2, University at Albany, SUNY, Albany, New York, United States
We have developed a universal nanosensor array for biosystems analyses. The array is composed of non-specific nanoreceptors which were assembled using three types of two-dimensional (2D) nanoparticles; nGO, MoS2, or WS2; and various fluorescently labeled single stranded DNA (ssDNA) molecules. The array was first employed for the identification of three radically different biosystems; five proteins, three types of live breast cancer cells and a structure-switching event of a macromolecule. The data matrices for each system were processed using Partial Least Squares (PLS) discriminant analysis. In all of the systems, the sensor array was able to identify each entity as separate clusters with 95% confidence and without any overlap. Out of 15 unknown entities with unknown protein concentrations tested, 14 of them were predicted successfully with correct concentration. 8 breast cancer cell samples out of 9 unknown entities from three cell types were predicted correctly. Later the nanosensor array was used for the discrimination of nine miRNA analogs which belong to the same miRNA family. All nine targets are highly similar in sequence, differing by only a single or few nucleobases. Both the identity and concentration of the unknown targets were determined using double-blind tests with this combinatorial approach. Unlike the typical lock-and-key sensing strategy, which relies on the most dominant interactions between the probe and target, our sensor array takes every single; minor or major; non-specific interaction into account. Therefore, this approach is bias-free, in which the background or possible false-positive signals are already included in the large data set. The analysis of the comprehensive big data set enables simultaneous and precise identification of every single non-specific entity tested with our sensor array. Multiple compounds can be analyzed simultaneously with a single step and separation-free procedure. Though we have studied only four groups of biosystems, this approach is universal and can be applied to a wide-range of biological and environmental systems analyses.