The work was primarily supported by research funding provided by NIH (U01CA214254,&#160;R01HD090927, R01AI122932, R01AI113725 and R21Al126361-01),&#160;Arizona Biomedical Research Commission (ABRC) young investigator award.

1. Preparation of nanoparticle probes
NOTE: This assay utilizes Functionalized Gold Nanorods (AuNRs; 25 nm diameter x 71 nm length) that are covalently conjugated with neutravidin polymers (AV) and have a surface plasmon resonance peak that produces a red (641 nm peak) scattering signal upon DFM illumination.
<ol>
	<li>Wash 40 &#181;L of AuNR-AV (2.56 x 1011 particles) three times with 200 &#181;L PBS (pH 7.0) by centrifugation and aspiration (8,500 x g at 4 &#176;C for 10 minutes), followed by a final centrifugation and aspiration step after which the AuNR-AV pellet is suspended in 40 &#181;L PBS.</li>
	<li>Mix this AuNR-AV suspension with 10 &#181;L biotinylated antibody (0.5 mg/mL) specific for an antigen on the surface of the exosome subtype of interest and 150 &#181;L of PBS and then mix at 4 &#176;C for 2 h using a mixer to allow neutravidin-biotin binding to reach completion.</li>
	<li>Wash the resulting antibody-conjugated AuNRs (AuNR-IgG) three times by centrifugation and aspiration (6,500 x g at 4 &#176;C for 10 minutes), and then suspend them in 200 &#181;L PBS and store them at 4 &#176;C until use. 
	NOTE: Sterile technique and short storage times must be used to avoid contamination and degradation of the AuNR-IgG. It is best to use antibody-conjugated AuNRs within 24 hours of their conjugation.</li>
</ol>
2. Preparation of EV capture slides
<ol>
	<li>Dilute selected exosome capture antibodies to 0.025 mg/mL in PBS and add 1 &#181;L/well of this dilution onto a multi-well protein A/G slide, and then incubate this slide at 37 &#176;C for 1 h in a humidified chamber to allow capture antibody binding to protein A/G immobilized on the slide.</li>
	<li>Aspirate wells to remove unbound antibodies, and wash wells three times by the addition and aspiration of 1 &#181;L/well of PBS, then load each well with 1 &#181;L of blocking buffer (see Table of Materials) and incubate the slide for 2 h at 37 &#176;C in a humidified chamber to block any remaining protein binding sites.</li>
	<li>Aspirate wells to remove blocking buffer, wash wells three times by the addition and aspiration of 1 &#181;L/well of PBS, and immediately use the blocked slides for exosome capture and analysis.</li>
</ol>
3. Standard curve preparation
<ol>
	<li>To accurately quantify the absolute or relative abundance of a specific exosome subtype, the user must generate a standard curve with a pure exosome population that uniformly expresses the exosome surface biomarker of interest. This study analyzes the abundance of exosomes expressing a metastasis-associated membrane protein, Ephrin A2 receptor, which has a reported relationship with pancreatic cancer stage and prognosis6,18. 
	NOTE: The human pancreatic cancer cell line PANC-1 and its exosomes are known to express this protein and isolated exosomes from this cell line were used to generate a standard curve to quantify the number of exosomes that express this protein in complex exosome samples.</li>
	<li>Culture cells for 48 hours at 37 &#176;C in serum-free culture media to allow exosome accumulation in the media, then isolate cell culture supernatants by centrifugation of suspension cultures or direct aspiration of culture media from adherent cell cultures.</li>
	<li>Centrifuge the collected media at 2000 x g for 30 min to remove debris and recover the supernatant.</li>
	<li>Filter the clarified culture supernatant through a 0.45 &#181;m low protein binding filter unit of appropriate capacity (e.g. a 250 mL polyethersulfone vacuum filtration unit).</li>
	<li>Concentrate the resulting filtrate by centrifugation at 3200 x g using a 100,000 nominal molecular weight limit filter system to a 250 &#181;L final volume. Collect the retained volume from this filter, then wash the filter with 200 &#181;L PBS, and combine this wash volume with the collected exosome sample volume.</li>
	<li>Centrifuge this sample at 21,000 x g for 45 minutes and carefully recover the supernatant, taking care not to collect any precipitated material.</li>
	<li>Centrifuge the recovered supernatant at 100,000 x g for 3 hours to precipitate the exosomes. Aspirate away the supernatant and collect the exosome pellet in 100 &#181;L PBS.</li>
	<li>Store the resulting exosome suspensions at 4 &#176;C if used within 24 hours or at -80 &#176;C for long term storage. 
	NOTE: Do not subject exosome samples to repeat freeze-thaw cycles.</li>
	<li>Quantify an aliquot of the exosome suspension after mixing by direct measurement of exosome numbers (e.g., by nanoparticle tracking analysis or tunable resistive pulse sensing or by measuring the protein concentration of exosome lysates by micro-bicinchoninic acid assay, or an equivalent method, as a means to approximate exosome quantity)16,19.</li>
	<li>Generate a set of serial dilutions of the exosome suspension to allow comparison of nanoparticle signal to input exosome number or protein content.</li>
	<li>Transfer 1 &#181;L of each exosome standard to each of its replicate wells on the assay plate. 
	NOTE: Standard curves can be used to calculate the slope of the correlation line between nanoparticle signal and exosome concentration to (1) evaluate assay performance and (2) determine the relative concentration of target exosomes in experimental samples.</li>
</ol>
4. Processing human plasma or serum samples
<ol>
	<li>Collect plasma or serum samples by standard methods and store at -80 &#176;C until needed for exosome analysis. Rapidly thaw samples in a room temperature water bath. Repeatedly mix the thawed samples by inversion to promote homogenous suspension. 
	NOTE: Results from serum and plasma samples may not be equivalent, since there is a significant release of exosomes during the clotting reaction.</li>
	<li>Centrifuge plasma or serum samples at 500 x g for 15 min to precipitate protein aggregates and other debris. Transfer an aliquot of the plasma or serum sample to a fresh tube and add PBS to generate a 1:1 dilution. Mix the diluted sample by gentle vortexing or inversion, as appropriate. Transfer 1 &#181;L of each plasma or serum suspension to each of its replicate wells on the assay plate.</li>
</ol>
5. Exosome capture and detection
<ol>
	<li>Load wells of a blocked EV capture slide with 1 &#181;L/well of exosome sample, using 8 replicates per sample, and incubate the slide overnight at 4 &#176;C in a humidified chamber. Aspirate all sample wells and then add 1 &#181;L/well of PBS to wash wells and remove unbound exosomes and other contaminants from the loaded exosome sample.</li>
	<li>Load sample wells with 1 &#181;L/well of a previously prepared AuNR-IgG suspension (see section 1 above) and incubate the slide for 2 h at 37 &#176;C in a humidified chamber. Aspirate the nanoparticle solution and wash the slide in PBS supplemented with 0.01% Tween-20 (PBST) for 10 min using a mixer, then aspirate and wash all sample wells with deionized water for 10 min using a rotating mixer, and air-dry for subsequent LMDFM imagery. 
	NOTE: Inter-assay coefficients of variation (CVs) are assessed from eight replicates of the same sample. Samples that exhibit CVs &#62;20% are considered non-informative and should be repeated, if there is sufficient sample.</li>
</ol>
6. DFM Image Capture
<ol>
	<li>Capture images for exosome quantification under consistent lighting using a digital camera attached to a microscope equipped with a dark-field condenser (1.2 &#60; NA &#60; 1.4) and a 4x objective employing a 1/220 s exposure time.</li>
	<li>Open the image capture software. 
	NOTE: We use NIS-Elements microscope imaging software (see Table of Materials) for the protocol described below, but it is possible to use another software that can match its image capture parameters. NIS-Elements Viewer imaging software is a free standalone program to view image files and data sets that contains analysis, visualization and archiving tools. The parameters below are also for a microscope with autofocus and an automated stage that permits multiple images to be automatically captured and stitched into a single image.</li>
	<li>Place the slide upside down on the microscope stage, adjust the slide position and apply a small drop of immersion oil on the back of the slide, where the condenser lens contacts the slide.</li>
	<li>Click the live button in the software interface, and adjust the exposure time against a high concentration standard well to ensure the image is not saturated.</li>
	<li>Open the Scan Large Image window from the Acquire tab and set the software interface parameters as follows: Macro Image Optical conf = current; Objective: 2:10x, Scanning Optical conf = current, Objective: 2:10x; Stitching Overlap = 20%; Stitching via = Optimal Path.</li>
	<li>Choose Create Large Image, Close active shutter during stage movement, Wait before each Capture: 20 ms, Focus manually at start, and Use step-by-step focus every 20 field. These setting will be saved with the scanned images.</li>
	<li>Move the microscope stage to define the left top right bottom limits of the target scan field. Adjust the focus to achieve a clear image on the monitor and adjust the condenser settings and environmental lighting as necessary to minimize any lighting irregularities in the focused image.</li>
	<li>Name the image output file in the software. Click the Scan button and allow the microscope to scan and create and save a stitched image of the entire slide.</li>
	<li>Open the saved image with the image capture software you are using and save it at 1/8 scale for subsequent analysis on the DSM plugin in ImageJ.</li>
</ol>
7. DFM image analysis
<ol>
	<li>Download the ImageJ program (https://imagej.nih.gov/ij/). Install the DSM algorithm plugin into ImageJ using the instructions listed at https://imagej.net/Plugins#Installing_plugins_manually.</li>
	<li>Open the ImageJ software, then set the following input parameters within the DSM algorithm: Contour threshold (Ct) = 253.020, Type = Red, Center scale (S) = 0.8, Low (Lt)/High (Ht) quantification limit = 0/62.</li>
	<li>Open the saved image from section 6.8 with ImageJ. Choose the DSM Scan button from the Plugins tab, then define the number of columns and rows according to the opened image. The program can recognize the detecting areas and analysis the scatter intensity of nanoparticle in according areas automatically. Set the following input parameters within the DSM Scan window: Resize Percentage = 25, Spot Diameter (in pixels) = 190 &#8211; 200, Diameter Range = 32, Increment diameter (in pixels) = 8, DSM Configuration - Low limit = 0, High limit = 62, Adjacent Distance = 100, Subtract Bias = 0. 
	NOTE: The results of scatter intensity of nanoparticle reflect the quantity of bound exosomes on the slide.</li>
</ol>

