Dextran-Functionalized Super-nanoparticle Assemblies of Quantum Dots for Enhanced Cellular Immunolabeling and Imaging

Smartphones as a platform for molecular analysis: concepts, methods, devices and future potential

Over the past 15 years, smartphones have had a transformative effect on everyday life. These devices also have the potential to transform molecular analysis over the next 15 years. The cameras of a smartphone, and its many additional onboard features, support optical detection and other aspects of engineering an analytical device. This article reviews the development of smartphones as platforms for portable chemical and biological analysis. It is equal parts conceptual overview, technical tutorial, critical summary of the state of the art, and outlook on how to advance smartphones as a tool for analysis. It further discusses the motivations for adopting smartphones as a portable platform, summarizes their enabling features and relevant optical detection methods, then highlights complementary technologies and materials such as 3D printing, microfluidics, optoelectronics, microelectronics, and nanoparticles. The broad scope of research and key advances from the past 7 years are reviewed as a prelude to a perspective on the challenges and opportunities for translating smartphone-based lab-on-a-chip devices from prototypes to authentic applications in health, food and water safety, environmental monitoring, and beyond. The convergence of smartphones with smart assays and smart apps powered by machine learning and artificial intelligence holds immense promise for realizing a future for molecular analysis that is powerful, versatile, democratized, and no longer just the stuff of science fiction.

The Future is Bright: The Emergence of Glassy Organic Dots for Biological Applications

Glassy organic dots (g-Odots) are an emerging class of luminescent nanoparticles that offer enhanced photostability, superior brightness, and modular tunability compared to other commonly employed nanoparticles. In the last several years, they have been used as bioimaging probes for single- and multi-photon cellular imaging, exhibiting low cytotoxicity even after several days. While they are emerging as promising materials for use in biological applications, g-Odots face several key challenges before their use can become widespread. In this concept, we outline the state of the literature on g-Odots and highlight a few ways in which their design and use can be improved upon.

Dextran-Encapsulated Nanoparticles and Super-Nanoparticle Assemblies: Preparation from Quantum Dots, Fluorescent Polymers, and Magnetic Nanoparticles for Application to Cellular Immunolabeling

Nanoparticles (NPs) continue to be developed as labels for bioanalysis and imaging due to their small size and, in many cases, emergent properties such as photoluminescence (PL) and superparamagnetism. Some applications stand to benefit from amplification of the advantageous properties of a NP, but this amplification is not a simple matter of scaling for size-dependent properties. One promising approach to amplification is, therefore, to assemble many copies of a NP into a larger but still nanoscale and colloidal entity. Here, we use multiple types of hydrophobic nanocrystal to show that amphiphilic dextran is a versatile material for the preparation and surface functionalization of such super-NP assemblies: CdSe/CdS/ZnS quantum dots (QDs), InP/ZnS QDs, and Si QDs; iron oxide magnetic NPs (MNPs); composites of QDs and MNPs; and composites of QDs and MNPs with fluorene-based and phenylenevinylene-based conjugated polymers. The amphiphilic dextran was also useful for the preparation of conjugated polymer NPs (CPNs) without the inclusion of inorganic nanocrystals. The prepared super-NPs and CPNs were characterized, physically and photophysically, at both the ensemble and the single-particle levels. Per colloidal entity, the super-QDs were orders of magnitude brighter than the individual QDs. This enhancement enabled assemblies of nominally more benign InP/ZnS and Si QDs to be competitive alternative materials to CdSe/CdS/ZnS QDs, which are normally much brighter when compared as individual nanocrystals. The dextran functionalization imparted low nonspecific binding and enabled the use of tetrameric antibody complexes (TACs) for simple and selective immunolabeling of cells with all of the prepared super-NP, CPN, and composite materials. Labeling with the super-QDs provided significantly enhanced PL signals, the super-MNPs enabled magnetic pull-down of cells, and both capabilities were concurrently available with composite assemblies. Overall, this study demonstrates that the preparatory method and functional benefits of amphiphilic dextran extend to a range of hydrophobic materials and combinations thereof. There is strong potential for assembling a diverse set of property-amplified designer labels that are ready-made for in vitro applications in bioanalysis and imaging.

Dual-Mode Fluorescent/Intelligent Lateral Flow Immunoassay Based on Machine Learning Algorithm for Ultrasensitive Analysis of Chloroacetamide Herbicides

Given the harmful effect of pesticide residues, it is essential to develop portable and accurate biosensors for the analysis of pesticides in agricultural products. In this paper, we demonstrated a dual-mode fluorescent/intelligent (DM-f/DM-i) lateral flow immunoassay (LFIA) for chloroacetamide herbicides, which utilized horseradish peroxidase-IgG conjugated time-resolved fluorescent nanoparticle probes as both a signal label and amplification tool. With the newly developed LFIA in the DM-f mode, the limits of detection (LODs) were 0.08 ng/mL of acetochlor, 0.29 ng/mL of metolachlor, 0.51 ng/mL of Propisochlor, and 0.13 ng/mL of their mixture. In the DM-i mode, machine learning (ML) algorithms were used for image segmentation, feature extraction, and correlation analysis to obtain multivariate fitted equations, which had high reliability in the regression model with R2 of 0.95 in the range of 2 × 102-2 × 105 pg/mL. Importantly, the practical applicability was successfully validated by determining chloroacetamide herbicides in the corn sample with good recovery rates (85.4 to 109.3%) that correlate well with the regression model. The newly developed dual-mode LFIA with reduced detection time (12 min) holds great potential for pesticide monitoring in equipment-limited environments using a portable test strip reader and laboratory conditions using ML algorithms.

Supra-Quantum Dot Assemblies to Maximize Color-Based Multiplexed Fluorescence Detection with a Smartphone Camera

Photoluminescence (PL) imaging and bioanalysis with smartphone-based devices are of growing interest for point-of-care/point-of-need diagnostics. Strategies for maximizing sensitivity have been explored in this context, but color multiplexing has been very limited, with its maximum level unexplored. Here, we evaluated color multiplexing with smartphone-based PL imaging by using supra-nanoparticle assemblies of quantum dots (supra-QDs). These materials were prepared as composite colors that were tailored to the red-green-blue (RGB) color space of smartphone cameras by coassembling different ratios of R-, G-, and B-emitting QDs on a silica nanoparticle scaffold. The supra-QDs were characterized and used to label cell-sized objects that were measured under flow with a smartphone-based device. Each color followed an approximately linear trajectory in the RGB space, and training of support vector machine models enabled color classification with overall accuracies ≥87% for 10-color multiplexing and better accuracies for fewer colors. Most misclassification occurred at low signal levels, such that establishing a nonclassifiable zone near the origin of RGB color space improved the overall 10-color classification accuracy to ≥94%. Similar improvements in accuracy with greater retention of data were possible with a probabilistic rather than a radial threshold. Simulations that were parameterized by experimental data suggested that ≥14-color multiplexing with accuracies ≥90% should be possible with an optimized supra-QD color set. This study is an important foundation for advancing RGB color-based multiplexing for imaging and analyses with smartphone cameras and related charge-coupled device and CMOS color image sensor technologies.