A Branched SELEX Approach Identifies RNA Aptamers That Bind Distinct HIV-1 Capsid Structural Components.

The HIV-1 capsid protein (CA) assumes distinct structural forms during replication, each presenting unique, solvent-accessible surfaces that facilitate multifaceted functions and host factor interactions. However, functional contributions of individual CA structures remain unclear, as evaluation of CA presents several technical challenges. To address this knowledge gap, we identified CA-targeting aptamers with different structural specificities, which emerged through a branched SELEX approach using an aptamer library previously selected to bind the CA hexamer lattice. Subsets were either highly specific for the CA lattice or bound both the CA lattice and CA hexamer. We then evaluated four representatives to reveal aptamer regions required for binding, highlighting interesting structural features and challenges in aptamer structure determination. Further, we demonstrate binding to biologically relevant CA structural forms and aptamer-mediated affinity purification of CA from cell lysates without virus or host modification, supporting the development of structural form-specific aptamers as exciting new tools for the study of CA.

Differentiation SELEX approach identifies RNA aptamers with different specificities for HIV-1 capsid assembly forms.

The HIV-1 capsid protein (CA) assumes distinct assembly forms during replication, each presenting unique, solvent-accessible surfaces that facilitate multifaceted functions and host factor interactions. However, contributions of individual CA assemblies remain unclear, as the evaluation of CA in cells presents several technical challenges. To address this need, we sought to identify CA assembly form-specific aptamers. Aptamer subsets with different specificities emerged from within a highly converged, pre-enriched aptamer library previously selected to bind the CA hexamer lattice. Subsets were either highly specific for CA lattice or bound both CA lattice and CA hexamer. We further evaluated four representatives to reveal aptamer structural features required for binding, highlighting interesting features and challenges in aptamer structure determination. Importantly, our aptamers bind biologically relevant forms of CA and we demonstrate aptamer-mediated affinity purification of CA from cell lysates without virus or host modification. Thus, we have identified CA assembly form-specific aptamers that represent exciting new tools for the study of CA.

Effects of redox label location on the performance of an electrochemical aptamer-based tumor necrosis factor-alpha sensor.

We report the development of an electrochemical aptamer-based sensor for real time detection of tumor necrosis factor-alpha. The focus of this study is to evaluate the effects of the redox label location on the overall sensor performance, including sensor stability, detection limit, reusability, and selectivity. Three aptamer probes, each labeled with methylene blue (MB) at a specific location, were designed and employed in the fabrication of the sensors. Among the three sensors, the sensor fabricated using an aptamer with the MB label located at the distal end has a detection limit of 100 pM and is regenerable. The sensor fabricated using an aptamer with an internal MB modification has a detection limit of 10 nM and is not regenerable. Both sensors can be employed in complex biological samples such as 50% urine and 50% saliva. However, the sensor fabricated with an aptamer with the MB label located at the proximal end suffers from poor reproducibility and is highly unstable, thus limiting its application as a sensor. On the bases of these results, placing the MB label at the distal end of the aptamer probe appears to be the most advantageous for this sensor design for it does not interfere with monolayer formation and target binding.

Printed Colorimetric Arrays for the Identification and Quantification of Acids and Bases.

Solid supported colorimetric sensing arrays have the advantage of portability and ease of use when deployed in the field, such as crime scenes, disaster zones, or in war zones, but many sensor arrays require complex fabrication methods. Here, we report a practical method for the fabrication of 4 × 4 colorimetric sensor arrays, which are printed on nylon membranes, using a commercially available inkjet printer. In order to test the efficacy of the printed arrays, they were exposed to 43 analytes at concentrations ranging from 0.001 to 3.0 M for a total of 559 samples of inorganic and organic acids or bases including hydrochloric, acetic, phthalic, malonic, picric, and trifluoroacetic acid, ammonium hydroxide, sodium hydroxide, lysine, and water as the control. Colorimetric data from the imaged arrays was analyzed with linear discriminant analysis and k-nearest neighbors to determine the analyte and concentration with ∼88-90% accuracy. Overall, the arrays have impressive analytical power to identify a variety of analytes at different concentrations while being simple to fabricate.

An Improved Comparison of Chemometric Analyses for the Identification of Acids and Bases With Colorimetric Sensor Arrays.

Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods were compared for the task of classifying 631 solutions (0.1 to 3 M) of acetic acid, malonic acid, lysine, and ammonia using an eight sensor colorimetric array. PCA and LDA (linear discriminant analysis) were effective for visualizing the dataset. For classification, linear discriminant analysis (LDA), (k nearest neighbors) KNN, (soft independent modelling by class analogy) SIMCA, recursive partitioning and regression trees (RPART), and hit quality index (HQI) were very effective with each method classifying compounds with over 90% correct assignments. Support vector machines (SVM) and partial least squares - discriminant analysis (PLS-DA) struggled with ~85 and 39% correct assignments, respectively. Additional mathematical treatments of the data set, such as incrementally increasing the exponents, did not improve the performance of LDA and KNN. The literature precedence indicates that the most common methods for analyzing colorimetric arrays are PCA, LDA, HCA, and KNN. To our knowledge, this is the first report of comparing and contrasting several more diverse chemometric methods to analyze the same colorimetric array data.