<ol>
	<li>Andaloussi S, E. L., Mager, I., Breakefield, X. O., Wood, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extracellular+vesicles:+biology+and+emerging+therapeutic+opportunities.">Extracellular vesicles: biology and emerging therapeutic opportunities.</a> Nature Reviews Drug Discovery. 12 (5), 347-357 (2013).</li><li>Choi, D. S., Kim, D. K., Kim, Y. K., Gho, Y. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteomics,+transcriptomics+and+lipidomics+of+exosomes+and+ectosomes.">Proteomics, transcriptomics and lipidomics of exosomes and ectosomes.</a> Proteomics. 13 (10-11), 1554-1571 (2013).</li><li>Schorey, J. S., Cheng, Y., Singh, P. P., Smith, V. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exosomes+and+other+extracellular+vesicles+in+host-pathogen+interactions.">Exosomes and other extracellular vesicles in host-pathogen interactions.</a> EMBO Reports. 16 (1), 24-43 (2015).</li><li>Hoshino, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumour+exosome+integrins+determine+organotropic+metastasis.">Tumour exosome integrins determine organotropic metastasis.</a> Nature. 527 (7578), 329-335 (2015).</li><li>Zaborowski, M. P., Balaj, L., Breakefield, X. O., Lai, C. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extracellular+Vesicles:+Composition,+Biological+Relevance,+and+Methods+of+Study.">Extracellular Vesicles: Composition, Biological Relevance, and Methods of Study.</a> Bioscience. 65 (8), 783-797 (2015).</li><li>Liang, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nanoplasmonic+Quantification+of+Tumor-derived+Extracellular+Vesicles+in+Plasma+Microsamples+for+Diagnosis+and+Treatment+Monitoring.">Nanoplasmonic Quantification of Tumor-derived Extracellular Vesicles in Plasma Microsamples for Diagnosis and Treatment Monitoring.</a> Nature Biomedical Engineering. 1, (2017).</li><li>Severino, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extracellular+Vesicles+in+Bile+as+Markers+of+Malignant+Biliary+Stenoses.">Extracellular Vesicles in Bile as Markers of Malignant Biliary Stenoses.</a> Gastroenterology. 153 (2), 495-504 (2017).</li><li>Osteikoetxea, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+and+proteomic+characterization+of+extracellular+vesicles+in+human+pancreatic+juice.">Detection and proteomic characterization of extracellular vesicles in human pancreatic juice.</a> Biochemical and Biophysical Research Communications. 499 (1), 37-43 (2018).</li><li>Bulacio, R. P., Nosetto, E. C., Brandoni, A., Torres, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+finding+of+caveolin-2+in+apical+membranes+of+proximal+tubule+and+first+detection+of+caveolin-2+in+urine:+A+promising+biomarker+of+renal+disease.">Novel finding of caveolin-2 in apical membranes of proximal tubule and first detection of caveolin-2 in urine: A promising biomarker of renal disease.</a> Journal of Cellular Biochemistry. , (2018).</li><li>Nair, S., Tang, K. D., Kenny, L., Punyadeera, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Salivary+exosomes+as+potential+biomarkers+in+cancer.">Salivary exosomes as potential biomarkers in cancer.</a> Oral Oncology. 84, 31-40 (2018).</li><li>Kim, J. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnostic+value+of+microRNAs+derived+from+exosomes+in+bronchoalveolar+lavage+fluid+of+early-stage+lung+adenocarcinoma:+A+pilot+study.">Diagnostic value of microRNAs derived from exosomes in bronchoalveolar lavage fluid of early-stage lung adenocarcinoma: A pilot study.</a> Thoracic Cancer. 9 (8), 911-915 (2018).</li><li>Boussadia, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acidic+microenvironment+plays+a+key+role+in+human+melanoma+progression+through+a+sustained+exosome+mediated+transfer+of+clinically+relevant+metastatic+molecules.">Acidic microenvironment plays a key role in human melanoma progression through a sustained exosome mediated transfer of clinically relevant metastatic molecules.</a> Journal of Experimental &#38; Clinical Cancer Research. 37 (1), 245 (2018).</li><li>An, M., Wu, J., Zhu, J., Lubman, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+an+Optimized+Ultracentrifugation+Method+versus+Size-Exclusion+Chromatography+for+Isolation+of+Exosomes+from+Human+Serum.">Comparison of an Optimized Ultracentrifugation Method versus Size-Exclusion Chromatography for Isolation of Exosomes from Human Serum.</a> Journal of Proteome Research. , (2018).</li><li>Brenner, A. W., Su, G. H., Momen-Heravi, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Isolation+of+Extracellular+Vesicles+for+Cancer+Diagnosis+and+Functional+Studies.">Isolation of Extracellular Vesicles for Cancer Diagnosis and Functional Studies.</a> Methods in Molecular Biology. 1882, 229-237 (2019).</li><li>Li, P., Kaslan, M., Lee, S. H., Yao, J., Gao, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Progress+in+Exosome+Isolation+Techniques.">Progress in Exosome Isolation Techniques.</a> Theranostics. 7 (3), 789-804 (2017).</li><li>Sun, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Noise+Reduction+Method+for+Quantifying+Nanoparticle+Light+Scattering+in+Low+Magnification+Dark-Field+Microscope+Far-Field+Images.">Noise Reduction Method for Quantifying Nanoparticle Light Scattering in Low Magnification Dark-Field Microscope Far-Field Images.</a> Analytical Chemistry. 88 (24), 12001-12005 (2016).</li><li>Sun, D., Hu, T. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+low+cost+mobile+phone+dark-field+microscope+for+nanoparticle-based+quantitative+studies.">A low cost mobile phone dark-field microscope for nanoparticle-based quantitative studies.</a> Biosensors and Bioelectronics. 99, 513-518 (2018).</li><li>Koshikawa, N., Minegishi, T., Kiyokawa, H., Seiki, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Specific+detection+of+soluble+EphA2+fragments+in+blood+as+a+new+biomarker+for+pancreatic+cancer.">Specific detection of soluble EphA2 fragments in blood as a new biomarker for pancreatic cancer.</a> Cell Death &#38; Disease. 8 (10), e3134 (2017).</li><li>Clayton, A., Turkes, A., Navabi, H., Mason, M. D., Tabi, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Induction+of+heat+shock+proteins+in+B-cell+exosomes.">Induction of heat shock proteins in B-cell exosomes.</a> Journal of Cell Science. 118 (Pt 16), 3631-3638 (2005).</li><li>Henne, W. M., Buchkovich, N. J., Emr, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ESCRT+pathway.">The ESCRT pathway.</a> Developmental Cell. 21 (1), 77-91 (2011).</li><li>Panagiotara, A., Markou, A., Lianidou, E. S., Patrinos, G. P., Katsila, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exosomes:+a+cancer+theranostics+road+map.">Exosomes: a cancer theranostics road map.</a> Public Health Genomics. 20 (2), 116-125 (2017).</li></ol>

