High-throughput and label-free single nanoparticle sizing based on time-resolved on-chip microscopy

Label-Free Light Scattering Imaging with Purified Brownian Motion Differentiates Small Extracellular Vesicles in Cell Microenvironments

Small extracellular vesicles (sEVs) are heterogeneous biological nanoparticles (NPs) with wide biomedicine applications. Tracking individual nanoscale sEVs can reveal information that conventional microscopic methods may lack, especially in cellular microenvironments. This usually requires biolabeling to identify single sEVs. Here, we developed a light scattering imaging method based on dark-field technology for label-free nanoparticle diffusion analysis (NDA). Compared with nanoparticle tracking analysis (NTA), our method was shown to determine the diffusion probabilities of a single NP. It was demonstrated that accurate size determination of NPs of 41 and 120 nm in diameter is achieved by purified Brownian motion (pBM), without or within the cell microenvironments. Our pBM method was also shown to obtain a consistent size estimation of the normal and cancerous plasma-derived sEVs without and within cell microenvironments, while cancerous plasma-derived sEVs are statistically smaller than normal ones. Moreover, we showed that the velocity and diffusion coefficient are key parameters for determining the diffusion types of the NPs and sEVs in a cancerous cell microenvironment. Our light scattering-based NDA and pBM methods can be used for size determination of NPs, even in cell microenvironments, and also provide a tool that may be used to analyze sEVs for many biomedical applications.

Dual-wavelength metalens enables Epi-fluorescence detection from single molecules

Single molecule fluorescence spectroscopy is at the heart of molecular biophysics research and the most sensitive biosensing assays. The growing demand for precision medicine and environmental monitoring requires the creation of miniaturized and portable sensing platforms. However, the need for highly sophisticated objective lenses has precluded the development of single molecule detection systems for truly portable devices. Here, we propose a dielectric metalens device of submicrometer thickness to excite and collect light from fluorescent molecules instead of an objective lens. The high numerical aperture, high focusing efficiency, and dual-wavelength operation of the metalens enable the implementation of fluorescence correlation spectroscopy with a single Alexa 647 molecule in the focal volume. Moreover, the metalens enables real-time monitoring of individual fluorescent nanoparticle transitions and identification of hydrodynamic diameters ranging from a few to hundreds of nanometers. This advancement in sensitivity extends the application of the metalens technology to ultracompact single-molecule sensors.

Particle transfer mediates dermal exposure of consumers to plasticizers in eraser and pen accessories

Dermal exposure to chemicals released from daily consumer products is a rising concern, particularly for children who are susceptible to unintentional hand-to-mouth transfer and related chemical exposure risk. However, chemical transfer induced by tiny particles of intact products has yet to be adequately addressed. The objective of the present study was to determine the potentiality of particles release from intact erasers and pen grips upon dermal contact by measuring the migration rates of the embedded plasticizers (phthalates and its alternatives). The results showed that billions of particles were released from erasers (0.6-1.2 × 109) and pen grips (0.2-1.6 × 108) upon dermal contact at ambient temperature, with sizes mainly smaller than 1 μm. The composition of eraser leachates was identical to that of the corresponding bulk eraser, as confirmed by Fourier-transform infrared spectroscopy and pyrolysis. Migrated hydrophobic plasticizers may be used as indicators of particle release from erasers and pen grips. The potentiality of particle release was negatively correlated with the total plasticizer contents (r = -0.51; p < 0.05) for both erasers and pen grips. These findings indicated that particles directly released from school supplies and accessories could be a non-negligible source of human exposure to plasticizers.

Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses

Current commercial air-quality monitoring devices lack a large dynamic range, especially at the small, ultrafine size scale. Furthermore, there is a low density of air-quality monitoring stations, reducing the precision with which local particulate matter hazards can be tracked. Here, we show a low-cost, lensfree, and portable air-quality monitoring device (LPAQD) that can detect and measure micron-sized particles down to 100 nm-sized particles, with the capability to track and measure particles in real time throughout a day and the ability to accurately measure particulate matter densities as low as 3 μg m-3. A vapor-condensed film is deposited onto the coverslip used to collect particles before the LPAQD is deployed at outdoor monitoring sites. The vapor-condensed film increases the scattering cross section of particles smaller than the pixel size, enabling the sub-pixel and sub-diffraction-limit-sized particles to be detected. The high dynamic range, low cost, and portability of this device can enable citizens to monitor their own air quality to hopefully impact user decisions that reduce the risk for particulate matter-related diseases.

Evolving utility of exosomes in pancreatic cancer management

Despite the development of newer oncological treatment, the survival of patients with pancreatic cancer (PC) remains poor. Recent studies have identified exosomes as essential mediators of intercellular communications and play a vital role in tumor initiation, metastasis and chemoresistance. Thus, the utility of liquid biopsies using exosomes in PC management can be used for early detection, diagnosis, monitoring as well as drug delivery vehicles for cancer therapy. This review summarizes the function, and clinical applications of exosomes in cancers as minimally invasive liquid biomarker in diagnostic, prognostic and therapeutic roles.

