Democratization of Nanoscale Imaging and Sensing Tools Using Photonics

Roadmap on Label-Free Super-Resolution Imaging

Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles which need to be overcome to break the classical diffraction limit of the LFSR imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability which are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.

Wave optics of imaging with contact ball lenses

Recent progress in microspherical superlens nanoscopy raises a fundamental question about the transition from super-resolution properties of mesoscale microspheres, which can provide a subwavelength resolution [Formula: see text], to macroscale ball lenses, for which the imaging quality degrades because of aberrations. To address this question, this work develops a theory describing the imaging by contact ball lenses with diameters [Formula: see text] covering this transition range and for a broad range of refractive indices [Formula: see text]. Starting from geometrical optics we subsequently proceed to an exact numerical solution of the Maxwell equations explaining virtual and real image formation as well as magnification M and resolution near the critical index [Formula: see text] which is of interest for applications demanding the highest M such as cellphone microscopy. The wave effects manifest themselves in a strong dependence of the image plane position and magnification on [Formula: see text], for which a simple analytical formula is derived. It is demonstrated that a subwavelength resolution is achievable at [Formula: see text]. The theory explains the results of experimental contact-ball imaging. The understanding of the physical mechanisms of image formation revealed in this study creates a basis for developing applications of contact ball lenses in cellphone-based microscopy.

Phone Camera Nano-Biosensor Using Mighty Sensitive Transparent Reusable Upconversion Paper

Lycopene, a natural colorant and antioxidant with a huge growing market, is highly susceptible to photo/thermal degradation, which demands real-time sensors. Hence, here a transparent upconversion nanoparticles (UCNPs) strip having 30 mol % Yb, 0.1 mol % Tm, and β-NaYF4 UCNPs, which shows an intense emission at 475 nm, has been developed. This strip has been found to be sensitive to lycopene with a detection limit as low as 10 nM using a smartphone camera, which is due to static quenching that is confirmed by the lifetime study. In comparison to previous paper strips, here the transparent strip has minimal scattering with maximum sensitivity in spite of not using any metal quenchers. An increase in strip hydrophobicity during the fabrication process complements the strip to selectively permeate and present an extraction-free substitute analysis for chromatography. Hydrophobicity endows the strip with the capability to reuse the strip with ∼100% luminescence recovery.

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.

Prototype Smartphone-Based Device for Flow Cytometry with Immunolabeling via Supra-nanoparticle Assemblies of Quantum Dots

Methods for the detection, enumeration, and typing of cells are important in many areas of research and healthcare. In this context, flow cytometers are a widely used research and clinical tool but are also an example of a large and expensive instrument that is limited to specialized laboratories. Smartphones have been shown to have excellent potential to serve as portable and lower-cost platforms for analyses that would normally be done in a laboratory. Here, we developed a prototype smartphone-based flow cytometer (FC). This compact 3D-printed device incorporated a laser diode and a microfluidic flow cell and used the built-in camera of a smartphone to track immunofluorescently labeled cells in suspension and measure their color. This capability was enabled by high-brightness supra-nanoparticle assemblies of colloidal semiconductor quantum dots (SiO₂@QDs) as well as a support vector machine (SVM) classification algorithm. The smartphone-based FC device detected and enumerated target cells against a background of other cells, simultaneously and selectively counted two different cell types in a mixture, and used multiple colors of SiO₂@QD-antibody conjugates to screen for and identify a particular cell type. The potential limits of multicolor detection are discussed alongside ideas for further development. Our results suggest that innovations in materials and engineering should enable eventual smartphone-based FC assays for clinical applications.

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.

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.

Sensing Biomarkers with Plasmonics

The detection of biomarkers is critical for enabling early disease diagnosis, monitoring the progression, and tracking the effectiveness of therapeutic intervention. Plasmonic sensors exhibit a broad range of analytical capabilities, from the rapid generation of colorimetric readouts to single-molecule sensitivity in ultralow sample volumes, which have led to their increased exploration in bioanalysis and point-of-care applications. This perspective presents selected accounts of recent developments on the different types of plasmonic sensing platforms, the pervasive challenges, and outlook on the pathway to translation. We highlight the sensing of upcoming biomarkers, including microRNA, circulating tumor cells, exosomes, and cell-free DNA, and discuss the opportunity of utilizing plasmonic nanomaterials and tools for biomarker detection beyond biofluids, such as in tissues, organs, and disease sites. The integration of plasmonic biosensors with established and upcoming technologies of instrumentation, sample pretreatment, and data analysis will help realize their translation to clinical settings for improving healthcare and enhancing the quality of life.

