skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: A nanopore interface for higher bandwidth DNA computing
Abstract DNA has emerged as a powerful substrate for programming information processing machines at the nanoscale. Among the DNA computing primitives used today, DNA strand displacement (DSD) is arguably the most popular, with DSD-based circuit applications ranging from disease diagnostics to molecular artificial neural networks. The outputs of DSD circuits are generally read using fluorescence spectroscopy. However, due to the spectral overlap of typical small-molecule fluorescent reporters, the number of unique outputs that can be detected in parallel is limited, requiring complex optical setups or spatial isolation of reactions to make output bandwidths scalable. Here, we present a multiplexable sequencing-free readout method that enables real-time, kinetic measurement of DSD circuit activity through highly parallel, direct detection of barcoded output strands using nanopore sensor array technology (Oxford Nanopore Technologies’ MinION device). These results increase DSD output bandwidth by an order of magnitude over what is currently feasible with fluorescence spectroscopy.  more » « less
Award ID(s):
2006864 1841188 1954665
PAR ID:
10484235
Author(s) / Creator(s):
; ; ; ; ; ; ;
Publisher / Repository:
Nature communications
Date Published:
Journal Name:
Nature Communications
Volume:
13
Issue:
1
ISSN:
2041-1723
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. x (Ed.)
    DNA strand displacement (DSD) emerged as a prominent reaction motif for engineering nucleic acid-based computational devices with programmable behaviours. However, strand displacement circuits are susceptible to background noise, known as leaks, which disrupt their intended function. The ill effects of leaks are particularly severe in circuits with complex dynamics, as leaks in them amplify nonlinearly, resulting in rapid circuit degradation. Shadow cancellation is a dynamic leak-elimination strategy originally proposed to control the leak growth in such circuits. However, the kinetic restrictions of the method incur a significant design overhead, making it less accessible. In this work, we use domain-level DSD simulations to examine the method’s capabilities, the inner workings of its components and, most importantly, its robustness to the practical deviations in its design requirements. First, we show that the method could stabilize the dynamics of several catalytic and autocatalytic dynamical systems heavily affected by leaks. Then, through several probing experiments, we show that its design restrictions could be significantly relaxed without impacting the circuit function by simply adjusting the circuit parameters. Finally, we discuss several ideas to tackle the practical challenges in applying the method to arbitrary DSD circuits, paving the way for future experimental work. 
    more » « less
  2. Abstract Chemical systems have the potential to direct the next generation of dynamic materials if they can be integrated with a material while acting as the material’s own regulatory network. Chemical networks that use DNA and RNA strand displacement coupled with RNA synthesis and degradation, such as genelets, are promising chemical systems for this role. Genelets can produce a range of dynamic behaviors that respond to unique sets of environmental inputs. While a number of networks that generate specific types of outputs which vary in both time and amplitude have been developed, there are fewer examples of networks that recognize specific types of inputs in time and amplitude. Advanced chemical circuits in biology are capable of reading a given substrate concentration with relatively high accuracy to direct downstream function, demonstrating that such a chemical circuit is possible. Taking inspiration from this, we designed a genelet circuit which responds to a range of inputs by delivering a binary output based on the input concentration, and tested the network’s performance using an in silico model of circuit behavior. By modifying the concentrations of two circuit elements, we demonstrated that such a network topography could yield various target input concentration profiles to which a given circuit is sensitive. The number of unique elements in the final network topography as well as the individual circuit element concentrations are commensurate with properties of circuits that have been demonstrated experimentally. These factors suggest that such a network could be built and characterized in the laboratory. 
    more » « less
  3. The cerebellum consists of parallel circuit modules that contribute to diverse behaviors, spanning motor to cognitive. Recent work employing cell-type-specific tracing has identified circumscribed output channels of the cerebellar nuclei (CbN) that could confer tight functional specificity. These studies have largely focused on excitatory projections of the CbN, however, leaving open the question of whether inhibitory neurons also constitute multiple output modules. We mapped output and input patterns to intersectionally restricted cell types of the interposed and adjacent interstitial nuclei in mice. In contrast to the widespread assumption of primarily excitatory outputs and restricted inferior olive-targeting inhibitory output, we found that inhibitory neurons from this region ramified widely within the brainstem, targeting both motor- and sensory-related nuclei, distinct from excitatory output targets. Despite differences in output targeting, monosynaptic rabies tracing revealed largely shared afferents to both cell classes. We discuss the potential novel functional roles for inhibitory outputs in the context of cerebellar theory. 
    more » « less
  4. Transformer is an algorithm that adopts self‐attention architecture in the neural networks and has been widely used in natural language processing. In the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. We evaluated this idea using real data sets (Escherichia colidata and the human genome NA12878 sequenced by Simpsonet al.) and demonstrated the ability of Transformers to detect methylation on ionic signal data. BackgroundOxford Nanopore long‐read sequencing technology addresses current limitations for DNA methylation detection that are inherent in short‐read bisulfite sequencing or methylation microarrays. A number of analytical tools, such as Nanopolish, Guppy/Tombo and DeepMod, have been developed to detect DNA methylation on Nanopore data. However, additional improvements can be made in computational efficiency, prediction accuracy, and contextual interpretation on complex genomics regions (such as repetitive regions, low GC density regions). MethodIn the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. Transformer is an algorithm that adopts self‐attention architecture in the neural networks and has been widely used in natural language processing. ResultsCompared to traditional deep‐learning method such as convolutional neural network (CNN) and recurrent neural network (RNN), Transformer may have specific advantages in DNA methylation detection, because the self‐attention mechanism can assist the relationship detection between bases that are far from each other and pay more attention to important bases that carry characteristic methylation‐specific signals within a specific sequence context. ConclusionWe demonstrated the ability of Transformers to detect methylation on ionic signal data. 
    more » « less
  5. Abstract Nanopores are increasingly powerful tools for single molecule sensing, in particular, for sequencing DNA, RNA and peptides. This success has spurred efforts to sequence non-canonical nucleic acid bases and amino acids. While canonical DNA and RNA bases have pKas far from neutral, certain non-canonical bases, natural RNA modifications, and amino acids are known to have pKas near neutral pHs at which nanopore sequencing is typically performed. Previous reports have suggested that the nanopore signal may be sensitive to the protonation state of an individual moiety. We sequenced ion currents with the MspA nanopore using a single stranded DNA containing a single non-canonical DNA base (Z) at various pH conditions. The Z-base has a near-neutral pKa ∼ 7.8. We find that the measured ion current is remarkably sensitive to the protonation state of the Z-base. We demonstrate how nanopores can be used to localize and determine the pKa of individual moieties along a polymer. More broadly, these experiments provide a path to mapping different protonation sites along polymers and give insight in how to optimize sequencing of polymers that contain moieties with near-neutral pKas. 
    more » « less