Kaleidoscopic fluorescent arrays for machine-learning-based point-of-care chemical sensing.

Multiplexed analysis allows simultaneous measurements of multiple targets, improving the detection sensitivity and accuracy. However, highly multiplexed analysis has been challenging for point-of-care (POC) sensing, which requires a simple, portable, robust, and affordable detection system. In this work, we developed paper-based POC sensing arrays consisting of kaleidoscopic fluorescent compounds. Using an indolizine structure as a fluorescent core skeleton, named Kaleidolizine (KIz), a library of 75 different fluorescent KIz derivatives were designed and synthesized. These KIz derivatives are simultaneously excited by a single ultraviolet (UV) light source and emit diverse fluorescence colors and intensities. For multiplexed POC sensing system, fluorescent compounds array on cellulose paper was prepared and the pattern of fluorescence changes of KIz on array were specific to target chemicals adsorbed on that paper. Furthermore, we developed a machine-learning algorithm for automated, rapid analysis of color and intensity changes of individual sensing arrays. We showed that the paper sensor arrays could differentiate 35 different volatile organic compounds using a smartphone-based handheld detection system. Powered by the custom-developed machine-learning algorithm, we achieved the detection accuracy of 97% in the VOC detection. The highly multiplexed paper sensor could have favorable applications for monitoring a broad-range of environmental toxins, heavy metals, explosives, pathogens.

New Technologies for Analysis of Extracellular Vesicles.

Extracellular vesicles (EVs) are diverse, nanoscale membrane vesicles actively released by cells. Similar-sized vesicles can be further classified (e.g., exosomes, microvesicles) based on their biogenesis, size, and biophysical properties. Although initially thought to be cellular debris, and thus under-appreciated, EVs are now increasingly recognized as important vehicles of intercellular communication and circulating biomarkers for disease diagnoses and prognosis. Despite their clinical potential, the lack of sensitive preparatory and analytical technologies for EVs poses a barrier to clinical translation. New analytical platforms including molecular ones are thus actively being developed to address these challenges. Recent advances in the field are expected to have far-reaching impact in both basic and translational studies. This article aims to present a comprehensive and critical overview of emerging analytical technologies for EV detection and their clinical applications.

Digital diffraction analysis enables low-cost molecular diagnostics on a smartphone.

The widespread distribution of smartphones, with their integrated sensors and communication capabilities, makes them an ideal platform for point-of-care (POC) diagnosis, especially in resource-limited settings. Molecular diagnostics, however, have been difficult to implement in smartphones. We herein report a diffraction-based approach that enables molecular and cellular diagnostics. The D3 (digital diffraction diagnosis) system uses microbeads to generate unique diffraction patterns which can be acquired by smartphones and processed by a remote server. We applied the D3 platform to screen for precancerous or cancerous cells in cervical specimens and to detect human papillomavirus (HPV) DNA. The D3 assay generated readouts within 45 min and showed excellent agreement with gold-standard pathology or HPV testing, respectively. This approach could have favorable global health applications where medical access is limited or when pathology bottlenecks challenge prompt diagnostic readouts.

Exploring alternative ovarian cancer biomarkers using innovative nanotechnology strategies.

Our increased understanding of ovarian cancer's blueprints (mediated by DNA and RNA) and behavior (mediated by proteins) points to wide differences across patients that cannot be depicted by histology alone. Conventional diagnosis usually entails an adequate tissue biopsy, which limits serial testing. There is thus a motivation to shift towards easier to obtain clinical samples (e.g., ascites or blood). In response, investigators are increasingly leveraging alternative circulating biomarkers in blood or proximal fluids and harnessing novel profiling platforms to help explore treatment-related effects on such biomarkers in serial fashion. In this review, we discuss how new nanotechnologies we developed intersect with alternative ovarian cancer biomarkers for improved understanding of metastases and therapeutic response.

Nanostar Clustering Improves the Sensitivity of Plasmonic Assays.