The authors declare that they have no competing financial interests.

Exosomes arise from regulated invaginations of the outer endosome membrane that produce multivesicular bodies, a specialized subset of endosomes that contain a large number of intraluminal vesicles that undergo fusion with the plasma membrane to release mature exosomes into the extracellular space. Due to this biogenesis pathway, exosomes can carry membrane-bound factors associated with membrane fractions that comprise or fuse with the endosome membrane, as well as multiple different types of cytosolic components, and thus contain cargoes of proteins, DNA and various RNA subtypes (mRNAs, microRNAs, long non-coding RNAs) that can reflect the phenotype of their cell of origin20. Since exosomes are secreted by most if not all cell types, can exhibit increased secretion from diseased or malignant cells, and accumulate in most body fluids, exosomes are the subject of comprehensive and systematic investigation as a promising minimally invasive means to detect specific disease conditions and monitor their responses to treatment21.
Exosome isolation, which is required for most current exosome analyses, is a lengthy and labor-intensive procedure, restricting the clinical translation of exosome-associated biomarkers with potential medical relevance. Many common isolation methods (ultracentrifugation, size-exclusion, precipitation, etc.) often do not sufficiently distinguish exosomes (30&#8211;100 nm) from micro-vesicles (100&#8211;1000 nm) and apoptotic bodies (100&#8211;5000 nm) due to overlaps in their size ranges or physical properties or may damage exosome integrity15. New approaches are under development that may permit more rapid exosome analyses, but it is not clear how feasible it may be to implement many of these platforms in clinical settings due.
In this report, we present a novel approach that allows nanoparticle-based high-throughput exosome quantification using low magnification dark field microscope images. This method does not require exosome purification, expensive dedicated equipment, or novel technical expertise and should thus be amenable to rapid translation in most research and clinical settings. Our assay can be applied to precisely quantify the concentration of a target exosome population bearing a specific biomarker when assay results are compared to a standard curve, since our results exhibit there is a strong linear correlation (R2 = 0.99) between optical response and exosome concentration. To demonstrate the real world potential of this approach, we have provided data where we employed this method to quantify the concentration of an exosome biomarker associated with pancreatic cancer in serum samples obtained from patients with and without pancreatic cancer.
In LMDFM, the entire sample well is imaged to avoid the selection bias found in high-magnification DFM analyses, where users must directly choose the sample fields to capture for subsequent image analysis, but is subject to light scattering artifacts from surface irregularities, including scratches and sample debris. This background can be reduced to detect target exosome-derived signal using our DSM noise-reduction algorithm that runs on the NIH image analysis program, ImageJ, but care must still be taken to avoid introducing such artifacts which may reduce the dynamic range of the assay.
Materials used in this assay:
Multi-well SuperProtein A/G slides holding 1 &#181;L/well were purchased from Arrayit Corporation (AGMSM192BC). Nanoparticles were obtained from Nanopartz (C12-25-650-TN-DIH-50-1, 6.4 x 1012/mL). DFM images were captured with a Nikon DiR2 digital camera attached to a Nikon Ti-Eclipse microscope, with consistent lighting and a 1/220 s exposure time. The PANC-1 cell line used in this study was purchased from the American Type Culture Collection.

