DNA circuit-based immunoassay for ultrasensitive protein pattern classification.

Cytokines are important immune modulators, and pivotal biomarkers for the diagnostic of various diseases. In standard analytical procedure, each protein is detected individually, using for instance gold standard ELISA protocols or nucleic acid amplification-based immunoassays. In recent years, DNA nanotechnology has been employed for creating sophisticated biomolecular systems that perform neuromorphic computing on molecular inputs, opening the door to concentration pattern recognition for biomedical applications. This work introduces immuno-PUMA (i-PUMA), an isothermal amplification-based immunoassay for ultrasensitive protein detection. The assay couples the convenience of supported format of an ELISA protocol with the computing capabilities of a DNA/enzyme circuit. We demonstrate a limit of detection of 2.1 fM, 8.7 fM and 450 aM for IL12, IL4 and IFNγ cytokines, respectively, outperforming the traditional ELISA format. i-PUMA's versatility extends to molecular computation, allowing the creation of 2-input perceptron-like classifiers for IL12 and IL4, with tunable weight sign and amplitude. Overall, i-PUMA represents a sensitive, low-cost, and versatile immunoassay with potential applications in multimarker-based sample classification, complementing existing molecular profiling techniques.

Time-encoded electrical detection of trace RNA biomarker by integrating programmable molecular amplifier on chip.

One of the serious challenges facing modern point-of-care (PoC) molecular diagnostic platforms relate to reliable detection of low concentration biomarkers such as nucleic acids or proteins in biological samples. Non-specific analyte-receptor interactions due to competitive binding in the presence of abundant molecules, inefficient mass transport and very low number of analyte molecules in sample volume, in general pose critical hurdles for successful implementation of such PoC platforms for clinical use. Focusing on these specific challenges, this work reports a unique PoC biosensor that combines the advantages of nanoscale biologically-sensitive field-effect transistor arrays (BioFET-arrays) realized in a wafer-scale top-down nanofabrication as high sensitivity electrical transducers with that of sophisticated molecular programs (MPs) customized for selective recognition of analyte miRNAs and amplification resulting in an overall augmentation of signal transduction strategy. The MPs realize a programmable universal molecular amplifier (PUMA) in fluidic matrix on chip and provide a biomarker-triggered exponential release of small nucleic acid sequences easily detected by receptor-modified BioFETs. A common miRNA biomarker LET7a was selected for successful demonstration of this novel biosensor, achieving limit of detection (LoD) down to 10 fM and wide dynamic ranges (10 pM-10 nM) in complex physiological solutions. As the determination of biomarker concentration is implemented by following the electrical signal related to analyte-triggered PUMA in time-domain instead of measuring the threshold shifts of BioFETs, and circumvents direct hybridization of biomarkers at transducer surface, this new strategy also allows for multiple usage (>3 times) of the biosensor platform suggesting exceptional cost-effectiveness for practical use.

Coupling Exponential to Linear Amplification for Endpoint Quantitative Analysis.

Exponential DNA amplification techniques are fundamental in ultrasensitive molecular diagnostics. These systems offer a wide dynamic range, but the quantification requires real-time monitoring of the amplification reaction. Linear amplification schemes, despite their limited sensitivity, can achieve quantitative measurement from a single end-point readout, suitable for low-cost, point-of-care, or massive testing. Reconciling the sensitivity of exponential amplification with the simplicity of end-point readout would thus break through a major design dilemma and open a route to a new generation of massively scalable quantitative bioassays. Here a hybrid nucleic acid-based circuit design is introduced to compute a logarithmic function, therefore providing a wide dynamic range based on a single end-point measurement. CELIA (Coupling Exponential amplification reaction to LInear Amplification) exploits a versatile biochemical circuit architecture to couple a tunable linear amplification stage - optionally embedding an inverter function - downstream of an exponential module in a one-pot format. Applied to the detection of microRNAs, CELIA provides a limit of detection in the femtomolar range and a dynamic range of six decades. This isothermal approach bypasses thermocyclers without compromising sensitivity, thereby opening the way to applications in various diagnostic assays, and providing a simplified, cost-efficient, and high throughput solution for quantitative nucleic acid analysis.

Functional analysis of single enzymes combining programmable molecular circuits with droplet-based microfluidics.