Simultaneous Sizing and Refractive Index Analysis of Heterogeneous Nanoparticle Suspensions

Rapid and reliable characterization of heterogeneous nanoparticle suspensions is a key technology across the nanosciences. Although approaches exist for homogeneous samples, they are often unsuitable for polydisperse suspensions, as particles of different sizes and compositions can lead to indistinguishable signals at the detector. Here, we introduce holographic nanoparticle tracking analysis, holoNTA, as a straightforward methodology that decouples size and material refractive index contributions. HoloNTA is applicable to any heterogeneous nanoparticle sample and has the sensitivity to measure the intrinsic heterogeneity of the sample. Specifically, we combined high dynamic range k-space imaging with holographic 3D single-particle tracking. This strategy enables long-term tracking by extending the imaging volume and delivers precise and accurate estimates of both scattering amplitude and diffusion coefficient of individual nanoparticles, from which particle refractive index and hydrodynamic size are determined. We specifically demonstrate, by simulations and experiments, that irrespective of localization uncertainty and size, the sizing sensitivity is improved as our extended detection volume yields considerably longer particle trajectories than previously reported by comparable technologies. As validation, we measured both homogeneous and heterogeneous suspensions of nanoparticles in the 40-250 nm size range and further monitored protein corona formation, where we identified subtle differences between the nanoparticle-protein complexes derived from avidin, bovine serum albumin, and streptavidin. We foresee that our approach will find many applications of both fundamental and applied nature where routine quantification and sizing of nanoparticles are required.

Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 103 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic.

A visual portable microfluidic experimental device with multiple electric field regulation functions

High portability and miniaturization are two of the most important objectives pursued by microfluidic methods. However, there remain many challenges for the design of portable and visual microfluidic devices (e.g., electrokinetic experiments) due to the use of a microscope and power supply. To this end, we report a visual portable microfluidic experimental device (PMED) with multiple electric field regulation functions, which can realize the electric field regulation functions of various basic microfluidic experiments through modular design. The internal reaction process of the microfluidic chip is displayed by a smartphone, and the experimental results are analyzed using a mobile phone application (APP). Taking the induced-charge electroosmosis (ICEO) particle focusing phenomenon as an example, we carried out detailed experiments on PMED and obtained conclusions consistent with numerical simulations. In addition to ICEO experiments, other functions such as alternating electroosmosis (ACEO), thermal buoyancy convection, and dielectrophoresis (DEP) can be realized by replacing module-specific covers. The device expands the application of microfluidic experiments and provides a certain reference for the further integration and portability of subsequent microfluidic experiment devices.

Automated detection of apoptotic versus nonapoptotic cell death using label-free computational microscopy

Identification of cell death mechanisms, particularly distinguishing between apoptotic versus nonapoptotic pathways, is of paramount importance for a wide range of applications related to cell signaling, interaction with pathogens, therapeutic processes, drug discovery, drug resistance, and even pathogenesis of diseases like cancers and neurogenerative disease among others. Here, we present a novel high-throughput method of identifying apoptotic versus necrotic versus other nonapoptotic cell death processes, based on lensless digital holography. This method relies on identification of the temporal changes in the morphological features of mammalian cells, which are unique to each cell death processes. Different cell death processes were induced by known cytotoxic agents. A deep learning-based approach was used to automatically classify the cell death mechanism (apoptotic vs necrotic vs nonapoptotic) with more than 93% accuracy. This label free approach can provide a low cost (<$250) alternative to some of the currently available high content imaging-based screening tools.

Advancing the science of dynamic airborne nanosized particles using Nano-DIHM

In situ and real-time characterization of aerosols is vital to several fundamental and applied research domains including atmospheric chemistry, air quality monitoring, or climate change studies. To date, digital holographic microscopy is commonly used to characterize dynamic nanosized particles, but optical traps are required. In this study, a novel integrated digital in-line holographic microscope coupled with a flow tube (Nano-DIHM) is demonstrated to characterize particle phase, shape, morphology, 4D dynamic trajectories, and 3D dimensions of airborne particles ranging from the nanoscale to the microscale. We demonstrate the application of Nano-DIHM for nanosized particles (≤200 nm) in dynamic systems without optical traps. The Nano-DIHM allows observation of moving particles in 3D space and simultaneous measurement of each particle's three dimensions. As a proof of concept, we report the real-time observation of 100 nm and 200 nm particles, i.e. polystyrene latex spheres and the mixture of metal oxide nanoparticles, in air and aqueous/solid/heterogeneous phases in stationary and dynamic modes. Our observations are validated by high-resolution scanning/transmission electron microscopy and aerosol sizers. The complete automation of software (Octopus/Stingray) with Nano-DIHM permits the reconstruction of thousands of holograms within an hour with 62.5 millisecond time resolution for each hologram, allowing to explore the complex physical and chemical processes of aerosols.

Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning

Particle agglutination assays are widely adopted immunological tests that are based on antigen-antibody interactions. Antibody-coated microscopic particles are mixed with a test sample that potentially contains the target antigen, as a result of which the particles form clusters, with a size that is a function of the antigen concentration and the reaction time. Here, we present a quantitative particle agglutination assay that combines mobile lens-free microscopy and deep learning for rapidly measuring the concentration of a target analyte; as its proof-of-concept, we demonstrate high-sensitivity C-reactive protein (hs-CRP) testing using human serum samples. A dual-channel capillary lateral flow device is designed to host the agglutination reaction using 4 μL of serum sample with a material cost of 1.79 cents per test. A mobile lens-free microscope records time-lapsed inline holograms of the lateral flow device, monitoring the agglutination process over 3 min. These captured holograms are processed, and at each frame the number and area of the particle clusters are automatically extracted and fed into shallow neural networks to predict the CRP concentration. 189 measurements using 88 unique patient serum samples were utilized to train, validate and blindly test our platform, which matched the corresponding ground truth concentrations in the hs-CRP range (0-10 μg mL^-1) with an R² value of 0.912. This computational sensing platform was also able to successfully differentiate very high CRP concentrations (e.g., >10-500 μg mL^-1) from the hs-CRP range. This mobile, cost-effective and quantitative particle agglutination assay can be useful for various point-of-care sensing needs and global health related applications.

Adaptive Parameter Model for Quasi-Spherical Cell Size Measurement Based on Lensless Imaging System

Many biological cells appear quasi-spherical, such as red blood cells, white blood cells, egg cells, cancer cells, etc. Cell size is an important basis for medical diagnosis. The traditional method is to use a microscope or flow cytometer to obtain the cell size. Either it depends on professionals and cannot be automated, or it is expensive and bulky, which are not suitable for point-of-care test. Lab-on-a-chip technology using a lensless imaging system gives a better solution for obtaining the cell size. In order to deal with the diffraction in the lensless imaging system, the distance between the light source and the cell, the distance between the cell and the CMOS image sensor and optical wavelength need to be accurately measured or controlled, which will greatly increase the complexity of the system, making it difficult to truly apply to point-of-care test. In this paper, an adaptive parameter model for quasi-spherical cell size measurement based on lensless imaging system is given. First, the diffraction theory used in the model is explained. Then, the adaptive algorithm of the system parameter is given. To illustrate the practicality of the algorithm, a quasi-spherical cell size measurement method and a super-resolution algorithm are given. Finally, the experiment proves that the adaptive parameter model is effective can meet the needs of quasi-spherical cell size measurement.

Accurate and fast modeling of scattering from random arrays of nanoparticles using the discrete dipole approximation and angular spectrum method

Lens-free microscopes can utilize holographic reconstruction techniques to recover the image of an object from the digitally recorded superposition of an unperturbed plane wave and a wave scattered by the object. Image reconstruction most commonly relies on the scalar angular spectrum method (ASM). While fast, the scalar ASM can be inaccurate for nanoscale objects, either because of the scalar approximation, or more generally, because it only models field propagation and not light-matter interaction, including inter-particle coupling. Here we evaluate the accuracy of the scalar ASM when combined with three different light-matter interaction models for computing the far-field light scattered by random arrays of gold and polystyrene nanoparticles. Among the three models-a dipole-matched transmission model, an optical path length model, and a binary amplitude model-we find that which model is most accurate depends on the nanoparticle material and packing density. For polystyrene particles at any packing density, there is always at least one model with error below 20%, while for gold nanoparticles with 40% or 50% surface coverage, there are no models that can provide errors better than 30%. The ASM error is determined in comparison to a discrete dipole approximation model, which is more computationally efficient than other full-wave modeling techniques. The knowledge of when and how the ASM fails can serve as a first step toward improved resolution in lens-free reconstruction and can also be applied to other random nanoparticle array applications such as lens-based super-resolution imaging, sub-diffraction beam focusing, and biomolecular sensing.

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro- and nanoplastic particles in water and tissue samples.

Red blood cell recognition and posture estimation in microfluidic chip based on lensless imaging

On the one hand, lensless imaging technology has become one of the key technologies to achieve point-of-care testing; on the other hand, microfluidic technology has shown great application potential in the field of biological detection. Using mainstream lensless imaging technology to achieve biological cell imaging in microfluidic chips has technical limitations. In particular, it is more difficult to achieve lensless imaging for non-spherical cells in microfluidic chips such as red blood cells. Achieving red blood cell recognition and posture estimation in a microfluidic chip under the lensless imaging, combined with mainstream lensless imaging technology, can provide more effective red blood cell morphological parameters for medical diagnosis. In this paper, the method for red blood cell recognition and posture estimation in microfluidic chips based on lensless imaging is given. First, the relevant theoretical basis is introduced. Then, the models of red blood cell recognition and posture estimation in microfluidic chips based on lensless imaging are given. The effect of red blood cell flipping on lensless imaging is analyzed in the modeling process. Finally, the effectiveness of the proposed method is verified by experiments. Experiments show that the proposed method can well achieve red blood cell recognition and posture estimation through the shape characteristics of red blood cells.