Tools to reverse-engineer multicellular systems: case studies using the fruit fly

Reverse-engineering how complex multicellular systems develop and function is a grand challenge for systems bioengineers. This challenge has motivated the creation of a suite of bioengineering tools to develop increasingly quantitative descriptions of multicellular systems. Here, we survey a selection of these tools including microfluidic devices, imaging and computer vision techniques. We provide a selected overview of the emerging cross-talk between engineering methods and quantitative investigations within developmental biology. In particular, the review highlights selected recent examples from the Drosophila system, an excellent platform for understanding the interplay between genetics and biophysics. In sum, the integrative approaches that combine multiple advances in these fields are increasingly necessary to enable a deeper understanding of how to analyze both natural and synthetic multicellular systems.

Nanosensors for water quality monitoring

Nanomaterial-enabled sensors are being designed for high-efficiency, multiplex-functionality and high-flexibility sensing applications. Many existing nanosensors have the inherent capacity to achieve such goals; however, they require further development into consumer- and operator-friendly tools with the ability to detect analytes in previously inaccessible locations, as well as at a greater scale than heretofore possible. Here, I discuss how nanotechnology-enabled sensors have great, as yet unmet, promise to provide widespread and potentially low-cost monitoring of chemicals, microbes and other analytes in drinking water.

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.

Patterned Colloidal Photonic Crystals

Colloidal photonic crystals (PCs) have been well developed because they are easy to prepare, cost-effective, and versatile with regards to modification and functionalization. Patterned colloidal PCs contribute a novel approach to constructing high-performance PC devices with unique structures and specific functions. In this review, an overview of the strategies for fabricating patterned colloidal PCs, including patterned substrate-induced assembly, inkjet printing, and selective immobilization and modification, is presented. The advantages of patterned PC devices are also discussed in detail, for example, improved detection sensitivity and response speed of the sensors, control over the flow direction and wicking rate of microfluidic channels, recognition of cross-reactive molecules through an array-patterned microchip, fabrication of display devices with tunable patterns, well-arranged RGB units, and wide viewing-angles, and the ability to construct anti-counterfeiting devices with different security strategies. Finally, the perspective of future developments and challenges is presented.

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.

Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning

Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nanosensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multispectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light sources or high-resolution spectrometers. By training a feature selection model over a large set of fabricated plasmonic nanosensors, we select the optimal set of illumination light-emitting diodes needed to create a minimum-error refractive index prediction model, which statistically takes into account the varied spectral responses and fabrication-induced variability of a given sensor design. This computational sensing approach was experimentally validated using a modular mobile plasmonic reader. We tested different plasmonic sensors with hexagonal and square periodicity nanohole arrays and revealed that the optimal illumination bands differ from those that are "intuitively" selected based on the spectral features of the sensor, e.g., transmission peaks or valleys. This framework provides a universal tool for the plasmonics community to design low-cost and mobile multispectral readers, helping the translation of nanosensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics. Beyond plasmonics, other types of sensors that operate based on spectral changes can broadly benefit from this approach, including e.g., aptamer-enabled nanoparticle assays and graphene-based sensors, among others.

Application of semiconductor quantum dots in bioimaging and biosensing

In this review we present new concepts and recent progress in the application of semiconductor quantum dots (QD) as labels in two important areas of biology, bioimaging and biosensing.