Exosomes are released from most cell types and play a key role in cell-to-cell communications, including pathophysiological processes associated with various diseases, since they can be home to specific tissues or cell types, and contain a variety of nucleic acids, proteins, and lipids that reflect their cell of origin and can exert regulatory effects on their recipient cells1,2,3,4. Exosomes are often secreted at elevated levels in disease states, can interact with both adjacent and distant cells, and are found at relatively high concentration in the circulation as well as most other body fluids, including saliva, urine, pancreatic and bile juice, and bronchoalveolar lavage fluid5,6,7,8,9,10,11. This abundance and stability of exosomes in human body fluids, coupled with their information-rich nature, makes them ideal biomarkers for disease diagnosis and treatment monitoring.
This includes tumor-derived exosomes (TDEs), which contain tumor-specific or selective factors, which can serve as disease biomarkers, including tumor-associated mutant alleles. TDEs can participate in the remodeling of the tumor microenvironment to facilitate tumor development and metastasis, and regulate anti-tumor responses12. Increased TDE secretion is a common phenotype of most cancers and several features of the tumor microenvironment, including hypoxia, acidic pH, and inflammation, are known to promote exosome secretion. Surprisingly, given the number of cells that secrete exosomes, an increase in total exosome level can, itself, function as a cancer biomarker. For example, a recent study found that the total EV concentration in&#160;bile&#160;juice discriminates malignant and nonmalignant in common&#160;bile&#160;duct stenosis patients with 100% accuracy7. Similar results have been found with studies using other body fluids, including plasma. However, due to the potential for subject to subject variation, and other confounding factors, most studies investigating exosomes as disease biomarkers have focused on detection of biomarker that are selectively associated with TDEs instead of total exosome numbers.
Translating exosome biomarkers into clinical practice, however, remains challenging since most reported exosome assay approaches require time-consuming and labor-intensive isolation procedures 13. Currently popular exosome isolation methods include ultracentrifugation, density gradients, size-exclusion, co-precipitation, affinity capture, and microfluidic isolation approaches. Ultracentrifugation is the &#8220;gold standard&#8221; method, and is most commonly used for exosome isolations, but this procedure is time-consuming and results in exosome damage and exosome membrane clustering, and produces exosome samples that are contaminated with proteins, lipoproteins and other factors that can influence subsequent analyses14. Most exosome isolation methods, including ultracentrifugation, cannot separate exosomes (30&#8211;150 nm) from micro-vesicles (100&#8211;1000 nm) and apoptotic bodies (100&#8211;5000 nm), which arise through different mechanisms and have different functions, due to the size overlap among these groups, and the diversity of exosome populations15. New approaches are needed to improve the sensitivity and reproducibility of exosome assays by improving exosome recovery while reducing exosome damage and contamination, although any assays based on such methods will also need to be optimized to render them suitable for translation to applications in clinical settings.
Several recent studies have proposed to employ integrated platforms to capture and analyze exosomes directly from body fluids. These methods employ microfluidic, electrokinetic, affinity capture, and various other methods for exosome isolation, and electrochemistry, surface plasmon resonance, and other methods to detect captured exosomes. It is not clear how feasible many of these approaches will be in clinical settings, due to their complexity, expense, low-throughput or other issues.
We have developed a rapid and inexpensive assay that can be used for sensitive and specific quantification of total exosomes and specific exosome subtypes, including disease-associated exosomes, such as TDEs, which requires only a small amount of sample and which employs a streamlined workflow suitable for clinical environments. In this assay, a slide is coated with antibodies that bind either an exosome-specific or disease-specific marker expressed on the exosome surface in order to directly capture target exosomes present in small volume plasma or serum samples applied to wells on the slide. Captured exosomes are then hybridized with an antibody-conjugated nanoparticle that recognizes the biomarker of interest on these exosomes, which can be either a general exosome marker or a factor specific for an exosome subtype of interest. Images of these sample wells are then captured using a dark field microscope (DFM) and analyzed to measure the light scattered from nanoparticles bound to exosomes of interest captured in each sample well6,16,17. Notably, imaging an entire sample well by low-magnification DFM (LMDFM) avoids a selection bias encountered with high magnification DFM analyses when users must directly choose which fields to capture for subsequent image analysis. LMDFM image analysis is subject to light scattering artifacts from surface irregularities, including scratches and sample debris, but this background can be reduced using a simple noise-reduction algorithm we developed to run on the NIH image analysis program, ImageJ (https://imagej.nih.gov/ij/). This algorithm first applies an input contour threshold that is used to detect the boundaries of the sample well to define the region of the image for subsequent analysis. The region defined by this contour region is then split to separate signal present in the red, blue and green channels of the image and the blue channel is subtracted from the red channel to remove signal arising from surface artifacts and uneven lighting from nanorod signal.
This article describes how to use this assay to rapidly quantify either total or specific exosome levels in plasma or serum samples.

