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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

Gold-Nanoparticle-Coated Magnetic Beads for ALP-Enzyme-Based Electrochemical Immunosensing in Human Plasma.

A simple and sensitive AuNP-coated magnetic beads (AMB)-based electrochemical biosensor platform was fabricated for bioassay. In this study, AuNP-conjugated magnetic particles were successfully prepared using biotin-streptavidin conjugation. The morphology and structure of the nanocomplex were characterized by scanning electron microscopy (SEM) with energy-dispersive X-ray analysis (EDX) and UV-visible spectroscopy. Moreover, cyclic voltammetry (CV) was used to investigate the effect of AuNP-MB on alkaline phosphatase (ALP) for electrochemical signal enhancement. An ALP-based electrochemical (EC) immunoassay was performed on the developed AuNP-MB complex with indium tin oxide (ITO) electrodes. Subsequently, the concentration of capture antibodies was well-optimized on the AMB complex via biotin-avidin conjugation. Lastly, the developed AuNP-MB immunoassay platform was verified with extracellular vesicle (EV) detection via immune response by showing the existence of EGFR proteins on glioblastoma multiforme (GBM)-derived EVs (108 particle/mL) spiked in human plasma. Therefore, the signal-enhanced ALP-based EC biosensor on AuNP-MB was favorably utilized as an immunoassay platform, revealing the potential application of biosensors in immunoassays in biological environments.

Recent Advances in Molecular Diagnosis of Pulmonary Fibrosis for Precision Medicine.

Pulmonary fibrosis is a serious, progressive lung disease characterized by scarring and stiffening lung tissues, affecting the respiratory system and leading to organ failure. It is a complex disease consisting of alveolar damage, chronic inflammation, and a varying degree of lung fibrosis. Significant challenges with pulmonary fibrosis include the lack of effective means to diagnose the disease at early stages, identify patients at higher risks of progress, and assess disease progression and treatment response. Precision medicine powered by accurate molecular profiling and phenotyping could significantly improve our understanding of the disease's heterogeneity, potential biomarkers for diagnosis and prognosis, and molecular targets for treatment development. This Review discusses various translational model systems, including organoids and lung-on-a-chip systems, biomarkers in single cells and extracellular vesicles, and functional pharmacodynamic markers. We also highlight emerging sensing technologies for molecular characterization of pulmonary fibrosis and biomarker detection.

Physisorption of Affinity Ligands Facilitates Extracellular Vesicle Detection with Low Non-Specific Binding to Plasmonic Gold Substrates.

Plasmonic biosensors are increasingly being used for the analysis of extracellular vesicles (EVs) originating from disease areas. However, the high non-specific binding of EVs to a gold-sensing surface has been a critical problem and hindered the true translational potential. Here, we report that direct antibody immobilization on the plasmonic gold surface via physisorption shows excellent capture of cancer-derived EVs with ultralow non-specific binding even at very high concentrations. Contrary to commonly used methods that involve thiol-based linker attachment and an EDC/sulfo-NHS reaction, we show a higher specific capture rate and >50-fold lower non-specific on citrate-capped plain and nanopatterned gold surfaces. The method provides a simple, fast, and reproducible means to functionalize plasmonic gold surfaces with antibodies for robust EV biosensing.

Hydrogel Stamping for Rapid, Multiplexed, Point-of-Care Immunostaining of Cells and Tissues.

In the era of precision oncology, multicolor fluorescence imaging has become a core technology for multiplexed molecular analysis of cellular and tissue specimens. However, conventional solution-based staining is labor-intensive and time-consuming and requires considerable expertise to yield optimal results, which creates difficulties for employing this technology in resource-limited settings. Here, we report a new immunostaining method based on hydrogel stamping, which is simple, fast, easy to use, and reproducible. We showed that a hydrophilic hydrogel stamp could effectively transfer fluorescent antibodies to targets and withdraw an excess solution when the reaction is completed, obviating the need for extra washing. This unique property allows for quality immunostaining in 5 min for cells using one-eighth of antibody consumption compared to the conventional solution-based method. Furthermore, we implemented fluorescence quenching and immunocycling with hydrogel staining for multiplexed analysis of 9 protein markers at a single cell level. Finally, we applied the immunocycling method to human breast cancer tissue samples and showed quality immunostaining over a large area (∼2 cm2) in 30 min for molecular subtyping of breast cancer. The hydrogel immunostaining could open new opportunities for rapid, automated, and multiplexed profiling in compact point-of-care systems for molecular cancer diagnosis.

Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease.

Deep learning has the potential to standardize and automate diagnostics for complex medical imaging data, but real-world clinical images are plagued by a high degree of heterogeneity and confounding factors that may introduce imbalances and biases to such processes. To address this, we developed and applied a data matching algorithm to 467,464 clinical brain magnetic resonance imaging (MRI) data from the Mass General Brigham (MGB) healthcare system for Alzheimer's disease (AD) classification. We identified 18 technical and demographic confounding factors that can be readily distinguished by MRI or have significant correlations with AD status and isolated a training set free from these confounds. We then applied an ensemble of 3D ResNet-50 deep learning models to classify brain MRIs between groups of AD, mild cognitive impairment (MCI), and healthy controls. From a confounder-free matched dataset of 287,367 MRI files, we achieved an area under the receiver operating characteristic (AUROC) of 0.82 in distinguishing healthy controls from patients with AD or MCI. We also showed that confounding factors in heterogeneous clinical data could lead to artificial gains in model performance for disease classification, which our data matching approach could correct. This approach could accelerate using deep learning models for clinical diagnosis and find broad applications in medical image analysis.

Integrated Analytical System for Clinical Single-Cell Analysis.

High-dimensional analyses of cancers can potentially be used to better define cancer subtypes, analyze the complex tumor microenvironment, and perform cancer cell pathway analyses for drug trials. Unfortunately, integrated systems that allow such analyses in serial fine needle aspirates within a day or at point-of-care currently do not exist. To achieve this, an integrated immunofluorescence single-cell analyzer (i2SCAN) for deep profiling of directly harvested cells is developed. By combining a novel cellular imaging system, highly cyclable bioorthogonal FAST antibody panels, and integrated computational analysis, it is shown that same-day analysis is possible in thousands of harvested cells. It is demonstrated that the i2SCAN approach allows comprehensive analysis of breast cancer samples obtained by fine needle aspiration or core tissues. The method is a rapid, robust, and low-cost solution to high-dimensional analysis of scant clinical specimens.