Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data

Celik, Numan; O’Brien, Fiona; Brennan, Sean; Rainbow, Richard D.; Dart, Caroline; Zheng, Yalin; Coenen, Frans; Barrett‐Jolley, Richard

doi:10.1038/s42003-019-0729-3

Cited by 33 publications

(32 citation statements)

References 43 publications

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“…In our study we could only demonstrate a beta phase relationship when applying a Hilbert transform and not with the FFT approach. This adds to previous literature reporting differences in frequency relationships with phase modulation [ 16 , 33 , 42 – 45 ]. These differences could be explained by the strength of oscillatory power during TMS stimulation.…”

Section: Discussionsupporting

confidence: 66%

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

et al. 2021

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The evaluation of transcranial magnetic stimulation (TMS)-induced motor evoked potentials (MEPs) promises valuable information about fundamental brain related mechanisms and may serve as a diagnostic tool for clinical monitoring of therapeutic progress or surgery procedures. However, reports about spontaneous fluctuations of MEP amplitudes causing high intra-individual variability have led to increased concerns about the reliability of this measure. One possible cause for high variability of MEPs could be neuronal oscillatory activity, which reflects fluctuations of membrane potentials that systematically increase and decrease the excitability of neuronal networks. Here, we investigate the dependence of MEP amplitude on oscillation power and phase by combining the application of single pulse TMS over the primary motor cortex with concurrent recordings of electromyography and electroencephalography. Our results show that MEP amplitude is correlated to alpha phase, alpha power as well as beta phase. These findings may help explain corticospinal excitability fluctuations by highlighting the modulatory effect of alpha and beta phase on MEPs. In the future, controlling for such a causal relationship may allow for the development of new protocols, improve this method as a (diagnostic) tool and increase the specificity and efficacy of general TMS applications.

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Section: Discussionsupporting

confidence: 66%

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

et al. 2021

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“…It automatically idealizes the complex activity of the single-molecule with enhanced accuracy and that the process is pretty fast, for details see ref. [ 64 ]. The critical first step in understanding the electrophysiology technique recorded ion channel current traces lies in event detection, which is the so-called “idealization”.…”

Section: Deep Learning Models Explain Ion Channel Featuresmentioning

confidence: 99%

Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Ashrafuzzaman

2021

Membranes

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Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.

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“…These machine learning algorithms could be transformative for the analysis of complex single-molecule data, because they could be used to quantify complex multi-state data. In fact, neural network techniques have already been applied to image processing in super-resolution microscopy, [167] fluorescence-based detection, [168] and also for signal analysis in nanopore sensors, [169,170] ion channel patch-clamps, [171] and DNA sequencers. [169,172] In the latter example, a neural network was applied to replace the hidden Markov model in a commercial nanopore-based DNA sequencer.…”

Section: Improving Data Analysismentioning

confidence: 99%

“…However, the quality of the output strongly depends on the quality of the training dataset, and therefore the availability of good training data is crucial. Training of the network could be done using experimental data, [169,170] or simulations, [171] or a combinatorial approach. [168] Although simulations provide a scalable means to rapidly generate training data, they should include faithful models of both the signal and the noise in the data.…”

Section: Improving Data Analysismentioning

confidence: 99%

Plasmonic Assemblies for Real‐Time Single‐Molecule Biosensing

Armstrong

Horáček

Zijlstra

2020

Small

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and bound states in the continuum, [27,28] and have been used in applications like biosensing [29] and color printing. [30] Plasmonic assemblies have proven particularly powerful in the field of biosensing. The presence of a biomarker results in, e.g., the (dis-)assembly of solution-phase plasmonic particles, or to changes in the particle-spacing in an assembly. Both result in strong changes in the far-field optical response of the plasmonic assemblies that is most often monitored using spectroscopy in a UV-vis spectrometer. In solution-based sensors, clustering of metal nanoparticles can be induced by target molecules, shifting the optical extinction spectrum of the nanoparticle solution (Figure 1d). [31] Such clustering assays based on particle (dis-) assembly have resulted in colorimetric assays for a range of analytes including proteins, DNA/RNA sequences, heavy ions, and toxic compounds. [31,32] Single-molecule biosensors, on the other hand, detect analytes by probing the optical response of a single nanoparticle. An important advantage of single-molecule sensing is the ability to construct histograms of sensor properties at the single-molecule level, e.g., the induced plasmon shift and statistical parameters like the waiting-time between events. Single-molecule biosensing using plasmonic sensors has been demonstrated in 2012 by Figure 1. Reported applications of nanoparticle assemblies. a) Left: Calculated SERS enhancement factors for a sphere dimer with a 2 nm gap spacing. Right: A Raman spectra of methylene blue dispersed on a monolayer (black) and bilayer (red) of gold nanoparticles. Adapted with permission. [11] Copyright 2019, American Chemical Society. (https://pubs.acs.org/doi/10.1021/acsnano.9b04224) Further permissions related to this material should be directed to the ACS. b) Non-linear emission spectrum of noncentrosymmetric patterned gold nanorods. The third-harmonic generation (THG) peak is in green and the second-harmonic generation (SHG) is in orange. Top inset-scheme of the assembly's THG (green) and SHG (orange) stemming from the red excitation wavelength (ω). Bottom inset-electron microscopy image of the nanoassembly (scale bar 200 nm). Adapted with permission. [16] Copyright 2019, American Chemical Society. c) Examples of metamaterials comprised of plasmonic assemblies (scale bars are 500 nm). Reproduced with permission. [25] Copyright 2014, Springer Nature. d) Left: A cluster assay biosensor in which nanoparticle assembly is induced by a target molecule. Right: An example UV-vis spectrum of the gold nanoparticle monomers (red) and subsequent assemblies upon addition of target molecules (blue).

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Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data

Cited by 33 publications

References 43 publications

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

Phase and power modulations on the amplitude of TMS-induced motor evoked potentials

Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Plasmonic Assemblies for Real‐Time Single‐Molecule Biosensing

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