ratio (SNR) of the optical system is directly linked to the numerical aperture (NA) of the microscope objective, which creates detection challenges for low-NA, wide-field and high-throughput imaging systems. Here we demonstrate a method to increase the light collection efficiency from micron-scale fluorescent objects using self-assembled vapor-condensed polyethylene glycol droplets, which act as micro-reflectors for fluorescent light. Around each fluorescent particle, a liquid meniscus is formed that increases the excitation efficiency and redirects part of the laterally-emitted fluorescent light towards the detector due to internal reflections at the liquid-air interface of the meniscus. The three-dimensional shape of this micro-reflector can be tuned as a function of time, vapor temperature, and substrate contact angle, providing us optimized SNR performance for fluorescent detection. Based on these self-assembled micro-reflectors, we experimentally demonstrate ~2.5-3 fold enhancement of the fluorescent signal from 2-10 µm sized particles. A theoretical explanation of the formation rate and shapes of these micro-reflectors is presented, along with a ray tracing model of their optical performance. This method can be used as a sample preparation technique for consumer electronics-based microscopy and sensing tools, thus increasing the sensitivity of low-NA systems that image fluorescent micro-objects.

Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning.

Rapid and sensitive detection of waterborne pathogens in drinkable and recreational water sources is crucial for treating and preventing the spread of water related diseases, especially in resource-limited settings. Here we present a field-portable and cost-effective platform for detection and quantification of Giardia lamblia cysts, one of the most common waterborne parasites, which has a thick cell wall that makes it resistant to most water disinfection techniques including chlorination. The platform consists of a smartphone coupled with an opto-mechanical attachment weighing ~205 g, which utilizes a hand-held fluorescence microscope design aligned with the camera unit of the smartphone to image custom-designed disposable water sample cassettes. Each sample cassette is composed of absorbent pads and mechanical filter membranes; a membrane with 8 µm pore size is used as a porous spacing layer to prevent the backflow of particles to the upper membrane, while the top membrane with 5 µm pore size is used to capture the individual Giardia cysts that are fluorescently labeled. A fluorescence image of the filter surface (field-of-view: ~0.8 cm(2)) is captured and wirelessly transmitted via the mobile-phone to our servers for rapid processing using a machine learning algorithm that is trained on statistical features of Giardia cysts to automatically detect and count the cysts captured on the membrane. The results are then transmitted back to the mobile-phone in less than 2 minutes and are displayed through a smart application running on the phone. This mobile platform, along with our custom-developed sample preparation protocol, enables analysis of large volumes of water (e.g., 10-20 mL) for automated detection and enumeration of Giardia cysts in ~1 hour, including all the steps of sample preparation and analysis. We evaluated the performance of this approach using flow-cytometer-enumerated Giardia-contaminated water samples, demonstrating an average cyst capture efficiency of ~79% on our filter membrane along with a machine learning based cyst counting sensitivity of ~84%, yielding a limit-of-detection of ~12 cysts per 10 mL. Providing rapid detection and quantification of microorganisms, this field-portable imaging and sensing platform running on a mobile-phone could be useful for water quality monitoring in field and resource-limited settings.

Biomedical imaging and sensing using flatbed scanners.

In this Review, we provide an overview of flatbed scanner based biomedical imaging and sensing techniques. The extremely large imaging field-of-view (e.g., ~600-700 cm(2)) of these devices coupled with their cost-effectiveness provide unique opportunities for digital imaging of samples that are too large for regular optical microscopes, and for collection of large amounts of statistical data in various automated imaging or sensing tasks. Here we give a short introduction to the basic features of flatbed scanners also highlighting the key parameters for designing scientific experiments using these devices, followed by a discussion of some of the significant examples, where scanner-based systems were constructed to conduct various biomedical imaging and/or sensing experiments. Along with mobile phones and other emerging consumer electronics devices, flatbed scanners and their use in advanced imaging and sensing experiments might help us transform current practices of medicine, engineering and sciences through democratization of measurement science and empowerment of citizen scientists, science educators and researchers in resource limited settings.

Special multicolor illumination and numerical tilt correction in volumetric digital holographic microscopy.

We introduce a color imaging method in our digital holographic microscope system (DHM). This DHM can create color images of freely floating, or moving objects inside a large volume by simultaneously capturing three holograms using three different illumination wavelengths. In this DHM a new light source assembly is applied, where we use single mode fibers according to the corresponding wavelengths that are tightly and randomly arranged into a small array in a single FC/PC connector. This design has significant advantages over the earlier approaches, where all the used illuminations are coupled in the same fiber. It avoids the coupling losses and provides a cost effective, compact solution for multicolor coherent illumination. We explain how to determine and correct the different fiber end positions caused tilt aberration during the hologram reconstruction process. To demonstrate the performance of the device, color hologram reconstructions are presented that can achieve at least 1 µm lateral resolution.

Giga-pixel fluorescent imaging over an ultra-large field-of-view using a flatbed scanner.