<table border="0" cellpadding="0" cellspacing="0" style="width:836px;" width="836">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:311px;">Eppendorf Repeater stream</td>
			<td style="width:120px;">Fisher Scientific</td>
			<td style="width:187px;">05-401-040</td>
			<td style="width:219px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:311px;">Eppendorf Research plus</td>
			<td style="width:120px;">Eppendorf</td>
			<td style="width:187px;">3120000011</td>
			<td>0.1 &#8211; 2.5 &#181;L, dark gray</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:311px;">Functionalized Gold Nanorods</td>
			<td style="width:120px;">Nanopartz</td>
			<td style="width:187px;">C12-25-650-TN-DIH-50-1</td>
			<td style="width:219px;">In vitro neutravidin polymer functionalization</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:311px;">HulaMixer Sample Mixer</td>
			<td style="width:120px;">Thermo Fisher Scientific</td>
			<td style="width:187px;">15920D</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:311px;">Incu-shaker 10L</td>
			<td style="width:120px;">Benchmark Scientific</td>
			<td style="width:187px;">H1010</td>
			<td></td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:311px;">Inverted Research Microscope</td>
			<td style="width:120px;">Nikon</td>
			<td style="width:187px;">Ti-DH</td>
			<td style="width:219px;">With Dark field condenser, DS-Ri2 camera, and Ti-SH-U universal holder, and motorized stage</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:311px;">NIS-Elements</td>
			<td style="width:120px;">Nikon</td>
			<td style="width:187px;"></td>
			<td>Microscope imaging software</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:311px;">Phosphate Buffered Saline (1X)</td>
			<td style="width:120px;">GE Healthcare Life Sciences</td>
			<td style="width:187px;">SH30256.02</td>
			<td>HyClone</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:311px;">Protein A/G Treated Glass Substrate Slides</td>
			<td style="width:120px;">Arrayit Corp.</td>
			<td style="width:187px;">AGMSM192BC</td>
			<td style="width:219px;">Premium microarray substrate</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:311px;">Q500 Sonicator</td>
			<td style="width:120px;">Qsonica, LLC</td>
			<td style="width:187px;">Q500-110</td>
			<td>With standard probe (#4220)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:311px;">Superblock blocking buffer</td>
			<td style="width:120px;">Thermo Scientific</td>
			<td style="width:187px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:311px;">TWEEN 20</td>
			<td style="width:120px;">Sigma Life Sciences</td>
			<td style="width:187px;">9005-64-5</td>
			<td></td>
		</tr>
	</tbody>
</table>

using nanoplasmon-enhanced scattering and low-magnification microscope imaging to quantify tumor-derived exosomes

Infected or malignant cells frequently secrete more exosomes, leading to elevated levels of disease-associated exosomes in the circulation. These exosomes have the potential to serve as biomarkers for disease diagnosis and to monitor disease progression and treatment response. However, most exosome analysis procedures require exosome isolation and purification steps, which are usually time-consuming and labor-intensive, and thus of limited utility in clinical settings. This report describes a rapid procedure to analyze specific biomarkers on the outer membrane of exosomes without requiring separate isolation and purification steps. In this method, exosomes are captured on the surface of a slide by exosome-specific antibodies and then hybridized with nanoparticle-conjugated antibody probes specific to a disease. After hybridization, the abundance of the target exosome population is determined by analyzing low-magnification dark-field microscope (LMDFM) images of the bound nanoparticles. This approach can be easily adopted for research and clinical use to analyze membrane-associated exosome biomarkers linked to disease.

Multi-well protein A/G coated slides (Figure 1A) were functionalized with anti-EphA2 antibody and then used to specifically capture EphA2-positive exosomes from serum samples of patients with and without pancreatic cancer (1 &#181;L/well) and incubated with gold nanorods conjugated with an anti-CD9 antibody (Figure 1B). DFM images of light scattered from these nanoparticles were analyzed using the DSM plugin in ImageJ software to quantify the bound EphA-2 positive exosomes in each well. The DSM algorithm automatically defines the boundary of a sample well, filters the noise from artifacts, calculates the scattered signal from each well, and outputs this information (Figure 2). The DSM algorithm strongly attenuates light scattering artifacts from scratches or debris present in the sample and improves the sensitivity and reproducibility of nanoparticle detection and can automatically process a batch of slide images for high-throughput use. This algorithm uses ImageJ commands and parameters input by the user to subtract image background, calculate the scattering signal from each well, and output a data and image file (Figure 3). Regions of interest are defined by the high intensity well boundaries in the capture image using the contour threshold function of the ImageJ macro program. Image analyses use a prede&#64257;ned contour threshold and image parameters to calculate the intensity of nanoparticle scattering for each well.
As reported in our previous work (supplementary information of Liang et al.6), exosomes isolated from PANC-1 cell cultures characterized by transmission electron microscopy and Western blot exhibited the size range, morphology, and protein marker expression consistent with a high purity exosome sample. The PANC-1 exosomes, prepared with the same procedure as our previous work, were used here to validate our nPES assay for exosome quantification. This assay used an anti-EphA2 antibody to capture a large population of exosomes from the total exosome population and an antibody against the general exosome protein CD9 to detect captured exosomes. Results obtained using serially diluted PANC-1 exosome samples, with protein concentrations ranging from 0.24 to 1.2 &#181;g/&#181;L, showed good reproducibility in replicate wells (Figure 4A) and a strong linear correlation between the scatter response and exosome protein concentration (Figure 4B).
To demonstrate the potential application of this method, serum samples from patients with and without pancreatic cancer were analyzed to detect the abundance of serum exosomes expressing the cancer-associated biomarker EphA2, using an anti-EphA2 antibody to directly capture target exosomes from serum and nanoparticles conjugated with an anti-CD9 antibody to detect bound exosomes. This analysis revealed that serum samples from the pancreatic cancer patients had signi&#64257;cantly higher levels of EphA2+ exosomes (Figure 5) than their controls.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59177/59177fig1.jpg" /> 
Figure 1: Exosome quantification scheme. (A) Schematic of multi-well protein A/G slides (192 wells) used in this assay. (B) Target exosomes are directly captured from samples, including serum and plasma, by surface immobilization on the capture antibody (e.g. anti-EphA2 antibody) bound to the slide, and then incubated with gold nanorods conjugated with a detection antibody (e.g. anti-CD9 antibody) before analysis by DFM image analysis. <a href="https://www.jove.com/files/ftp_upload/59177/59177fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59177/59177fig2.jpg" /> 
Figure 2: Exosome quantification by applying the DSM algorithm to LMDFM images. Low-magnification images are processed with the DSM algorithm to eliminate background signal and signal artifacts that can arise from scratches, mixing voids, debris, and uneven sample illumination to allow robust detection of gold nanorod signal, which correlates with exosome concentration. This figure has been adapted with permission from Sun, D. et al. Noise Reduction Method for Quantifying Nanoparticle Light Scattering in Low Magnification Dark-Field Microscope Far-Field Images. Analytical Chemistry. 88 (24), 12001-12005 (2016). Copyright (2016) American Chemical Society. <a href="https://www.jove.com/files/ftp_upload/59177/59177fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59177/59177fig3.jpg" /> 
Figure 3: Schematic of the DSM algorithm commands and outputs. Indicated steps use native commands from ImageJ and all input parameters are selected according to the requirements of experiments via graphical user interface. This figure has been adapted with permission from Sun, D. et al. Noise Reduction Method for Quantifying Nanoparticle Light Scattering in Low Magnification Dark-Field Microscope Far-Field Images. Analytical Chemistry. 88 (24), 12001-12005 (2016). Copyright (2016) American Chemical Society. <a href="https://www.jove.com/files/ftp_upload/59177/59177fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59177/59177fig4.jpg" /> 
Figure 4: Representative nPES LMDFM images and DSM output data. (A) LMDFM image of an nPES assay analyzing a concentration gradient of PANC-1 exosomes and (B) the linear correlation of the optical signal and exosome concentration from this slide (left to right: 0.24, 0.356, 0.53, 0.80, 1.20 &#181;g/&#181;L respectively for each column). Data are presented as Mean&#177;SE, n=6, with a Pearson correlation coefficient R2 = 0.99 and coefficient of variation for the replicates of each concentration &#60; 0.2. <a href="https://www.jove.com/files/ftp_upload/59177/59177fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59177/59177fig5.jpg" /> 
Figure 5: LMDFM signal of EphA2+ exosomes differs in serum from patients with and without pancreatic cancer. Serum samples analyzed by nPES using an anti-EphA2 capture antibody (cancer-associated) and an anti-CD9 detection antibody (general exosome marker) exhibited a significant difference in the concentration of EphA2+ exosomes in serum samples of patients with and without pancreatic cancer (N = 7/group). Results are presented as Mean&#177;SE. **p = 0.002 by Mann-Whitney U-test (two-sided). This figure has been modified from [Sun, D. et al. Noise Reduction Method for Quantifying Nanoparticle Light Scattering in Low Magnification Dark-Field Microscope Far-Field Images. Analytical Chemistry. 88 (24), 12001-12005 (2016)]. <a href="https://www.jove.com/files/ftp_upload/59177/59177fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes at JoVE.com

