Project description:Extracellular vesicles (EVs) provide an invaluable tool to analyse physiological processes because they transport, in biological fluids, biomolecules secreted from diverse tissues of an individual. EV biomarker detection requires highly sensitive techniques able to identify individual molecules. However, the lack of widespread, affordable methodologies for high-throughput EV analyses means that studies on biomarkers have not been done in large patient cohorts. To develop tools for EV analysis in biological samples, we evaluated here the critical parameters to optimise an assay based on immunocapture of EVs followed by flow cytometry. We describe a straightforward method for EV detection using general EV markers like the tetraspanins CD9, CD63 and CD81, that allowed highly sensitive detection of urinary EVs without prior enrichment. In proof-of-concept experiments, an epithelial marker enriched in carcinoma cells, EpCAM, was identified in EVs from cell lines and directly in urine samples. However, whereas EVs isolated from 5-10 ml of urine were required for western blot detection of EpCAM, only 500 μl of urine were sufficient to visualise EpCAM expression by flow cytometry. This method has the potential to allow any laboratory with access to conventional flow cytometry to identify surface markers on EVs, even non-abundant proteins, using minimally processed biological samples.
Project description:New investigation results point to the potential participation of extracellular vesicles (EVs) in the pathogenesis of coronavirus infection, its progression, and mechanisms of the therapy effectiveness. This dictates the necessity to transfer scientific testing technologies to medical practice. Here, we demonstrated the method of phenotyping and quantitative analysis of plasma EVs based on differential centrifugation, immunostaining, and high-sensitivity multicolor flow cytometry. We used EV markers that were potentially associated with SARS-CoV-2 dissemination via vesicles and cell-origination markers, characterizing objects from different cell types that could influence clinical manifestation of COVID-19. Plasma levels of CD235a+ and CD14+ EVs in patients with moderate infection were significantly increased while CD8+ and CD19+ EVs were decreased comparing with HD. Patients with severe infection had lower levels of CD4+, CD19+, and CD146+ EVs than HD. These findings demonstrate that EV concentrations in COVID-19 are severity related. Moreover, the three-point dynamic assessment demonstrated significant loss of CD63+ and CD147+ plasma EVs. The used method can be a convenient tool for vital infection pathogenesis investigation and for COVID-19 diagnostics.
Project description:A major challenge in many clinical diagnostic applications is the measurement of low-abundance proteins and other biomolecules in biological fluids. Digital technologies such as the digital enzyme-linked immunosorbent assay (ELISA) have enabled 1000-fold increases in sensitivity over conventional protein detection methods. However, current digital ELISA technologies still possess insufficient sensitivities for many rare protein biomarkers and require specialized instrumentation or time-consuming workflows that have limited their widespread implementation. To address these challenges, we have developed a more sensitive and streamlined digital ELISA platform, Molecular On-bead Signal Amplification for Individual Counting (MOSAIC), which attains low attomolar limits of detection, with an order of magnitude enhancement in sensitivity over these other methods. MOSAIC uses a rapid, automatable flow cytometric readout that vastly increases throughput and is easily integrated into existing laboratory infrastructure. As MOSAIC provides high sampling efficiencies for rare target molecules, assay bead number can readily be tuned to enhance signal-to-background with high measurement precision. Furthermore, the solution-based signal readout of MOSAIC expands the number of analytes that can simultaneously be measured for higher-order multiplexing with femtomolar sensitivities or below, compared with microwell- or droplet-based digital methods. As a proof of principle, we apply MOSAIC toward improving the detectability of low-abundance cytokines in saliva and ultrasensitive multiplexed measurements of eight protein analytes in plasma and saliva. The attomolar sensitivity, high throughput, and broad multiplexing abilities of MOSAIC provide highly accessible and versatile ultrasensitive capabilities that can potentially accelerate protein biomarker discovery and diagnostic testing for diverse disease applications.
Project description:Extracellular vesicles (EVs) are promising biomarkers for several diseases, however, no simple and robust methods exist to characterize EVs in a clinical setting. The EV Array analysis is based on a protein microarray platform, where antibodies are printed onto a solid surface that enables the capture of small EVs (sEVs) by their surface or surface-associated proteins. The EV Array analysis was transferred to an easily handled microtiter plate (MTP) format and a range of optimization experiments were performed within this study. The optimization was performed in a comprehensive analytical setup where the focus was on the selection of additives added to spotting-, blocking-, and incubation buffers as well as the storage of printed antibody arrays under different temperatures from one day to 12 weeks. After ending the analysis, the stability of the fluorescent signal was investigated at different storage conditions for up to eight weeks. The various parameters and conditions tested within this study were shown to have a high influence on each other. The reactivity of the spots was found to be preserved for up to 12 weeks when stored at room temperature and using blocking procedure IV in combination with trehalose in the spotting buffer. Similar preservation could be obtained using glycerol or sciSPOT D1 in the spotting buffers, but only if stored at 4 °C after blocking procedure I. Conclusively, it was found that immediate scanning of the MTPs after analysis was not critical if stored dried, in the dark, and at room temperature. The findings in this study highlight the necessity of performing optimization experiments when transferring an established analysis to a new technological platform.