Emerging technologies and commercial products in exosome-based cancer diagnosis and prognosis

Academic and industrial groups worldwide have reported technological advances in exosome-based cancer diagnosis and prognosis. However, the potential translation of these emerging technologies for research and clinical settings remains unknown. This work overviews the role of exosomes in cancer diagnosis and prognosis, followed by a survey on emerging exosome technologies, particularly microfluidic advances for the isolation and detection of exosomes in cancer research. The advantages and drawbacks of each of the technologies used for the isolation, detection and engineering of exosomes are evaluated to address their clinical challenges for cancer diagnosis and prognosis. Furthermore, commercial platforms for exosomal detection and analysis are introduced, and their performance and impact on cancer diagnosis and prognosis are assessed. Also, the risks associated with the further development of the next generation of exosome devices are discussed. The outcome of this work could facilitate recognizing deliverable Exo-devices and technologies with unprecedented functionality and predictable manufacturability for the next-generation of cancer diagnosis and prognosis.

High-Speed Lens-Free Holographic Sensing of Protein Molecules Using Quantitative Agglutination Assays

Accurate, cost-effective, easy-to-use, and point-of-care sensors for protein biomarker levels are important for disease diagnostics. A cost-effective and compact readout approach that has been used for several diagnostic applications is lens-free holographic microscopy, which provides an ultralarge field of view and submicron resolution when it is coupled with pixel super-resolution techniques. Despite its potential as a diagnostic technique, lens-free microscopy has not previously been applied to quantitative protein molecule sensing in solution, which can simplify sensing protocols and ultimately enable measurements of binding kinetics in physiological conditions. Here, we sense interferon-γ (an immune system biomarker) and NeutrAvidin molecules in solution by combining lens-free microscopy with a one-step bead-based agglutination assay, enabled by a custom high-speed light-emitting diode (LED) array and automated image processing routines. We call this a quantitative large-area binding (QLAB) sensor. The high-speed light source provides, for the first time, pixel super-resolved imaging of >10⁴ 2 μm beads in solution undergoing Brownian motion, without significant motion blur. The automated image processing routines enable the counting of individual beads and clusters, providing a quantitative sensor readout that depends on both bead and analyte concentrations. Fits to the chemical binding theory are provided. For NeutrAvidin, we find a limit of detection (LOD) of <27 ng/mL (450 pM) and a dynamic range of 2-4 orders of magnitude. For mouse interferon-γ, the LOD is <3 ng/mL (200 pM) and the dynamic range is at least 4 orders of magnitude. The QLAB sensor holds promise for point-of-care applications in low-resource communities and where protocol simplicity is important.

Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis

Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis.

Detection of Bacterial and Viral Pathogens Using Photonic Point-of-Care Devices

Infectious diseases caused by bacteria and viruses are highly contagious and can easily be transmitted via air, water, body fluids, etc. Throughout human civilization, there have been several pandemic outbreaks, such as the Plague, Spanish Flu, Swine-Flu, and, recently, COVID-19, amongst many others. Early diagnosis not only increases the chance of quick recovery but also helps prevent the spread of infections. Conventional diagnostic techniques can provide reliable results but have several drawbacks, including costly devices, lengthy wait time, and requirement of trained professionals to operate the devices, making them inaccessible in low-resource settings. Thus, a significant effort has been directed towards point-of-care (POC) devices that enable rapid diagnosis of bacterial and viral infections. A majority of the POC devices are based on plasmonics and/or microfluidics-based platforms integrated with mobile readers and imaging systems. These techniques have been shown to provide rapid, sensitive detection of pathogens. The advantages of POC devices include low-cost, rapid results, and portability, which enables on-site testing anywhere across the globe. Here we aim to review the recent advances in novel POC technologies in detecting bacteria and viruses that led to a breakthrough in the modern healthcare industry.

Mycobacterium tuberculosis extracellular vesicles: exploitation for vaccine technology and diagnostic methods

Tuberculosis (TB) is a fatal epidemic disease usually caused by Mycobacterium tuberculosis (Mtb). Pervasive latent infection, multidrug- and extensively drug-resistant tuberculosis (MDR- and XDR-TB), and TB/HIV co-infection make TB a global health problem, which emphasises the design and development of efficient vaccines and diagnostic biomarkers. Extracellular vesicles (EVs) secretion is a conserved phenomenon in all the domains of life. Various cargos such as nucleic acids, toxins, lipoproteins, and enzymes have been recognised in these nano-sized vesicles that may be involved in bacterial physiology and pathogenesis. The intrinsic adjuvant effect, native immunogenic cargo, sensing by host immune cells, circulation in all body fluids, and comprehensive distribution of antigens introduce EVs as a promising tool for designing novel vaccines, diagnostic biomarkers, and drug delivery systems. Genetic engineering of the EV-producing bacteria and the subsequent production of proper EVs could facilitate the development of the EV-based therapeutic applications. Recently, it was demonstrated that thick-walled mycobacteria release EVs, which contain immunodominant cargos such as lipoglycans and lipoproteins. The present article is a comprehensive review on the recent findings of Mtb EVs biology and the exploitation of EVs for the vaccine technology and diagnostic methods.