We demonstrate a new fluorescent imaging technique that can screen for fluorescent micro-objects over an ultra-wide field-of-view (FOV) of ~532 cm(2), i.e., 19 cm × 28 cm, reaching a space-bandwidth product of more than 2 billion. For achieving such a large FOV, we modified the hardware and software of a commercially available flatbed scanner, and added a custom-designed absorbing fluorescent filter, a two-dimensional array of external light sources for computer-controlled and high-angle fluorescent excitation. We also re-programmed the driver of the scanner to take full control of the scanner hardware and achieve the highest possible exposure time, gain and sensitivity for detection of fluorescent micro-objects through the gradient index self-focusing lens array that is positioned in front of the scanner sensor chip. For example, this large FOV of our imaging platform allows us to screen more than 2.2 mL of undiluted whole blood for detection of fluorescent micro-objects within <5 minutes. This high-throughput fluorescent imaging platform could be useful for rare cell research and cytometry applications by enabling rapid screening of large volumes of optically dense media. Our results constitute the first time that a flatbed scanner has been converted to a fluorescent imaging system, achieving a record large FOV.

Fluorescent imaging of single nanoparticles and viruses on a smart phone.

Optical imaging of nanoscale objects, whether it is based on scattering or fluorescence, is a challenging task due to reduced detection signal-to-noise ratio and contrast at subwavelength dimensions. Here, we report a field-portable fluorescence microscopy platform installed on a smart phone for imaging of individual nanoparticles as well as viruses using a lightweight and compact opto-mechanical attachment to the existing camera module of the cell phone. This hand-held fluorescent imaging device utilizes (i) a compact 450 nm laser diode that creates oblique excitation on the sample plane with an incidence angle of ~75°, (ii) a long-pass thin-film interference filter to reject the scattered excitation light, (iii) an external lens creating 2× optical magnification, and (iv) a translation stage for focus adjustment. We tested the imaging performance of this smart-phone-enabled microscopy platform by detecting isolated 100 nm fluorescent particles as well as individual human cytomegaloviruses that are fluorescently labeled. The size of each detected nano-object on the cell phone platform was validated using scanning electron microscopy images of the same samples. This field-portable fluorescence microscopy attachment to the cell phone, weighing only ~186 g, could be used for specific and sensitive imaging of subwavelength objects including various bacteria and viruses and, therefore, could provide a valuable platform for the practice of nanotechnology in field settings and for conducting viral load measurements and other biomedical tests even in remote and resource-limited environments.

On-chip biomedical imaging.

Lab-on-a-chip systems have been rapidly emerging to pave the way toward ultra-compact, efficient, mass producible and cost-effective biomedical research and diagnostic tools. Although such microfluidic and microelectromechanical systems have achieved high levels of integration, and are capable of performing various important tasks on the same chip, such as cell culturing, sorting and staining, they still rely on conventional microscopes for their imaging needs. Recently, several alternative on-chip optical imaging techniques have been introduced, which have the potential to substitute conventional microscopes for various lab-on-a-chip applications. Here we present a critical review of these recently emerging on-chip biomedical imaging modalities, including contact shadow imaging, lens-free holographic microscopy, fluorescent on-chip microscopy and lens-free optical tomography.

In-line hologram segmentation for volumetric samples.

We propose a fast, noniterative method to segment an in-line hologram of a volumetric sample into in-line subholograms according to its constituent objects. In contrast to the phase retrieval or twin image elimination algorithms, we do not aim or require to reconstruct the complex wave field of all the objects, which would be a more complex task, but only provide a good estimate about the contribution of the particular objects to the original hologram quickly. The introduced hologram segmentation algorithm exploits the special inner structure of the in-line holograms and applies only the estimated supports and reconstruction distances of the corresponding objects as parameters. The performance of the proposed method is demonstrated and analyzed experimentally both on synthetic and measured holograms. We discussed how the proposed algorithm can be efficiently applied for object reconstruction and phase retrieval tasks.

Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy.

We discuss unique features of lens-free computational imaging tools and report some of their emerging results for wide-field on-chip microscopy, such as the achievement of a numerical aperture (NA) of ∼0.8-0.9 across a field of view (FOV) of more than 20 mm(2) or an NA of ∼0.1 across a FOV of ∼18 cm(2), which corresponds to an image with more than 1.5 gigapixels. We also discuss the current challenges that these computational on-chip microscopes face, shedding light on their future directions and applications.

Hologram positioning servo for phase-encoded holographic data storage systems.

We propose a method using phase encryption and hologram multiplexing to encode positional information into the hologram, which can be used during readout to find the correct position of the reference beam. We also include a method to align the position of the phase code in the reference beam during readout, with which we achieved approximately 1/100 hologram size (4.4 microm) precision electronically, without the need of a precise mechanical hologram positioning device. We prove the feasibility of the method with experiments.

Hybrid multinary modulation using a phase modulating spatial light modulator and a low-pass spatial filter.

We propose a method for performing binary intensity and continuous phase modulation of beams with a spatial light modulator (SLM) and a low-pass spatial filtering 4-f system. With our method it is possible to avoid the use of phase masks in holographic data storage systems or to enhance the phase encoding of the SLM by making it capable of binary amplitude modulation. The data storage capabilities and the limitations of the method are studied.