Star-shaped Au nanoparticles (Au nanostars, AuNS) have been developed to improve the plasmonic sensitivity, but their application has largely been limited to single-particle probes. We herein describe a AuNS clustering assay based on nanoscale self-assembly of multiple AuNS and which further increases detection sensitivity. We show that each cluster contains multiple nanogaps to concentrate electric fields, thereby amplifying the signal via plasmon coupling. Numerical simulation indicated that AuNS clusters assume up to 460-fold higher field density than Au nanosphere clusters of similar mass. The results were validated in model assays of protein biomarker detection. The AuNS clustering assay showed higher sensitivity than Au nanosphere. Minimizing the size of affinity ligand was found important to tightly confine electric fields and improve the sensitivity. The resulting assay is simple and fast and can be readily applied to point-of-care molecular detection schemes.

Oxidation sharpening, template stripping, and passivation of ultra-sharp metallic pyramids and wedges.

Ultra-sharp metallic pyramids and wedges with tunable tip angles and 5-nm tip radii are replicated from oxidation-sharpened silicon templates with high throughput (80 million pyramids per wafer). Atomic layer deposition of Al2 O3 shells can protect these sharp pyramidal tips for subsequent usage in near-field imaging.

Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform.

Label-free detection of multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. First, we characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles are required.

Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.

In this work, we introduce a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on clinical brain MRI while regressing demographic and technical confounding factors. We trained MUCRAN using 17,076 clinical T1 Axial brain MRIs collected from Massachusetts General Hospital before 2019 and demonstrated that MUCRAN could successfully regress major confounding factors in the vast clinical dataset. We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in AD detection. By combining MUCRAN and the uncertainty quantification method, we showed consistent and significant increases in the AD detection accuracy for newly collected MGH data (post-2019; 84.6% with MUCRAN vs. 72.5% without MUCRAN) and for data from other hospitals (90.3% from Brigham and Women's Hospital and 81.0% from other hospitals). MUCRAN offers a generalizable approach for deep-learning-based disease detection in heterogenous clinical data.

Non-contact microfluidic analysis of the stiffness of single large extracellular vesicles from IDH1-mutated glioblastoma cells.

In preparation for leveraging extracellular vesicles (EVs) for disease diagnostics and therapeutics, fundamental research is being done to understand EV biological, chemical, and physical properties. Most published studies have investigated nanoscale EVs and focused on EV biochemical content. There is much less understanding of large microscale EV characteristics and EV mechanical properties. We recently introduced a non-contact microfluidic technique that measures the stiffness of large EVs (>1 µm diameter). This pilot study probes the robustness of the microfluidic technique to distinguish between EV populations by comparing stiffness distributions of large EVs derived from glioblastoma cell lines. EVs derived from cells expressing the IDH1 mutation, a common glioblastoma mutation known to disrupt lipid metabolism, were stiffer than those expressed from wild-type cells in a statistical comparison of sample medians. A supporting lipidomics analysis showed that the IDH1 mutation increased the amount of saturated lipids in EVs. Taken together, these data encourage further investigation into the potential of high-throughput microfluidics to distinguish between large EV populations that differ in biomolecular composition. These findings contribute to the understanding of EV biomechanics, in particular for the less studied microscale EVs.

Plasmon-Enhanced Characterization of Single Extracellular Vesicles.

Extracellular vesicles (EVs) represent heterogeneous populations of membrane-bound vesicles shed from almost all kinds of cells. Although superior to conventional methods, most newly developed EV sensing platforms still require a certain number of EVs, measuring bulk signals from a group of vesicles. A new analytical approach that enables single EV analysis could be extremely valuable for understanding EVs' subtypes, heterogeneity, and production dynamics during disease development and progression. Here, we describe a new nanoplasmonic sensing platform for sensitive single EV analysis. Termed nPLEX-FL (nano-plasmonic EV analysis with enhanced fluorescence detection), the system amplifies EVs' fluorescence signals using periodic gold nanohole structures, enabling sensitive, multiplexed analysis of single EVs.

CRISPR/Cas13a-Based MicroRNA Detection in Tumor-Derived Extracellular Vesicles.