High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader

Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an automated and cost-effective cellphone-based 96-well microtiter-plate (MTP) reader, capable of performing AST without the need for trained diagnosticians. Our system includes a 3D-printed smartphone attachment that holds and illuminates the MTP using a light-emitting-diode array. An inexpensive optical fiber-array enables the capture of the transmitted light of each well through the smartphone camera. A custom-designed application sends the captured image to a server to automatically determine well-turbidity, with results returned to the smartphone in ~1 minute. We tested this mobile-reader using MTPs prepared with 17 antibiotics targeting Gram-negative bacteria on clinical isolates of Klebsiella pneumoniae, containing highly-resistant antimicrobial profiles. Using 78 patient isolate test-plates, we demonstrated that our mobile-reader meets the FDA-defined AST criteria, with a well-turbidity detection accuracy of 98.21%, minimum-inhibitory-concentration accuracy of 95.12%, and a drug-susceptibility interpretation accuracy of 99.23%, with no very major errors. This mobile-reader could eliminate the need for trained diagnosticians to perform AST, reduce the cost-barrier for routine testing, and assist in spatio-temporal tracking of bacterial resistance.

3D-printed bioanalytical devices

While 3D printing technologies first appeared in the 1980s, prohibitive costs, limited materials, and the relatively small number of commercially available printers confined applications mainly to prototyping for manufacturing purposes. As technologies, printer cost, materials, and accessibility continue to improve, 3D printing has found widespread implementation in research and development in many disciplines due to ease-of-use and relatively fast design-to-object workflow. Several 3D printing techniques have been used to prepare devices such as milli- and microfluidic flow cells for analyses of cells and biomolecules as well as interfaces that enable bioanalytical measurements using cellphones. This review focuses on preparation and applications of 3D-printed bioanalytical devices.

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.

Flexible Plasmonic Sensors

Mechanical flexibility and the advent of scalable, low-cost, and high-throughput fabrication techniques have enabled numerous potential applications for plasmonic sensors. Sensitive and sophisticated biochemical measurements can now be performed through the use of flexible plasmonic sensors integrated into existing medical and industrial devices or sample collection units. More robust sensing schemes and practical techniques must be further investigated to fully realize the potentials of flexible plasmonics as a framework for designing low-cost, embedded and integrated sensors for medical, environmental, and industrial applications.

Surface-Enhanced Raman Spectroscopy Biosensing: In Vivo Diagnostics and Multimodal Imaging

This perspective presents recent developments in the application of surface-enhanced Raman spectroscopy (SERS) to biosensing, with a focus on in vivo diagnostics. We describe the concepts and methodologies developed to date and the target analytes that can be detected. We also discuss how SERS has evolved from a "point-and-shoot" stand-alone technique in an analytical chemistry laboratory to an integrated quantitative analytical tool for multimodal imaging diagnostics. Finally, we offer a guide to the future of SERS in the context of clinical diagnostics.

Conceptual design of tetraazaporphyrin- and subtetraazaporphyrin-based functional nanocarbon materials: electronic structures, topologies, optical properties, and methane storage capacities

A large variety of conceptual three- and fourfold tetraazaporphyrin- and subtetraazaporphyrin-based functional 3D nanocage and nanobarrel structures have been proposed on the basis of in silico design. The designed structures differ in their sizes, topology, porosity, and conjugation properties. The stability of nanocages of Oh symmetry and nanobarrels of D4h symmetry was revealed on the basis of DFT and MD calculations, whereas their optical properties were assessed using a TDDFT approach and a long-range corrected LC-wPBE exchange-correlation functional. It was shown that the electronic structures and vertical excitation energies of the functional nanocage and nanobarrel structures could be easily tuned via their size, topology, and the presence of bridging sp(3) carbon atoms. TDDFT calculations suggest significantly lower excitation energies in fully conjugated nanocages and nanobarrels compared with systems with bridging sp(3) carbon fragments. Based on DFT and TDDFT calculations, the optical properties of the new materials can rival those of known quantum dots and are superior to those of monomeric phthalocyanines and their analogues. The methane gas adsorption properties of the new nanostructures and nanotubes generated by conversion from nanobarrels were studied using an MD simulation approach. The ability to store large quantities of methane (106-216 cm(3) (STP) cm(-3)) was observed in all cases with several compounds being close to or exceeding the DOE target of 180 cm(3) (STP) cm(-3) for material-based methane storage at a pressure of 3.5 MPa and room temperature.

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.