The analysis of proteins at the single-molecule level reveals heterogeneous behaviours that are masked in ensemble-averaged techniques. The digital quantification of enzymes traditionally involves the observation and counting of single molecules partitioned into microcompartments via the conversion of a profluorescent substrate. This strategy, based on linear signal amplification, is limited to a few enzymes with sufficiently high turnover rate. Here we show that combining the sensitivity of an exponential molecular amplifier with the modularity of DNA-enzyme circuits and droplet readout makes it possible to specifically detect, at the single-molecule level, virtually any D(R)NA-related enzymatic activity. This strategy, denoted digital PUMA (Programmable Ultrasensitive Molecular Amplifier), is validated for more than a dozen different enzymes, including many with slow catalytic rate, and down to the extreme limit of apparent single turnover for Streptococcus pyogenes Cas9. Digital counting uniquely yields absolute molar quantification and reveals a large fraction of inactive catalysts in all tested commercial preparations. By monitoring the amplification reaction from single enzyme molecules in real time, we also extract the distribution of activity among the catalyst population, revealing alternative inactivation pathways under various stresses. Our approach dramatically expands the number of enzymes that can benefit from quantification and functional analysis at single-molecule resolution. We anticipate digital PUMA will serve as a versatile framework for accurate enzyme quantification in diagnosis or biotechnological applications. These digital assays may also be utilized to study the origin of protein functional heterogeneity.

In Vitro Enzyme Self-Selection Using Molecular Programs.

Directed evolution provides a powerful route for in vitro enzyme engineering. State-of-the-art techniques functionally screen up to millions of enzyme variants using high throughput microfluidic sorters, whose operation remains technically challenging. Alternatively, in vitro self-selection methods, analogous to in vivo complementation strategies, open the way to even higher throughputs, but have been demonstrated only for a few specific activities. Here, we leverage synthetic molecular networks to generalize in vitro compartmentalized self-selection processes. We introduce a programmable circuit architecture that can link an arbitrary target enzymatic activity to the replication of its encoding gene. Microencapsulation of a bacterial expression library with this autonomous selection circuit results in the single-step and screening-free enrichment of genetic sequences coding for programmed enzymatic phenotypes. We demonstrate the potential of this approach for the nicking enzyme Nt.BstNBI (NBI). We applied autonomous selection conditions to enrich for thermostability or catalytic efficiency, manipulating up to 107 microcompartments and 5 × 105 variants at once. Full gene reads of the libraries using nanopore sequencing revealed detailed mutational activity landscapes, suggesting a key role of electrostatic interactions with DNA in the enzyme's turnover. The most beneficial mutations, identified after a single round of self-selection, provided variants with, respectively, 20 times and 3 °C increased activity and thermostability. Based on a modular molecular programming architecture, this approach does not require complex instrumentation and can be repurposed for other enzymes, including those that are not related to DNA chemistry.

Silicon chambers for enhanced incubation and imaging of microfluidic droplets.

Droplet microfluidics has become a powerful tool in life sciences, underlying digital assays, single-cell sequencing or directed evolution, and it is making foray in physical sciences as well. Imaging and incubation of droplets are crucial, yet they are encumbered by the poor optical, thermal and mechanical properties of PDMS, a material commonly used in microfluidics labs. Here we show that Si is an ideal material for droplet chambers. Si chambers pack droplets in a crystalline and immobile monolayer, are immune to evaporation or sagging, boost the number of collected photons, and tightly control the temperature field sensed by droplets. We use the mechanical and optical benefits of Si chambers to image ≈1 million of droplets from a multiplexed digital assay - with an acquisition rate similar to the best in-line methods. Lastly, we demonstrate their applicability with a demanding assay that maps the thermal dependence of Michaelis-Menten constants with an array of ≈150 000 droplets. The design of the Si chambers is streamlined to avoid complicated fabrication and improve reproducibility, which makes Si a complementary material to PDMS in the toolbox of droplet microfluidics.

Programmable Ultrasensitive Molecular Amplifier for Digital and Multiplex MicroRNA Quantification.

Digital bioassays, popularized by digital PCR, provide some of the most robust and accurate methods for nucleic acid quantification. In this chapter, we detail a protocol for digital, isothermal, and multiplex detection of microRNAs, which relies on a recently developed amplification method. Our approach uses programmable ultrasensitive molecular amplifiers (PUMAs) to reveal the presence of target microRNAs randomly isolated in picoliter-size microfluidic droplets. Nonspecific amplification in droplets that do not contain a target is eliminated by an active threshold mechanism. Multiple circuits can be assembled for the multiplex digital detection of up to three targets. We finally present the option of using fluorescent dropcodes to streamline the assay and analyze more than a dozen samples in parallel.