MicroRNAs (miRNAs) in extracellular vesicles (EVs) play essential roles in cancer initiation and progression. Quantitative measurements of EV miRNAs are critical for cancer diagnosis and longitudinal monitoring. Traditional PCR-based methods, however, require multi-step procedures and remain as bulk analysis. Here, the authors introduce an amplification-free and extraction-free EV miRNA detection method using a CRISPR/Cas13a sensing system. CRISPR/Cas13a sensing components are encapsulated in liposomes and delivered them into EVs through liposome-EV fusion. This allows for accurately quantify specific miRNA-positive EV counts using 1 × 108  EVs. The authors show that miR-21-5p-positive EV counts are in the range of 2%-10% in ovarian cancer EVs, which is significantly higher than the positive EV counts from the benign cells (<0.65%). The result show an excellent correlation between bulk analysis with the gold-standard method, RT-qPCR. The authors also demonstrate multiplexed protein-miRNA analysis in tumor-derived EVs by capturing EpCAM-positive EVs and quantifying miR-21-5p-positive ones in the subpopulation, which show significantly higher counts in the plasma of cancer patients than healthy controls. The developed EV miRNA sensing system provides the specific miRNA detection method in intact EVs without RNA extraction and opens up the possibility of multiplexed single EV analysis for protein and RNA markers.

Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform.

Label-free detecting multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. Herein, we first thoroughly characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles is required.

Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting.

Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer's, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.

Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform.

Label-free detecting multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. Herein, we first thoroughly characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles is required.

Rapid PCR kit: lateral flow paper strip with Joule heater for SARS-CoV-2 detection.

Polymerase chain reaction (PCR)-based diagnostic kits for point-of-care (POC) testing are highly desirable to prevent the spread of infectious diseases. Here, we demonstrate a rapid PCR testing kit that involves integrating a lateral flow paper strip with a nichrome-based thin film heater. The use of a paper membrane as a PCR-solution container results in fast thermocycling without a cooler because the membrane can contain the solution with a high specific surface area where Joule heating is applied. After PCR, amplified products are simultaneously detected at the lateral flow paper strip with the naked eye. Severe acute respiratory syndrome ß-coronavirus RNA can be detected within 30 min after PCR solution injection. This work reveals that the paper membrane can act as not only a capillary flow channel but also as a promising platform for fast PCR and detection.

Dual-Enhanced Plasmonic Biosensing for Point-of-Care Sepsis Detection.

Rapid, sensitive, simultaneous quantification of multiple biomarkers in point-of-care (POC) settings could improve the diagnosis and management of sepsis, a common, potentially life-threatening condition. Compared to high-end commercial analytical systems, POC systems are often limited by low sensitivity, limited multiplexing capability, or low throughput. Here, we report an ultrasensitive, multiplexed plasmonic sensing technology integrating chemifluorescence signal enhancement with plasmon-enhanced fluorescence detection. Using a portable imaging system, the dual chemical and plasmonic amplification enabled rapid analysis of multiple cytokine biomarkers in 1 h with sub-pg/mL sensitivities. Furthermore, we also developed a plasmonic sensing chip based on nanoparticle-spiked gold nanodimple structures fabricated by wafer-scale batch processes. We used the system to detect six cytokines directly from clinical plasma samples (n = 20) and showed 100% accuracy for sepsis detection. The described technology could be employed in rapid, ultrasensitive, multiplexed plasmonic sensing in POC settings for myriad clinical conditions.

Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis.