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

Clinical translation of exosome-derived biomarkers for diseased and malignant cells is hindered by the lack of rapid and accurate quantification methods. This report describes the use of low-magnification dark-field microscope images to quantify specific exosome subtypes in small volume serum or plasma samples.

Infected or malignant cells frequently secrete more exosomes, leading to elevated levels of disease-associated exosomes in the circulation. These exosomes ...

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7.2K Views.  West China Hospital of Sichuan University. Clinical translation of exosome-derived biomarkers for diseased and malignant cells is hindered by the lack of rapid and accurate quantification methods. This report describes the use of low-magnification dark-field microscope images to quantify specific exosome subtypes in small volume serum or plasma samples.

Video: Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

The aortic valve, located between the left ventricle and the aorta, allows for unidirectional blood flow, preventing backflow into the ventricle. Aortic valve leaflets are composed of interstitial cells suspended within an extracellular matrix (ECM) and are lined with an endothelial cell monolayer. The valve withstands a harsh, dynamic environment and is constantly exposed to shear, flexion, tension, and compression. Research has shown calcific lesions in diseased valves occur in areas of high mechanical stress as a result of endothelial disruption or interstitial matrix damage1-3. Hence, it is not surprising that epidemiological studies have shown high blood pressure to be a leading risk factor in the onset of aortic valve disease4. 
The only treatment option currently available for valve disease is surgical replacement of the diseased valve with a bioprosthetic or mechanical valve5. Improved understanding of valve biology in response to physical stresses would help elucidate the mechanisms of valve pathogenesis. In turn, this could help in the development of non-invasive therapies such as pharmaceutical intervention or prevention. Several bioreactors have been previously developed to study the mechanobiology of native or engineered heart valves6-9. Pulsatile bioreactors have also been developed to study a range of tissues including cartilage10, bone11 and bladder12. The aim of this work was to develop a cyclic pressure system that could be used to elucidate the biological response of aortic valve leaflets to increased pressure loads. 
The system consisted of an acrylic chamber in which to place samples and produce cyclic pressure, viton diaphragm solenoid valves to control the timing of the pressure cycle, and a computer to control electrical devices. The pressure was monitored using a pressure transducer, and the signal was conditioned using a load cell conditioner. A LabVIEW program regulated the pressure using an analog device to pump compressed air into the system at the appropriate rate. The system mimicked the dynamic transvalvular pressure levels associated with the aortic valve; a saw tooth wave produced a gradual increase in pressure, typical of the transvalvular pressure gradient that is present across the valve during diastole, followed by a sharp pressure drop depicting valve opening in systole. The LabVIEW program allowed users to control the magnitude and frequency of cyclic pressure. The system was able to subject tissue samples to physiological and pathological pressure conditions. This device can be used to increase our understanding of how heart valves respond to changes in the local mechanical environment.

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

A cyclic pressure bioreactor capable of subjecting heart valve tissue to physiological and pathological pressure conditions has been designed. A LabVIEW program allows users to control pressure magnitude, amplitude and frequency. This device can be used to study the mechanobiology of heart valve tissue or isolated cells.

The aortic valve, located between the left ventricle and the aorta, allows for unidirectional blood flow, preventing backflow into the ventricle. Aortic ...

From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the regulation of many cellular functions. However, due to the fast dynamics and the tiny structures involved, a very high spatio-temporal resolution is required to catch the real behavior of molecules. Here we present the experimental protocol for studying the dynamics of fluorescently-labeled plasma-membrane proteins and lipids in live cells with high spatiotemporal resolution. Notably, this approach doesn&#8217;t need to track each molecule, but it calculates population behavior using all molecules in a given region of the membrane. The starting point is a fast imaging of a given region on the membrane. Afterwards, a complete spatio-temporal autocorrelation function is calculated correlating acquired images at increasing time delays, for example each 2, 3, n repetitions. It is possible to demonstrate that the width of the peak of the spatial autocorrelation function increases at increasing time delay as a function of particle movement due to diffusion. Therefore, fitting of the series of autocorrelation functions enables to extract the actual protein mean square displacement from imaging (iMSD), here presented in the form of apparent diffusivity vs average displacement. This yields a quantitative view of the average dynamics of single molecules with nanometer accuracy. By using a GFP-tagged variant of the Transferrin Receptor (TfR) and an ATTO488 labeled 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (PPE) it is possible to observe the spatiotemporal regulation of protein and lipid diffusion on &#181;m-sized membrane regions in the micro-to-milli-second time range.