Molecular Computation for Molecular Classification.

DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.

Accurate gene consensus at low nanopore coverage.

BACKGROUND: Nanopore technologies allow high-throughput sequencing of long strands of DNA at the cost of a relatively large error rate. This limits its use in the reading of amplicon libraries in which there are only a few mutations per variant and therefore they are easily confused with the sequencing noise. Consensus calling strategies reduce the error but sacrifice part of the throughput on reading typically 30 to 100 times each member of the library. FINDINGS: In this work, we introduce SINGLe (SNPs In Nanopore reads of Gene Libraries), an error correction method to reduce the noise in nanopore reads of amplicons containing point variations. SINGLe exploits that in an amplicon library, all reads are very similar to a wild-type sequence from which it is possible to experimentally characterize the position-specific systematic sequencing error pattern. Then, it uses this information to reweight the confidence given to nucleotides that do not match the wild-type in individual variant reads and incorporates it on the consensus calculation. CONCLUSIONS: We tested SINGLe in a mutagenic library of the KlenTaq polymerase gene, where the true mutation rate was below the sequencing noise. We observed that contrary to other methods, SINGLe compensates for the systematic errors made by the basecallers. Consequently, SINGLe converges to the true sequence using as little as 5 reads per variant, fewer than the other available methods.

Lessons from Biomass Valorization for Improving Plastic-Recycling Enzymes.

Synthetic polymers such as plastics exhibit numerous advantageous properties that have made them essential components of our daily lives, with plastic production doubling every 15 years. The relatively low cost of petroleum-based polymers encourages their single use and overconsumption. Synthetic plastics are recalcitrant to biodegradation, and mismanagement of plastic waste leads to their accumulation in the ecosystem, resulting in a disastrous environmental footprint. Enzymes capable of depolymerizing plastics have been reported recently that may provide a starting point for eco-friendly plastic recycling routes. However, some questions remain about the mechanisms by which enzymes can digest insoluble solid substrates. We review the characterization and engineering of plastic-eating enzymes and provide some comparisons with the field of lignocellulosic biomass valorization.

A small-molecule chemical interface for molecular programs.

In vitro molecular circuits, based on DNA-programmable chemistries, can perform an increasing range of high-level functions, such as molecular level computation, image or chemical pattern recognition and pattern generation. Most reported demonstrations, however, can only accept nucleic acids as input signals. Real-world applications of these programmable chemistries critically depend on strategies to interface them with a variety of non-DNA inputs, in particular small biologically relevant chemicals. We introduce here a general strategy to interface DNA-based circuits with non-DNA signals, based on input-translating modules. These translating modules contain a DNA response part and an allosteric protein sensing part, and use a simple design that renders them fully tunable and modular. They can be repurposed to either transmit or invert the response associated with the presence of a given input. By combining these translating-modules with robust and leak-free amplification motifs, we build sensing circuits that provide a fluorescent quantitative time-response to the concentration of their small-molecule input, with good specificity and sensitivity. The programmability of the DNA layer can be leveraged to perform DNA based signal processing operations, which we demonstrate here with logical inversion, signal modulation and a classification task on two inputs. The DNA circuits are also compatible with standard biochemical conditions, and we show the one-pot detection of an enzyme through its native metabolic activity. We anticipate that this sensitive small-molecule-to-DNA conversion strategy will play a critical role in the future applications of molecular-level circuitry.

Advances in multiplexed techniques for the detection and quantification of microRNAs.

MicroRNA detection is currently a crucial analytical chemistry challenge: almost 2000 papers were referenced in PubMed in 2018 and 2019 for the keywords "miRNA detection method". MicroRNAs are potential biomarkers for multiple diseases including cancers, neurodegenerative and cardiovascular diseases. Since miRNAs are stably released in bodily fluids, they are of prime interest for the development of non-invasive diagnosis methods, such as liquid biopsies. Their detection is however challenging, as high levels of sensitivity, specificity and robustness are required. The analysis also needs to be quantitative, since the aim is to detect miRNA concentration changes. Moreover, a high multiplexing capability is also of crucial importance, since the clinical potential of miRNAs probably lays in our ability to perform parallel mapping of multiple miRNA concentrations and recognize typical disease signature from this profile. A plethora of biochemical innovative detection methods have been reported recently and some of them provide new solutions to the problem of sensitive multiplex detection. In this review, we propose to analyze in particular the new developments in multiplexed approaches to miRNA detection. The main aspects of these methods (including sensitivity and specificity) will be analyzed, with a particular focus on the demonstrated multiplexing capability and potential of each of these methods.