Seasonal outbreaks of respiratory viral infections remain a global concern, with increasing morbidity and mortality rates recorded annually. Timely and false responses contribute to the widespread of respiratory pathogenic diseases owing to similar symptoms at an early stage and subclinical infection. The prevention of emerging novel viruses and variants is also a big challenge. Reliable point-of-care diagnostic assays for early infection diagnosis play a critical role in the response to threats of epidemics or pandemics. We developed a facile method for specifically identifying different viruses based on surface-enhanced Raman spectroscopy (SERS) with pathogen-mediated composite materials on Au nanodimple electrodes and machine learning (ML) analyses. Virus particles were trapped in three-dimensional plasmonic concave spaces of the electrode via electrokinetic preconcentration, and Au films were simultaneously electrodeposited, leading to the acquisition of intense and in-situ SERS signals from the Au-virus composites for ultrasensitive SERS detection. The method was useful for rapid detection analysis (<15 min), and the ML analysis for specific identification of eight virus species, including human influenza A viruses (i.e., H1N1 and H3N2 strains), human rhinovirus, and human coronavirus, was conducted. The highly accurate classification was achieved using the principal component analysis-support vector machine (98.9%) and convolutional neural network (93.5%) models. This ML-associated SERS technique demonstrated high feasibility for direct multiplex detection of different virus species for on-site applications.

Electrokinetically enhanced label-free plasmonic sensing for rapid detection of tumor-derived extracellular vesicles.

ANALYSIS: of rare circulating extracellular vesicles (EV) from early cancers or different types of host cells requires extremely sensitive EV sensing technologies. Nanoplasmonic EV sensing technologies have demonstrated good analytical performances, but their sensitivity is often limited by EVs' diffusion to the active sensor surface for specific target EV capture. Here, we developed an advanced plasmonic EV platform with electrokinetically enhanced yields (KeyPLEX). The KeyPLEX system effectively overcomes diffusion-limited reactions with applied electroosmosis and dielectrophoresis forces. These forces bring EVs toward the sensor surface and concentrate them in specific areas. Using the keyPLEX, we showed significant improvements in detection sensitivity by ∼100-fold, leading to the sensitive detection of rare cancer EVs from human plasma samples in 10 min. The keyPLEX system could become a valuable tool for point-of-care rapid EV analysis.

Plasmon-Enhanced Single Extracellular Vesicle Analysis for Cholangiocarcinoma Diagnosis.

Cholangiocarcinoma (CCA) is a fatal disease often detected late in unresectable stages. Currently, there are no effective diagnostic methods or biomarkers to detect CCA early with high confidence. Analysis of tumor-derived extracellular vesicles (tEVs) harvested from liquid biopsies can provide a new opportunity to achieve this goal. Here, an advanced nanoplasmonic sensing technology is reported, termed FLEX (fluorescence-amplified extracellular vesicle sensing technology), for sensitive and robust single EV analysis. In the FLEX assay, EVs are captured on a plasmonic gold nanowell surface and immunolabeled for cancer-associated biomarkers to identify tEVs. The underlying plasmonic gold nanowell structures then amplify EVs' fluorescence signals, an effective amplification process at the single EV level. The FLEX EV analysis revealed a wide heterogeneity of tEVs and their marker levels. FLEX also detected small tEVs not detected by conventional EV fluorescence imaging due to weak signals. Tumor markers (MUC1, EGFR, and EPCAM) are identified in CCA, and this marker combination is applied to detect tEVs in clinical bile samples. The FLEX assay detected CCA with an area under the curve of 0.93, significantly better than current clinical markers. The sensitive and accurate nanoplasmonic EV sensing technology can aid in early CCA diagnosis.

The Liquid Biopsy Consortium: Challenges and opportunities for early cancer detection and monitoring.

The emerging field of liquid biopsy stands at the forefront of novel diagnostic strategies for cancer and other diseases. Liquid biopsy allows minimally invasive molecular characterization of cancers for diagnosis, patient stratification to therapy, and longitudinal monitoring. Liquid biopsy strategies include detection and monitoring of circulating tumor cells, cell-free DNA, and extracellular vesicles. In this review, we address the current understanding and the role of existing liquid-biopsy-based modalities in cancer diagnostics and monitoring. We specifically focus on the technical and clinical challenges associated with liquid biopsy and biomarker development being addressed by the Liquid Biopsy Consortium, established through the National Cancer Institute. The Liquid Biopsy Consortium has developed new methods/assays and validated existing methods/technologies to capture and characterize tumor-derived circulating cargo, as well as addressed existing challenges and provided recommendations for advancing biomarker assays.