Spatial distribution and temporal dynamics of plasma membrane proteins and lipids is a hot topic in biology. Here this issue is addressed by a spatio-temporal image fluctuation analysis that provides conceptually the same physical quantities of single particle tracking, but it uses small molecular labels and standard microscopy setups.

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the ...

High Speed Sub-GHz Spectrometer for Brillouin Scattering Analysis

The goal of this protocol is to build a parallel high-extinction and high-resolution optical Brillouin spectrometer. Brillouin spectroscopy is a non-contact measurement method that can be used to obtain direct readouts of viscoelastic material properties. It has been a useful tool in material characterization, structural monitoring and environmental sensing. In the past, Brillouin spectroscopy has usually employed scanning Fabry-Perot etalons to perform spectral analysis. This process requires high illumination power and long acquisition times, making the technique unsuitable for biomedical applications. A recently introduced novel spectrometer overcomes this challenge by employing two VIPAs in a cross-axis configuration. This innovation enables sub-Gigahertz (GHz) resolution spectral analysis with sub-second acquisition time and illumination power within the safety limits of biological tissue. The multiple new applications facilitated by this improvement are currently being explored in biological research and clinical application.

Here we present a protocol to build a rapid Brillouin spectrometer. Cascading virtually imaged phase array (VIPA) etalons achieve a measurement speed more than 1,000 times faster than traditional scanning Fabry-Perot spectrometers. This improvement provides the means for Brillouin analysis of tissue and biomaterials at low power levels in vivo.

The goal of this protocol is to build a parallel high-extinction and high-resolution optical Brillouin spectrometer. Brillouin spectroscopy is a ...

A Rapid and Chemical-free Hemoglobin Assay with Photothermal Angular Light Scattering

Photo-thermal angular light scattering (PT-AS) is a novel optical method for measuring the hemoglobin concentration ([Hb]) of blood samples. On the basis of the intrinsic photothermal response of hemoglobin molecules, the sensor enables high-sensitivity, chemical-free measurement of [Hb]. [Hb] detection capability with a limit of 0.12 g/dl over the range of 0.35 - 17.9 g/dl has been demonstrated previously. The method can be readily implemented using inexpensive consumer electronic devices such as a laser pointer and a webcam. The use of a micro-capillary tube as a blood container also enables the hemoglobin assay with a nanoliter-scale blood volume and a low operating cost. Here, detailed instructions for the PT-AS optical setup and signal processing procedures are presented. Experimental protocols and representative results for blood samples in anemic conditions ([Hb] = 5.3, 7.5, and 9.9 g/dl) are also provided, and the measurements are compared with those from a hematology analyzer. Its simplicity in implementation and operation should enable its wide adoption in clinical laboratories and resource-limited settings.

A photo-thermal angular light scattering (PT-AS) sensor enables the rapid and chemical-free hemoglobin assay of nanoliter-scale blood samples. Here, details of the PT-AS setup and a measurement protocol for the hemoglobin concentration in blood are provided. Representative results for anemic blood samples are also presented.

Photo-thermal angular light scattering (PT-AS) is a novel optical method for measuring the hemoglobin concentration ([Hb]) of blood samples. On the basis ...

Using Extraordinary Optical Transmission to Quantify Cardiac Biomarkers in Human Serum

For a biosensing platform to have clinical relevance in point-of-care (POC) settings, assay sensitivity, reproducibility, and ability to reliably monitor analytes against the background of human serum are crucial.
Nanoimprinting lithography (NIL) was used to fabricate, at a low cost, sensing areas as large as 1.5 mm x 1.5 mm. The sensing surface was made of high-fidelity arrays of nanoholes, each with an area of about 140 nm2. The great reproducibility of NIL made it possible to employ a one-chip, one-measurement strategy on 12 individually manufactured surfaces, with minimal chip-to-chip variation. These nanoimprinted localized surface plasmon resonance (LSPR) chips were extensively tested on their ability to reliably measure a bioanalyte at concentrations varying from 2.5 to 75 ng/mL amidst the background of a complex biofluid-in this case, human serum. The high fidelity of NIL enables the generation of large sensing areas, which in turn eliminates the need for a microscope, as this biosensor can be easily interfaced with a commonly available laboratory light source. These biosensors can detect cardiac troponin in serum with a high sensitivity, at a limit of detection (LOD) of 0.55 ng/mL, which is clinically relevant. They also show low chip-to-chip variance (due to the high quality of the fabrication process). The results are commensurable with widely used enzyme-linked immunosorbent assay (ELISA)-based assays, but the technique retains the advantages of an LSPR-based sensing platform (i.e., amenability to miniaturization and multiplexing, making it more feasible for POC applications).

This work describes a nanoimprinting lithography method to fabricate high-quality sensing arrays that work on the principle of extraordinary optical transmission. The biosensor is low-cost, robust, easy to use, and can detect cardiac troponin I in serum at clinically relevant concentrations (99th percentile cutoff &#8764;10-400 pg/mL, depending on the assay).

For a biosensing platform to have clinical relevance in point-of-care (POC) settings, assay sensitivity, reproducibility, and ability to reliably monitor ...

Fabrication and Validation of an Organ-on-chip System with Integrated Electrodes to Directly Quantify Transendothelial Electrical Resistance

Organs-on-chips, in vitro models involving the culture of (human) tissues inside microfluidic devices, are rapidly emerging and promise to provide useful research tools for studying human health and disease. To characterize the barrier function of cell layers cultured inside organ-on-chip devices, often transendothelial or transepithelial electrical resistance (TEER) is measured. To this end, electrodes are usually integrated into the chip by micromachining methods to provide more stable measurements than is achieved with manual insertion of electrodes into the inlets of the chip. However, these electrodes frequently hamper visual inspection of the studied cell layer or require expensive cleanroom processes for fabrication. To overcome these limitations, the device described here contains four easily integrated electrodes that are placed and fixed outside of the culture area, making visual inspection possible. Using these four electrodes the resistance of six measurement paths can be quantified, from which the TEER can be directly isolated, independent of the resistance of culture medium-filled microchannels. The blood-brain barrier was replicated in this device and its TEER was monitored to show the device applicability. This chip, the integrated electrodes and the TEER determination method are generally applicable in organs-on-chips, both to mimic other organs or to be incorporated into existing organ-on-chip systems.