Project description:Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.
Project description:Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.
Project description:We report a covalent chemistry-based hepatocellular carcinoma (HCC)-specific extracellular vesicle (EV) purification system for early detection of HCC by performing digital scoring on the purified EVs. Earlier detection of HCC creates more opportunities for curative therapeutic interventions. EVs are present in circulation at relatively early stages of disease, providing potential opportunities for HCC early detection. We develop an HCC EV purification system (i.e., EV Click Chips) by synergistically integrating covalent chemistry-mediated EV capture/release, multimarker antibody cocktails, nanostructured substrates, and microfluidic chaotic mixers. We then explore the translational potential of EV Click Chips using 158 plasma samples of HCC patients and control cohorts. The purified HCC EVs are subjected to reverse-transcription droplet digital PCR for quantification of 10 HCC-specific mRNA markers and computation of digital scoring. The HCC EV-derived molecular signatures exhibit great potential for noninvasive early detection of HCC from at-risk cirrhotic patients with an area under receiver operator characteristic curve of 0.93 (95% CI, 0.86 to 1.00; sensitivity = 94.4%, specificity = 88.5%).
Project description:Extracellular vesicle (EV)-based therapies and vaccines are emerging. However, employment at the scale for population-based dose development is always a huge bottleneck. In order to overcome such a roadblock, we introduce a simple and straightforward approach for promoting cellular production of dendritic cell derived EVs (DEVs) by leveraging phototherapy based light induction. Under the optimization of light wavelengths, intensities, and exposure times, we achieved more than 13-fold enhancement in DEV production rate, while maintaining good integral quality and immune function from produced EVs. The LED light at 365 nm is optimal to reliably trigger enhanced cellular production of EVs no matter cell line types. Our observation and other reported studies support longer near UV wavelength does not impair cell growth. We conducted a series of investigations in terms of size, zeta potential, morphology, immune surface markers and cytokines, biocompatibility, cellular uptake behaviour, and immune-modulation ability on eliciting cellular responses in vitro. We also validated the biodistribution, immunogenicity, and administration safety using light-promoted DEVs in mice models from both male and female genders. Overall data supports that light promoted DEVs are highly immune functional with great biocompatibility for serving as good therapeutic platforms. The in vivo animal study also demonstrated light-promoted DEVs are as well tolerated as native DEVs, with no safety concerns. Taken together, the data supports that light promoted DEVs are in excellent quality, high biocompatibility, in vivo tolerant, and viable for serving as an ideal therapeutic platform in scalable production.
Project description:Herein, we developed a specific, rapid sensor to quantify placental extracellular vesicle (EV) protein biomarkers of early pregnancy complications. A distinct tetraspanin CD9 and placental alkaline phosphatase (PLAP) expression pattern was observed via targeted multiple reaction monitoring of EVs from maternal plasma collected before 18 weeks of gestation. A classification model was developed using training and validation patient sets, distinguishing between individuals at high risk of developing complications from those with normal pregnancies, achieving 80% sensitivity, 90% specificity, 89% positive predictive value (PPV), and 82% negative predictive value (NPV). Superparamagnetic nanoflowers that captured target EVs (CD9+/PLAP+) were used to construct a 4-flex glass strip nanozymatic readout system. The sensor analyzes plasma for EVs, identifying gestational diabetes mellitus risk with a 95% combined sensitivity, 100% specificity, 100% PPV, and 96% NPV. This nanoplatform identifies individuals at risk of developing pregnancy complications with a >90% classification accuracy, exhibiting potential for clinical applications.
Project description:Detecting early cancer through liquid biopsy is challenging due to the lack of specific biomarkers for early lesions and potentially low levels of these markers. The current study systematically develops an extracellular-vesicle (EV)-based test for early detection, specifically focusing on high-grade serous ovarian carcinoma (HGSOC). The marker selection is based on emerging insights into HGSOC pathogenesis, notably that it arises from precursor lesions within the fallopian tube. This work thus establishes murine fallopian tube (mFT) cells with oncogenic mutations and performs proteomic analyses on mFT-derived EVs. The identified markers are then evaluated with an orthotopic HGSOC animal model. In serially-drawn blood of tumor-bearing mice, mFT-EV markers increase with tumor initiation, supporting their potential use in early cancer detection. A pilot clinical study (n = 51) further narrows EV markers to five candidates, EpCAM, CD24, VCAN, HE4, and TNC. The combined expression of these markers distinguishes HGSOC from non-cancer with 89% sensitivity and 93% specificity. The same markers are also effective in classifying three groups (non-cancer, early-stage HGSOC, and late-stage HGSOC). The developed approach, for the first time inaugurated in fallopian tube-derived EVs, could be a minimally invasive tool to monitor women at high risk of ovarian cancer for timely intervention.