Rapid quantification of prion proteins using resistive pulse sensing

Prion diseases are a group of fatal transmissible neurological conditions caused by the change in conformation of intrinsic cellular prion protein (PrP^C). We present a rapid assay using aptamers and resistive pulse sensing, RPS, to extract and quantify PrP^C from complex sample matrices. We functionalise the surface of superparamagnetic beads, SPBs, with a DNA aptamer. First SPB's termed P-beads, are used to pre-concentrate the analyte from a large sample volume. The PrP^C protein is then eluted from the P-beads before aptamer modified sensing beads, S-beads, are added. The velocity of the S-beads through the nanopore reveals the concentration of the PrP^C protein. The process is done in under an hour and allows the detection of picomol's of protein.

Computational Image Analysis of Guided Acoustic Waves Enables Rheological Assessment of Sub-nanoliter Volumes

We present a method for the computational image analysis of high frequency guided sound waves based upon the measurement of optical interference fringes, produced at the air interface of a thin film of liquid. These acoustic actuations induce an affine deformation of the liquid, creating a lensing effect that can be readily observed using a simple imaging system. We exploit this effect to measure and analyze the spatiotemporal behavior of the thin liquid film as the acoustic wave interacts with it. We also show that, by investigating the dynamics of the relaxation processes of these deformations when actuation ceases, we are able to determine the liquid's viscosity using just a lens-free imaging system and a simple disposable biochip. Contrary to all other acoustic-based techniques in rheology, our measurements do not require monitoring of the wave parameters to obtain quantitative values for fluid viscosities, for sample volumes as low as 200 pL. We envisage that the proposed methods could enable high throughput, chip-based, reagent-free rheological studies within very small samples.

Nanoplasmonic Approaches for Sensitive Detection and Molecular Characterization of Extracellular Vesicles

All cells release a multitude of nanoscale extracellular vesicles (nEVs) into circulation, offering immense potential for new diagnostic strategies. Yet, clinical translation for nEVs remains a challenge due to their vast heterogeneity, our insufficient ability to isolate subpopulations, and the low frequency of disease-associated nEVs in biofluids. The growing field of nanoplasmonics is poised to address many of these challenges. Innovative materials engineering approaches based on exploiting nanoplasmonic phenomena, i.e., the unique interaction of light with nanoscale metallic materials, can achieve unrivaled sensitivity, offering real-time analysis and new modes of medical and biological imaging. We begin with an introduction into the basic structure and function of nEVs before critically reviewing recent studies utilizing nanoplasmonic platforms to detect and characterize nEVs. For the major techniques considered, surface plasmon resonance (SPR), localized SPR, and surface enhanced Raman spectroscopy (SERS), we introduce and summarize the background theory before reviewing the studies applied to nEVs. Along the way, we consider notable aspects, limitations, and considerations needed to apply plasmonic technologies to nEV detection and analysis.

Smartphone-based fluorescent lateral flow immunoassay platform for highly sensitive point-of-care detection of Zika virus nonstructural protein 1

Simple, inexpensive, and rapid diagnostic tests in low-resource settings with limited laboratory equipment and technical expertise are instrumental in reducing morbidity and mortality from epidemic infectious diseases. We developed a smartphone-based fluorescent lateral flow immunoassay (LFIA) platform for the highly sensitive point-of-care detection of Zika virus nonstructural protein 1 (ZIKV NS1). An attachment was designed and 3D-printed to integrate the smartphone with external optical and electrical components, enabling the miniaturization of the instrument and reduction in cost and complexity. Quantum dot microspheres were utilized as probes in fluorescent LFIA because of their extremely bright fluorescence signal. This approach can achieve quantitative point-of-care detection of ZIKV NS1 within 20 min. Limits of detection (LODs) in buffer and serum were 0.045 and 0.15 ng mL^-1, respectively. Despite the high structural similarity, a high-level Dengue virus NS1 as interferent showed limited cross-reactivity. Furthermore, this assay was successfully applied to detecte ZIKV NS1 and virions spiked in complex biological samples, indicating its practical application capability. Given its low cost, compact size, and excellent analytical performance, the proposed smartphone-based fluorescent LFIA platform holds considerable potential in rapid and accurate point-of-care detection of ZIKV NS1 and provides new insight into the design and application of molecular diagnostic methods in low-resource settings.

Extraordinary Focusing Effect of Surface Nanolenses in Total Internal Reflection Mode

Microscopic lenses are paramount in solar energy harvesting, optical devices, and imaging technologies. This work reports an extraordinary focusing effect exhibited by a surface nanolens (i.e., with at least one dimension of subwavelength) that is situated in an evanescent field from the total internal reflection (TIR) of light illuminated to the supporting substrate above the critical angle. Our measurements show that the position, shape, and size of the surface area with enhanced light intensity are determined by the geometry of the nanolens and the incident angle, in good agreement with simulation results. This strong focusing effect of the surface nanolens is shown to significantly promote the plasmonic effect of deposited gold nanoparticles on the lens surface inlight conversion and to vaporize surrounding water to microbubbles by using low laser power. This work further demonstrates that the light redistribution by the surface nanolens in TIR enables a range of novel applications in selectively local visualization of specimens in fluorescence imaging, optical trapping of colloids from an external flow, and selective materials deposition from photoreactions.