This publication describes the fabrication of an organ-on-chip device with integrated electrodes for direct quantification of transendothelial electrical resistance (TEER). For validation, the blood-brain barrier was mimicked inside this microfluidic device and its barrier function was monitored. The presented methods for electrode integration and direct TEER quantification are generally applicable.

Organs-on-chips, in vitro models involving the culture of (human) tissues inside microfluidic devices, are rapidly emerging and promise to provide useful ...

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Deep brain stimulation (DBS), which involves insertion of an electrode to deliver stimulation to a localized brain region, is an established therapy for movement disorders and is being applied to a growing number of disorders. Computational modeling has been successfully used to predict the clinical effects of DBS; however, there is a need for novel modeling techniques to keep pace with the growing complexity of DBS devices. These models also need to generate predictions quickly and accurately. The goal of this project is to develop an image processing pipeline to incorporate structural magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) into an interactive, patient specific model to simulate the effects of DBS. A virtual DBS lead can be placed inside of the patient model, along with active contacts and stimulation settings, where changes in lead position or orientation generate a new finite element mesh and solution of the bioelectric field problem in near real-time, a timespan of approximately 10 seconds. This system also enables the simulation of multiple leads in close proximity to allow for current steering by varying anodes and cathodes on different leads. The techniques presented in this paper reduce the burden of generating and using computational models while providing meaningful feedback about the effects of electrode position, electrode design, and stimulation configurations to researchers or clinicians who may not be modeling experts.

The goal of this project is to develop an interactive, patient-specific modeling pipeline to simulate the effects of deep brain stimulation in near real-time and provide meaningful feedback as to how these devices influence neural activity in the brain.

Deep brain stimulation (DBS), which involves insertion of an electrode to deliver stimulation to a localized brain region, is an established therapy for ...

Stiffness Measurement of Soft Silicone Substrates for Mechanobiology Studies Using a Widefield Fluorescence Microscope

Soft tissues in the human body typically have stiffness in the kilopascal (kPa) range. Accordingly, silicone and hydrogel flexible substrates have been proven to be useful substrates for culturing cells in a physical microenvironment that partially mimics in vivo conditions. Here, we present a simple protocol for characterizing the Young's moduli of isotropic linear elastic substrates typically used for mechanobiology studies. The protocol consists of preparing a soft silicone substrate on a Petri dish or stiff silicone, coating the top surface of the silicone substrate with fluorescent beads, using a millimeter-scale sphere to indent the top surface (by gravity), imaging the fluorescent beads on the indented silicone surface using a fluorescence microscope, and analyzing the resultant images to calculate the Young's modulus of the silicone substrate. Coupling the substrate's top surface with a moduli extracellular matrix protein (in addition to the fluorescent beads) allows the silicone substrate to be readily used for cell plating and subsequent studies using traction force microscopy experiments. The use of stiff silicone, instead of a Petri dish, as the base of the soft silicone, enables the use of mechanobiology studies involving external stretch. A specific advantage of this protocol is that a widefield fluorescence microscope, which is commonly available in many labs, is the major equipment necessary for this procedure. We demonstrate this protocol by measuring the Young's modulus of soft silicone substrates of different elastic moduli.

Substrates with stiffness in the kilopascal-range are useful to study the response of cells to physiologically relevant micro-environment stiffness. Using just a widefield fluorescence microscope, the Young's modulus of soft silicone gels can be determined using an indentation with a suitable sphere.

Soft tissues in the human body typically have stiffness in the kilopascal (kPa) range. Accordingly, silicone and hydrogel flexible substrates have been ...

Conducting Multiple Imaging Modes with One Fluorescence Microscope

Fluorescence microscopy is a powerful tool to detect biological molecules in situ and monitor their dynamics and interactions in real-time. In addition to conventional epi-fluorescence microscopy, various imaging techniques have been developed to achieve specific experimental goals. Some of the widely used techniques include single-molecule fluorescence resonance energy transfer (smFRET), which can report conformational changes and molecular interactions with angstrom resolution, and single-molecule detection-based super-resolution (SR) imaging, which can enhance the spatial resolution approximately ten&#160;to twentyfold compared to diffraction-limited microscopy. Here we present a customer-designed integrated system, which merges multiple imaging methods in one microscope, including conventional epi-fluorescent imaging, single-molecule detection-based SR imaging, and multi-color single-molecule detection, including smFRET imaging. Different imaging methods can be achieved easily and reproducibly by switching optical elements. This set-up is easy to adopt by any research laboratory in biological sciences with a need for routine and diverse imaging experiments at a reduced cost and space relative to building separate microscopes for individual purposes.

Here we present a practical guide of building an integrated microscopy system, which merges conventional epi-fluorescent imaging, single-molecule detection-based super-resolution imaging, and multi-color single-molecule detection, including single-molecule fluorescence resonance energy transfer imaging, into one set-up in a cost-efficient way.

Fluorescence microscopy is a powerful tool to detect biological molecules in situ and monitor their dynamics and interactions in real-time. In addition to ...

A Guide to Build a Highly Inclined Swept Tile Microscope for Extended Field-of-view Single-molecule Imaging

Single-molecule imaging has greatly advanced our understanding of molecular mechanisms in biological studies. However, it has been challenging to obtain large field-of-view, high-contrast images in thick cells and tissues. Here, we introduce highly inclined swept tile (HIST) microscopy that overcomes this problem. A pair of cylindrical lenses was implemented to generate an elongated excitation beam that was scanned over a large imaging area via a fast galvo mirror. A 4f configuration was used to position optical components. A scientific complementary metal-oxide semiconductor camera detected the fluorescence signal and blocked the out-of-focus background with a dynamic confocal slit synchronized with the beam sweeping. We present a step-by-step instruction on building the HIST microscope with all basic components.

A detailed instruction is described on how to build a highly inclined swept tile (HIST) microscope and its usage for single-molecule imaging.

Single-molecule imaging has greatly advanced our understanding of molecular mechanisms in biological studies. However, it has been challenging to obtain ...