Objective 1: we made a proof of principle using microscopic beads, which are decorated with DNA strands for 1) identifying the target miRNA by a combination of fluorescent bead-code, 2) capturing miRNA from the samples and 3) report its presence through the emission of a fluorescence signal. Importantly, because the number of beads is higher than the number of miRNAs, each bead captures either 0 or 1 miRNA, this according to a random (Poisson) distribution. The use of magnetic beads allows to wash them, and therefore to discard all components of the samples that can interfere with the subsequent amplification step.
The particles are then isolated in water-in-oil droplets (using a microfluidic chip, 1 bead per drop), together with an amplification mixture composed of DNA strands and enzymes that transform the miRNA target into a fluorescence signal. Therefore, the particles having captured their cognate miRNA would trigger the amplification reaction and in turn activate the reporting DNA strand (resulting in a positive fluorescence signal on the particle). By contrast, the particles that have not captured their target miRNA would stay off (negative particles). All particles are finally analyzed by a flow cytometer, an instrument that measures the fluorescence intensity of individual particles as they pass through a laser beam. For each particle is measured the fluorescence intensity of its bead code (which serves as index the particle to its target miRNA) and of the reporting strand (to classify the particle as positive or negative). The particle ON/OFF ratio allows to compute using statistics, for each target miRNA, its concentration in the sample. We have made a proof of concept for the digital and multiplex quantification of 6-10 miRNAs. The new methodology has been optimized to detect endogenous miRNA from cell extract. We have adapted this technique to the detection of other biological targets, in particular enzymes (12 demonstrated, e.g. DNA polymerases, nucleases such as Cas9, glycosylases, kinases, phosphatases…). We showed that beyond its use for biosensing applications, the assays can be used to investigate the functional heterogeneity of a population of enzymes, of great interest for directed evolution of proteins.
The second objective of this work is being conducted in collaboration with the group of Anthony Genot (LIMMS CNRS/U. of Tokyo), specialized DNA computing. The idea is to create DNA/enzyme reaction networks that mimic the architecture of an artificial neural network (ANN). Like an ANN that classify pictures of cats and dogs, our molecular neural network aims at classifying patient samples according to their content in miRNA. We have shown that our molecular toolbox can implement the essential ingredient of such DNA neuron that are: i) tunable weights: this is achieved by adjusting the concentration of the DNA template that convert the input molecule into a signal strand. We also show that negative weight can be implement by having the corresponding DNA template produce an anti-signal strand. ii) summation: all signal strands produced from different inputs can be collected by an amplification template. iii) nonlinear activation function: Once a threshold (set by a drain template), is exceeded, i.e. the signal strands reaches a given concentration, the amplification template exponential amplify the signal strand, resulting in a sharp transition from low to high concentration of the signal strand. Such simple DNA neuron can be used to build a perceptron-like network, allowing for the linear classification of samples based on up to 10 miRNA input. We demonstrated linear as well as nonlinear space partitioning. This is the culminating demonstration of an article published in Nature in 2022.