Optimized sensing of sparse and small targets using lens-free holographic microscopy

Lens-free holographic microscopy offers sub-micron resolution over an ultra-large field-of-view >20 mm², making it suitable for bio-sensing applications that require the detection of small targets at low concentrations. Various pixel super-resolution techniques have been shown to enhance resolution and boost signal-to-noise ratio (SNR) by combining multiple partially-redundant low-resolution frames. However, it has been unclear which technique performs best for small-target sensing. Here, we quantitatively compare SNR and resolution in experiments using no regularization, cardinal-neighbor regularization, and a novel implementation of sparsity-promoting regularization that uses analytically-calculated gradients from Bayer-pattern image sensors. We find that sparsity-promoting regularization enhances the SNR by ~8 dB compared to the other methods when imaging micron-scale beads with surface coverages up to ~4%.

Mobile Technologies for the Discovery, Analysis, and Engineering of the Global Microbiome

The microbiome has been heralded as a gauge of and contributor to both human health and environmental conditions. Current challenges in probing, engineering, and harnessing the microbiome stem from its microscopic and nanoscopic nature, diversity and complexity of interactions among its members and hosts, as well as the spatiotemporal sampling and in situ measurement limitations induced by the restricted capabilities and norm of existing technologies, leaving some of the constituents of the microbiome unknown. To facilitate significant progress in the microbiome field, deeper understanding of the constituents' individual behavior, interactions with others, and biodiversity are needed. Also crucial is the generation of multimodal data from a variety of subjects and environments over time. Mobile imaging and sensing technologies, particularly through smartphone-based platforms, can potentially meet some of these needs in field-portable, cost-effective, and massively scalable manners by circumventing the need for bulky, expensive instrumentation. In this Perspective, we outline how mobile sensing and imaging technologies could lead the way to unprecedented insight into the microbiome, potentially shedding light on various microbiome-related mysteries of today, including the composition and function of human, animal, plant, and environmental microbiomes. Finally, we conclude with a look at the future, propose a computational microbiome engineering and optimization framework, and discuss its potential impact and applications.

Micro-optics for microfluidic analytical applications

This critical review summarizes the developments in the integration of micro-optical elements with microfluidic platforms for facilitating detection and automation of bio-analytical applications.

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.

Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring

Optical compound microscope has been a major tool in biomedical imaging for centuries. Its performance relies on relatively complicated, bulky and expensive lenses and alignment mechanics. In contrast, the lensless microscope digitally reconstructs microscopic images of specimens without using any lenses, as a result of which it can be made much smaller, lighter and lower-cost. Furthermore, the limited space-bandwidth product of objective lenses in a conventional microscope can be significantly surpassed by a lensless microscope. Such lensless imaging designs have enabled high-resolution and high-throughput imaging of specimens using compact, portable and cost-effective devices to potentially address various point-of-care, global-health and telemedicine related challenges. In this review, we discuss the operation principles and the methods behind lensless digital holographic on-chip microscopy. We also go over various applications that are enabled by cost-effective and compact implementations of lensless microscopy, including some recent work on air quality monitoring, which utilized machine learning for high-throughput and accurate quantification of particulate matter in air. Finally, we conclude with a brief future outlook of this computational imaging technology.

Computational sensing of herpes simplex virus using a cost-effective on-chip microscope

Caused by the herpes simplex virus (HSV), herpes is a viral infection that is one of the most widespread diseases worldwide. Here we present a computational sensing technique for specific detection of HSV using both viral immuno-specificity and the physical size range of the viruses. This label-free approach involves a compact and cost-effective holographic on-chip microscope and a surface-functionalized glass substrate prepared to specifically capture the target viruses. To enhance the optical signatures of individual viruses and increase their signal-to-noise ratio, self-assembled polyethylene glycol based nanolenses are rapidly formed around each virus particle captured on the substrate using a portable interface. Holographic shadows of specifically captured viruses that are surrounded by these self-assembled nanolenses are then reconstructed, and the phase image is used for automated quantification of the size of each particle within our large field-of-view, ~30 mm². The combination of viral immuno-specificity due to surface functionalization and the physical size measurements enabled by holographic imaging is used to sensitively detect and enumerate HSV particles using our compact and cost-effective platform. This computational sensing technique can find numerous uses in global health related applications in resource-limited environments.

