Coffee Ring Aptasensor for Rapid Protein Detection

We introduce a new biosensing platform for rapid protein detection that combines one of the simplest methods for biomolecular concentration, coffee ring formation, with a sensitive aptamer-based optical detection scheme. In this approach, aptamer beacons are utilized for signal transduction where a fluorescence signal is emitted in the presence of the target molecule. Signal amplification is achieved by concentrating aptamer-target complexes within liquid droplets, resulting in the formation of coffee ring “spots”. Surfaces with various chemical coatings were utilized to investigate the correlation among surface hydrophobicity, concentration efficiency, and signal amplification. On the basis of our results, we found that the increase in the coffee ring diameter with larger droplet volumes is independent of surface hydrophobicity. Furthermore, we show that highly hydrophobic surfaces produce enhanced particle concentration via coffee ring formation, resulting in signal intensities 6-fold greater than those on hydrophilic surfaces. To validate this biosensing platform for the detection of clinical samples, we detected α-thrombin in human serum and 4-fold-diluted whole blood. Coffee ring spots from serum and blood produced detection signals up to 40 times larger than those from samples in liquid droplets. Additionally, this biosensor exhibits a lower limit of detection of 2 ng/mL (54 pM) in serum, and 4 ng/mL (105 pM) in blood. On the basis of its simplicity and high performance, this platform demonstrates immense potential as an inexpensive diagnostic tool for the detection of disease biomarkers, particularly for use in developing countries that lack the resources and facilities required for conventional biodetection practices

Boletín de Segovia: Número 44 - 1861 abril 10

Copia digital. Madrid : Ministerio de Cultura. Subdirección General de Coordinación Bibliotecaria, 200

\uabNozze\ubb napoletane. \uabLa serva onorata\ubb di Giambattista Lorenzi e Nicol\uf2 Piccinni (1792)

Nel 1792 il teatro napoletano dei Fiorentini ospit\uf2 la prima rappresentazione di una nuova opera comica di Giambattista Lorenzi e Niccol\uf2 Piccinni: \uabLa serva onorata\ubb. Il libretto era in realt\ue0 una libera rielaborazione delle \uabNozze di Figaro\ubb di Da Ponte per Mozart. Il saggio avanza alcune ipotesi circa l'arrivo del testo nella capitale meridionale e procede poi a un dettagliato esame delle manipolazioni subite dal modello viennese, che risulta omologato alle convenzioni estetiche e produttive locali. Particolare attenzione viene riservata al ruolo degli interpreti napoletani, che con le proprie caratteristiche condizionano vistosamente il processo di adattamento. In chiusura vengono proposte alcune riflessioni sui (presunti) significati politici dei testi di Beaumarchais e Da Ponte alla luce della ricezione napoletana. La trascrizione integrale della versione piccinniana di \uabPorgi, Amor, qualche ristoro\ubb viene offerta in appendice

Single-drug effects of KSHV reactivation and related cellular signaling.

<p>(A) Shown are the five drugs that are used in the drug combinations and the mechanisms by which they induce KSHV reactivation. The diagram also illustrates the known crosstalk among these five drugs. : Synergistic effect; : Inhibitory effect. Representative known interactions among different molecules: a. Proteasome inhibitor prevents the activation of NF-kB <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Adams1" target="_blank">[43]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Adams2" target="_blank">[44]</a>. b. PKC activates NF-kB in T and B lymphocytes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Krappmann1" target="_blank">[45]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Williams1" target="_blank">[46]</a>. c. NF-kB inhibits herpesvirus reactivation <i>in vitro</i> and <i>in vivo </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Brown1" target="_blank">[13]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Krug1" target="_blank">[47]</a>. d. Glucocorticoids such as Dexamethasone inhibit NF-kB activity through induction of IkB <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Auphan1" target="_blank">[48]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Ayroldi1" target="_blank">[49]</a>. e. Dexamethasone and cAMP may synergistically regulate the expression of a subset of genes in lymphocytes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Dowd1" target="_blank">[40]</a>. f. PKA pathway and PKC pathway can synergize <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-Rabbi1" target="_blank">[50]</a> or antagonize <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone.0020998-HermannKleiter1" target="_blank">[51]</a> each other in different circumstances. (B) Shown are the KSHV reactivation rates upon treatment with the five drugs individually (Blue: Bortezomib, Red: db-cAMP, Green: Prostratin, Purple: Valproate, Cyan: Dexamethasone). The nine concentrations used are nine two-fold dilutions of the following maximum concentrations for the drugs Bortezomib 320(nM), db-cAMP 8(mM), Prostratin 80(uM), Valproate 6(mM), Dexamethasone 400(nM). The concentrations are also the nine concentrations (Conc. I) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone-0020998-t001" target="_blank">Table 1</a>.</p