Multiplex Digital MicroRNA Detection Using Cross-Inhibitory DNA Circuits.

Ubiquitous post-transcriptional regulators in eukaryotes, microRNAs are currently emerging as promising biomarkers of physiological and pathological processes. Multiplex and digital detection of microRNAs represents a major challenge toward the use of microRNA signatures in clinical settings. The classical reverse transcription polymerase chain reaction quantification approach has important limitations because of the need for thermocycling and a reverse transcription step. Simpler, isothermal alternatives have been proposed, yet none could be adapted in both a digital and multiplex format. This is either because of a lack of sensitivity that forbids single molecule detection or molecular cross-talk reactions that are responsible for nonspecific amplification. Building on an ultrasensitive isothermal amplification mechanism, we present a strategy to suppress cross-talk reactions, allowing for robust isothermal and multiplex detection of microRNA targets. Our approach relies on target-specific DNA circuits interconnected with DNA-encoded inhibitors that repress nonspecific signal amplification. We demonstrate the one-step, isothermal, digital, and simultaneous quantification of various pairs of important microRNA targets.

Quantifying the Performance of Micro-Compartmentalized Directed Evolution Protocols.

High-throughput, in vitro approaches for the evolution of enzymes rely on a random micro-encapsulation to link phenotypes to genotypes, followed by screening or selection steps. In order to optimise these approaches, or compare one to another, one needs a measure of their performance at extracting the best variants of a library. Here, we introduce a new metric, the Selection Quality Index (SQI), which can be computed from a simple mock experiment, performed with a known initial fraction of active variants. In contrast to previous approaches, our index integrates the effect of random co-encapsulation, and comes with a straightforward experimental interpretation. We further show how this new metric can be used to extract general protocol efficiency trends or reveal hidden selection mechanisms such as a counterintuitive form of beneficial poisoning in the compartmentalized self-replication protocol.

Isothermal digital detection of microRNAs using background-free molecular circuit.

MicroRNAs, a class of transcripts involved in the regulation of gene expression, are emerging as promising disease-specific biomarkers accessible from tissues or bodily fluids. However, their accurate quantification from biological samples remains challenging. We report a sensitive and quantitative microRNA detection method using an isothermal amplification chemistry adapted to a droplet digital readout. Building on molecular programming concepts, we design a DNA circuit that converts, thresholds, amplifies, and reports the presence of a specific microRNA, down to the femtomolar concentration. Using a leak absorption mechanism, we were able to suppress nonspecific amplification, classically encountered in other exponential amplification reactions. As a result, we demonstrate that this isothermal amplification scheme is adapted to digital counting of microRNAs: By partitioning the reaction mixture into water-in-oil droplets, resulting in single microRNA encapsulation and amplification, the method provides absolute target quantification. The modularity of our approach enables to repurpose the assay for various microRNA sequences.

Streamlined digital bioassays with a 3D printed sample changer.

Droplet-based microfluidics has permeated many areas of life sciences including biochemistry, biology and medicine. Water-in-oil droplets act as independent femto- to nano-liter reservoirs, enabling the parallelization of (bio)chemical reactions with a minimum sample input. Among the range of applications spanned by droplet microfluidics, digital detection of biomolecules, using Poissonian isolation of single molecules in compartments, has gained considerable attention due to the high accuracy, sensitivity and robustness of these methods. However, while the droplet throughput can be very high, the sample throughput of these methods is poor in comparison to well plate-based assays. This limitation comes from the necessity to convert independently each sample into a monodisperse emulsion. In this paper, we report a versatile device that performs the quick sequential partitioning of up to 15 samples using a single microfluidic chip. A 3D printed sample rotor is loaded with all samples and connected to a pressure source. Simple magnetic actuation is then used to inject the samples in the microfluidic chip without pressure disruption. This procedure generates monodisperse droplets with high sample-to-sample consistency. We also describe a fluorescent barcoding strategy that allows all samples to be collected, incubated, imaged and analyzed simultaneously, thus decreasing significantly the time of the assay. As an example of application, we perform a droplet digital PCR assay for the quantification of a DNA amplicon from 8 samples in less than 2 hours. We further validate our approach demonstrating the parallel quantification of 11 microRNAs from a human sample using an isothermal nucleic acid amplification chemistry. As an off-chip device, the sample changer can be connected to a variety of microfluidic geometries and therefore, used for a wide range of applications.