Quantitative interferometric reflectance imaging for the detection and measurement of biological nanoparticles

The sensitive detection and quantitative measurement of biological nanoparticles such as viruses or exosomes is of growing importance in biology and medicine since these structures are implicated in many biological processes and diseases. Interferometric reflectance imaging is a label-free optical biosensing method which can directly detect individual biological nanoparticles when they are immobilized onto a protein microarray. Previous efforts to infer bio-nanoparticle size and shape have relied on empirical calibration using a 'ruler' of particle samples of known size, which was inconsistent and qualitative. Here, we present a mechanistic physical explanation and experimental approach by which interferometric reflectance imaging may be used to not only detect but also quantitatively measure bio-nanoparticle size and shape. We introduce a comprehensive optical model that can quantitatively simulate the scattering of arbitrarily-shaped nanoparticles such as rod-shaped or filamentous virions. Finally, we optimize the optical design for the detection and quantitative measurement of small and low-index bio-nanoparticles immersed in water.

Optical visualization and quantification of enzyme activity using dynamic droplet lenses

In this paper, we describe an approach to measuring enzyme activity based on the reconfiguration of complex emulsions. Changes in the morphology of these complex emulsions, driven by enzyme-responsive surfactants, modulate the transmission of light through a sample. Through this method we demonstrate how simple photodetector measurements may be used to monitor enzyme kinetics. This approach is validated by quantitative measurements of enzyme activity for three different classes of enzymes (amylase, lipase, and sulfatase), relying on two distinct mechanisms for coupling droplet morphology to enzyme activity (host-guest interactions with uncaging and molecular cleavage).

Expansion Mini-Microscopy: An Enabling Alternative in Point-of-Care Diagnostics

Diagnostics play a significant role in health care. In the developing world and low-resource regions the utility for point-of-care (POC) diagnostics becomes even greater. This need has long been recognized, and diagnostic technology has seen tremendous progress with the development of portable instrumentation such as miniature imagers featuring low complexity and cost. However, such inexpensive devices have not been able to achieve a resolution sufficient for POC detection of pathogens at very small scales, such as single-cell parasites, bacteria, fungi, and viruses. To this end, expansion microscopy (ExM) is a recently developed technique that, by physically expanding preserved biological specimens through a chemical process, enables super-resolution imaging on conventional microscopes and improves imaging resolution of a given microscope without the need to modify the existing microscope hardware. Here we review recent advances in ExM and portable imagers, respectively, and discuss the rational combination of the two technologies, that we term expansion mini-microscopy (ExMM). In ExMM, the physical expansion of a biological sample followed by imaging on a mini-microscope achieves a resolution as high as that attainable by conventional high-end microscopes imaging non-expanded samples, at significant reduction in cost. We believe that this newly developed ExMM technique is likely to find widespread applications in POC diagnostics in resource-limited and remote regions by expanded-scale imaging of biological specimens that are otherwise not resolvable using low-cost imagers.

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.

Subsampled phase retrieval for temporal resolution enhancement in lensless on-chip holographic video

On-chip holographic video is a convenient way to monitor biological samples simultaneously at high spatial resolution and over a wide field-of-view. However, due to the limited readout rate of digital detector arrays, one often faces a tradeoff between the per-frame pixel count and frame rate of the captured video. In this report, we propose a subsampled phase retrieval (SPR) algorithm to overcome the spatial-temporal trade-off in holographic video. Compared to traditional phase retrieval approaches, our SPR algorithm uses over an order of magnitude less pixel measurements while maintaining suitable reconstruction quality. We use an on-chip holographic video setup with pixel sub-sampling to experimentally demonstrate a factor of 5.5 increase in sensor frame rate while monitoring the in vivo movement of Peranema microorganisms.

Laser Light-field Fusion for Wide-field Lensfree On-chip Phase Contrast Microscopy of Nanoparticles

Wide-field lensfree on-chip microscopy, which leverages holography principles to capture interferometric light-field encodings without lenses, is an emerging imaging modality with widespread interest given the large field-of-view compared to lens-based techniques. In this study, we introduce the idea of laser light-field fusion for lensfree on-chip phase contrast microscopy for detecting nanoparticles, where interferometric laser light-field encodings acquired using a lensfree, on-chip setup with laser pulsations at different wavelengths are fused to produce marker-free phase contrast images of particles at the nanometer scale. As a proof of concept, we demonstrate, for the first time, a wide-field lensfree on-chip instrument successfully detecting 300 nm particles across a large field-of-view of ~30 mm² without any specialized or intricate sample preparation, or the use of synthetic aperture- or shift-based techniques.

Mobile health: the power of wearables, sensors, and apps to transform clinical trials

Mobile technology has become a ubiquitous part of everyday life, and the practical utility of mobile devices for improving human health is only now being realized. Wireless medical sensors, or mobile biosensors, are one such technology that is allowing the accumulation of real-time biometric data that may hold valuable clues for treating even some of the most devastating human diseases. From wearable gadgets to sophisticated implantable medical devices, the information retrieved from mobile technology has the potential to revolutionize how clinical research is conducted and how disease therapies are delivered in the coming years. Encompassing the fields of science and engineering, analytics, health care, business, and government, this report explores the promise that wearable biosensors, along with integrated mobile apps, hold for improving the quality of patient care and clinical outcomes. The discussion focuses on groundbreaking device innovation, data optimization and validation, commercial platform integration, clinical implementation and regulation, and the broad societal implications of using mobile health technologies.