Multi-drug response maps of KSHV reactivation.

<p>Figure showing plots of the KSHV reactivation rates as a function of drugs db-cAMP and Prostratin, for various concentrations of drug Bortezomib. The colors are solely a function of the reactivation levels in each panel. (A) Drugs Valproate and Dexamethasone are fixed at zero. (B) Drugs Valproate and Dexamethasone are fixed at 6 mM and 210.5 nM.</p

Evaluation of combinatorial effects of drugs on reactivation and cellular signaling.

<p>(A) Plot of the maximum achievable reactivation rates using combinations of two, three, four, and five drugs as predicted by the mathematical KSHV reactivation model. (B) A summary of the predicted interactions between the applied drugs and their effects of these interactions on KSHV reactivation. : Synergistic effect; : Inhibitory effect.</p

Dimensionality reduction of the predictive reactivation model.

<p>(A) Plot of the regression coefficients of the different regressors used in linear regression. (B) Plot of the percentage of variance explained as a function of the number of Partial Least Squares components used in Partial Least Squares Regression. (C) Plot of the lowest residual sum of squares for models of regressors. (D) Plot of the regressor coefficients of the best model using 10 regressors. The regressors are shown as well.</p

Characterization of the effect of drug combinations on KSHV reactivation.

<p>(A) Distribution of the concentrations of the five drugs in the 50 drug combinations that lead to the highest KSHV reactivation rates simulated by the predictive reactivation model (blue bars). The drug concentration ranges in the optimal drug concentrations generated by the experiment-based cross entropy procedure are shaded in red. The bottom right figure shows a histogram of the reactivation rate of the top performing 50 samples. (B) Representative KSHV reactivation outputs for five-drug combinations. The results of the 1st (top graph) and 12th (middle graph) iterations in the first set of optimization iterations, and the 3rd (bottom graph) iteration in the second set of optimization iterations with smaller concentration ranges are shown. The x-axis represents the different drug combinations used in each iteration; the y-axis shows relative percentage of GFP-positive cells in the total cell population. The highest percentage of GFP-positive cells in individual iterations is set as 1.</p

Predictive modeling of reactivation rates.

<p>(A) Shown is the correlation between the measured reactivation (x-axis) and the predicted reactivation (trained outputs) (y-axis) using 588 out of 600 total input-output points (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#s4" target="_blank">methods</a> section - Neural network model). The circles represent individual data points. The dotted diagonal line represents a perfect fit between the measured and predicted reactivation rates. (B) The measured and predicted reactivation rates of 48 new randomly selected drug combinations. The x-axis shows the measured reactivation rates, and the y-axis showed the predicted reactivation rates using the predictive reactivation model.</p

Table of drug concentrations used in this study.

<p>Conc. I indicates the concentration used for the model-based KSHV reactivation modeling and for the experiment-based optimization. Conc. II indicates the set of refined concentrations used in the second part of the experiment-based optimization. These concentrations were used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone-0020998-g001" target="_blank">Figures 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020998#pone-0020998-g003" target="_blank">3</a>.</p