Emerging isothermal amplification technologies for microRNA biosensing: Applications to liquid biopsies.

The potential of microRNAs (miRNAs) as biomarker candidates in clinical practice for diagnosis, prognosis and treatment response prediction, especially in liquid biopsies, has led to a tremendous demand for techniques that can detect these molecules rapidly and accurately. Hence, numerous achievements have been reported recently in miRNA research. In this review, we discuss the challenges associated with the emerging field of miRNA detection, which are linked to the intrinsic properties of miRNAs, advantages and drawbacks of the currently available technologies and their potential applications in clinical research. We summarize the most promising nucleic acid amplification techniques applied to the in vitro detection of miRNAs, with a particular emphasis on the state of the art for isothermal alternatives to RT-qPCR. We detail the sensitivity, specificity and quantitativity of these approaches, as well as their potential for multiplexing. We also review the different detection formats to which these chemistries have been adapted, including analog readouts such as real-time monitoring, digital counting based on single-molecule amplification in compartments, and surface-based strategies.

Selection strategies for randomly partitioned genetic replicators.

The amplification cycle of many replicators (natural or artificial) involves the usage of a host compartment, inside of which the replicator expresses phenotypic compounds necessary to carry out its genetic replication. For example, viruses infect cells, where they express their own proteins and replicate. In this process, the host cell boundary limits the diffusion of the viral protein products, thereby ensuring that phenotypic compounds, such as proteins, promote the replication of the genes that encoded them. This role of maintaining spatial colocalization, also called genotype-phenotype linkage, is a critical function of compartments in natural selection. In most cases, however, individual replicating elements do not distribute systematically among the hosts, but are randomly partitioned. Depending on the replicator-to-host ratio, more than one variant may thus occupy some compartments, blurring the genotype-phenotype linkage and affecting the effectiveness of natural selection. We derive selection equations for a variety of such random multiple occupancy situations, in particular considering the effect of replicator population polymorphism and internal replication dynamics. We conclude that the deleterious effect of random multiple occupancy on selection is relatively benign, and may even completely vanish is some specific cases. In addition, given that higher mean occupancy allows larger populations to be channeled through the selection process, and thus provide a better exploration of phenotypic diversity, we show that it may represent a valid strategy in both natural and technological cases.

Spatiotemporal control of DNA-based chemical reaction network via electrochemical activation in microfluidics.

In recent years, DNA computing frameworks have been developed to create dynamical systems which can be used for information processing. These emerging synthetic biochemistry tools can be leveraged to gain a better understanding of fundamental biology but can also be implemented in biosensors and unconventional computing. Most of the efforts so far have focused on changing the topologies of DNA molecular networks or scaling them up. Several issues have thus received little attention and remain to be solved to turn them into real life technologies. In particular, the ability to easily interact in real-time with them is a key requirement. The previous attempts to achieve this aim have used microfluidic approaches, such as valves, which are cumbersome. We show that electrochemical triggering using DNA-grafted micro-fabricated gold electrodes can be used to give instructions to these molecular systems. We demonstrate how this approach can be used to release at specific times and locations DNA- based instructions. In particular, we trigger reaction-diffusion autocatalytic fronts in microfluidic channels. While limited by the stability of the Au-S bond, this easy to implement, versatile and scalable technique can be used in any biology laboratory to provide new ways to interact with any DNA-based computing framework.

Synthesis and materialization of a reaction-diffusion French flag pattern.

During embryo development, patterns of protein concentration appear in response to morphogen gradients. These patterns provide spatial and chemical information that directs the fate of the underlying cells. Here, we emulate this process within non-living matter and demonstrate the autonomous structuration of a synthetic material. First, we use DNA-based reaction networks to synthesize a French flag, an archetypal pattern composed of three chemically distinct zones with sharp borders whose synthetic analogue has remained elusive. A bistable network within a shallow concentration gradient creates an immobile, sharp and long-lasting concentration front through a reaction-diffusion mechanism. The combination of two bistable circuits generates a French flag pattern whose 'phenotype' can be reprogrammed by network mutation. Second, these concentration patterns control the macroscopic organization of DNA-decorated particles, inducing a French flag pattern of colloidal aggregation. This experimental framework could be used to test reaction-diffusion models and fabricate soft materials following an autonomous developmental programme.