Unconventional methods of imaging: computational microscopy and compact implementations

In the past two decades or so, there has been a renaissance of optical microscopy research and development. Much work has been done in an effort to improve the resolution and sensitivity of microscopes, while at the same time to introduce new imaging modalities, and make existing imaging systems more efficient and more accessible. In this review, we look at two particular aspects of this renaissance: computational imaging techniques and compact imaging platforms. In many cases, these aspects go hand-in-hand because the use of computational techniques can simplify the demands placed on optical hardware in obtaining a desired imaging performance. In the first main section, we cover lens-based computational imaging, in particular, light-field microscopy, structured illumination, synthetic aperture, Fourier ptychography, and compressive imaging. In the second main section, we review lensfree holographic on-chip imaging, including how images are reconstructed, phase recovery techniques, and integration with smart substrates for more advanced imaging tasks. In the third main section we describe how these and other microscopy modalities have been implemented in compact and field-portable devices, often based around smartphones. Finally, we conclude with some comments about opportunities and demand for better results, and where we believe the field is heading.

Wide-field imaging of birefringent synovial fluid crystals using lens-free polarized microscopy for gout diagnosis

Gout is a form of crystal arthropathy where monosodium urate (MSU) crystals deposit and elicit inflammation in a joint. Diagnosis of gout relies on identification of MSU crystals under a compensated polarized light microscope (CPLM) in synovial fluid aspirated from the patient's joint. The detection of MSU crystals by optical microscopy is enhanced by their birefringent properties. However, CPLM partially suffers from the high-cost and bulkiness of conventional lens-based microscopy, and its relatively small field-of-view (FOV) limits the efficiency and accuracy of gout diagnosis. Here we present a lens-free polarized microscope which adopts a novel differential and angle-mismatched polarizing optical design achieving wide-field and high-resolution holographic imaging of birefringent objects with a color contrast similar to that of a standard CPLM. The performance of this computational polarization microscope is validated by imaging MSU crystals made from a gout patient's tophus and steroid crystals used as negative control. This lens-free polarized microscope, with its wide FOV (>20 mm(2)), cost-effectiveness and field-portability, can significantly improve the efficiency and accuracy of gout diagnosis, reduce costs, and can be deployed even at the point-of-care and in resource-limited clinical settings.

Demosaiced pixel super-resolution for multiplexed holographic color imaging

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.

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.

Lensless Imaging and Sensing

High-resolution optical microscopy has traditionally relied on high-magnification and high-numerical aperture objective lenses. In contrast, lensless microscopy can provide high-resolution images without the use of any focusing lenses, offering the advantages of a large field of view, high resolution, cost-effectiveness, portability, and depth-resolved three-dimensional (3D) imaging. Here we review various approaches to lensless imaging, as well as its applications in biosensing, diagnostics, and cytometry. These approaches include shadow imaging, fluorescence, holography, superresolution 3D imaging, iterative phase recovery, and color imaging. These approaches share a reliance on computational techniques, which are typically necessary to reconstruct meaningful images from the raw data captured by digital image sensors. When these approaches are combined with physical innovations in sample preparation and fabrication, lensless imaging can be used to image and sense cells, viruses, nanoparticles, and biomolecules. We conclude by discussing several ways in which lensless imaging and sensing might develop in the near future.

Detection and isolation of circulating exosomes and microvesicles for cancer monitoring and diagnostics using micro-/nano-based devices

In the last several years, nanoscale vesicles that originate from tumor cells and which can be found circulating in the blood (i.e. exosomes and microvesicles) have been discovered to contain a wealth of proteomic and genetic information to monitor cancer progression, metastasis, and drug efficacy. However, the use of exosomes and microvesicles as biomarkers to improve patient care has been limited by their small size (30 nm-1 μm) and the extensive sample preparation required for their isolation and measurement. In this Critical Review, we explore the emerging use of micro and nano-technology to isolate and detect exosomes and microvesicles in clinical samples and the application of this technology to the monitoring and diagnosis of cancer.

Democratization of Nanoscale Imaging and Sensing Tools Using Photonics

Providing means for researchers and citizen scientists in the developing world to perform advanced measurements with nanoscale precision can help to accelerate the rate of discovery and invention as well as improve higher education and the training of the next generation of scientists and engineers worldwide. Here, we review some of the recent progress toward making optical nanoscale measurement tools more cost-effective, field-portable, and accessible to a significantly larger group of researchers and educators. We divide our review into two main sections: label-based nanoscale imaging and sensing tools, which primarily involve fluorescent approaches, and label-free nanoscale measurement tools, which include light scattering sensors, interferometric methods, photonic crystal sensors, and plasmonic sensors. For each of these areas, we have primarily focused on approaches that have either demonstrated operation outside of a traditional laboratory setting, including for example integration with mobile phones, or exhibited the potential for such operation in the near future.

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.