Multiplexed direct detection of barcoded protein reporters on a nanopore array

Cardozo, Nicolas; Zhang, Karen; Doroschak, Kathryn; Nguyen, Aerilynn; Siddiqui, Zoheb; Bogard, Nicholas; Strauß, Karin; Ceze, Luís; Nivala, Jeff

doi:10.1038/s41587-021-01002-6

Cited by 30 publications

(30 citation statements)

References 24 publications

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“…Another strategy to unfold a protein relies on electrostatic pulling force as demonstrated for thioredoxin, which was captured with the help of an oligonucleotide attached to the C-terminus and gradually unfolded and translocated through an α-HL pore via a Brownian ratchet mechanism ( Rodriguez-Larrea and Bayley, 2013 ; Simon et al., 1992 ) ( Figure 3 G). This unfolding through electrostatic pulling was also used to demonstrate the readout of protein expression levels through genetically encoded reporter proteins that are detectable with the commercial ONT sequencing platform, an approach that offers a significantly increased multiplexing potential ( Cardozo et al., 2022 ). Subsequently, ClpX, the unfoldase of the ClpXP unfolding and degradation complex ( Baker and Sauer, 2012 ), was adopted to pull folded proteins through α-HL pores.…”

Section: Sensing Biomedical Relevant Molecules With Biological Nanoporesmentioning

confidence: 99%

Biological nanopores for single-molecule sensing

2022

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Section: Sensing Biomedical Relevant Molecules With Biological Nanoporesmentioning

confidence: 99%

Biological nanopores for single-molecule sensing

2022

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“… 80 A comparison between RFs and Convolutional Neural Networks (CNNs) has recently been conducted. 81 Using either a set of engineered signal features as input to an RF classifier or the raw ionic current signals directly into a CNN, both algorithms are found to achieve similar classification accuracy ranging from 80% to 90%, depending on the hyperparameters and data sets.…”

Section: Ml-based Signal Processing For Nanopore Sensingmentioning

confidence: 99%

“…A similar classifier is used in bacterial shape discrimination and for label-free electrical diagnostics of influenza to distinguish among individual viruses by their distinct features from the same group. , Recently, RoF and RF-based classifiers have been developed to identify four kinds of coronal viruses according to the features of translocation spikes, even when they have highly similar size and shape . A comparison between RFs and Convolutional Neural Networks (CNNs) has recently been conducted . Using either a set of engineered signal features as input to an RF classifier or the raw ionic current signals directly into a CNN, both algorithms are found to achieve similar classification accuracy ranging from 80% to 90%, depending on the hyperparameters and data sets.…”

Section: Ml-based Signal Processing For Nanopore Sensingmentioning

confidence: 99%

A Guide to Signal Processing Algorithms for Nanopore Sensors

2021

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Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

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“…The nanopore ionic current signal would then be dependent on the sequence of the strand immobilized within the pore 17 . To verify this approach, we tested biotin-modified DNA strands in the presence of streptavidin on a MinION nanopore sensor array using a custom run script that briefly reversed the applied voltage across the nanopore array every ten seconds, allowing us to repeatedly capture strands from the bulk solution and then eject them electrophoretically 30 . Repeatedly sampling strands from the bulk flow cell solution in this manner allowed us to detect DNA-specific nanopore capture events at a concentration-dependent frequency (Fig.…”

Section: Main Textmentioning

confidence: 99%

“…The nanopore data analysis pipeline is adapted from Cardozo et al 30 and begins by isolating capture events from raw nanopore current. A capture event occurs when the nanopore current drops to 70% or below of its open pore level for longer than one millisecond.…”

Section: Nanopore Data Analysismentioning

confidence: 99%

A nanopore interface for high bandwidth DNA computing

Zhang

Chen

Doroschak

et al. 2021

Preprint

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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). We show that engineered reporter probes can be detected and classified with high accuracy at the single-molecule level directly from raw nanopore signals using deep learning. We then demonstrate this method's utility in multiplexed detection of clinically relevant microRNA sequences. These results increase DSD output bandwidth by an order of magnitude over what is possible with fluorescence spectroscopy, laying the foundations for a new paradigm in DNA circuit readout and programmable multiplexed molecular diagnostics using portable nanopore devices.

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Multiplexed direct detection of barcoded protein reporters on a nanopore array

Cited by 30 publications

References 24 publications

Biological nanopores for single-molecule sensing

Biological nanopores for single-molecule sensing

A Guide to Signal Processing Algorithms for Nanopore Sensors

A nanopore interface for high bandwidth DNA computing

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