DNA-Binding Magnetic Nanoreactor Beads for Digital PCR Analysis.

Digital PCR (dPCR) is based on the separation of target amplification reactions into many compartments with randomly distributed template molecules. Here, we present a novel digital PCR format based on DNA binding magnetic nanoreactor beads (mNRBs). Our approach relies on the binding of all nucleic acids present in a sample to the mNRBs, which both provide a high-capacity binding matrix for capturing nucleic acids from a sample and define the space available for PCR amplification by the internal volume of their hydrogel core. Unlike conventional dPCR, this approach does not require a precise determination of the volume of the compartments used but only their number to calculate the number of amplified targets. We present a procedure in which genomic DNA is bound, the nanoreactors are loaded with PCR reagents in an aqueous medium, and amplification and detection are performed in the space provided by the nanoreactor suspended in fluorocarbon oil. mNRBs exhibit a high DNA binding capacity of 1.1 ng DNA/mNRB (95% CI 1.0-1.2) and fast binding kinetics with ka = 0.21 s-1 (95% CI 0.20-0.23). The dissociation constant KD was determined to be 0.0011 µg/µL (95% CI 0.0007-0.0015). A simple disposable chamber plate is used to accommodate the nanoreactor beads in a monolayer formation for rapid thermocycling and fluorescence detection. The performance of the new method was compared with conventional digital droplet PCR and found to be equivalent in terms of the precision and linearity of quantification. In addition, we demonstrated that mNRBs provide quantitative capture and loss-free analysis of nucleic acids contained in samples in different volumes.

Ultra-precise quantification of mRNA targets across a broad dynamic range with nanoreactor beads.

Precise quantification of molecular targets in a biological sample across a wide dynamic range is a key requirement in many diagnostic procedures, such as monitoring response to therapy or detection of measurable residual disease. State of the art digital PCR assays provide for a dynamic range of four orders of magnitude. However digital assays are complex and require sophisticated microfluidic tools. Here we present an assay format that enables ultra-precise quantification of RNA targets in a single measurement across a dynamic range of more than six orders of magnitude. The approach is based on hydrogel beads that provide for microfluidic free compartmentalization of the sample as they are used as nanoreactors for reverse transcription, PCR amplification and combined real time and digital detection of gene transcripts. We have applied these nanoreactor beads for establishing an assay for the detection and quantification of BCR-ABL1 fusion transcripts. The assay has been characterized for its precision and linear dynamic range. A comparison of the new method against conventional real time RT-PCR analysis (reference method) with clinical samples from patients with chronic myeloid leukemia (CML) revealed excellent concordance with Pearsons correlation coefficient of 0.983 and slope of 1.08.

Gene regulatory network inference: data integration in dynamic models-a review.

Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.