Automated whole-cell patch-clamp electrophysiology of neurons in vivo

Kodandaramaiah, Suhasa B.; Franzesi, Giovanni Talei; Chow, Brian Y.; Boyden, Edward S.; Forest, Craig R.

doi:10.1038/nmeth.1993

Cited by 220 publications

(240 citation statements)

References 13 publications

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“…For instance, patch clamping of muscle, nerve or brain cells is a prime candidate for tight integration of wet-lab experiment and computer modelling. The robotized, high-throughput systems available (Kodandaramaiah et al, 2012;Kolb et al, 2013;Wood et al, 2004) already have their own formats for specifying voltage-stepping protocols, and excitable cells are among the best-modelled biological systems in public repositories (Box 2). It is a small step to use the same protocol specifications for wet-lab and virtual experiments, and thus streamline the confrontation of simulated and experimental data.…”

Section: Box 3: Expanding the Scope Of Virtual Experimentsmentioning

confidence: 99%

A call for virtual experiments: Accelerating the scientific process

Cooper

Vik

Waltemath

2015

Progress in Biophysics and Molecular Biology

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Experimentation is fundamental to the scientific method, whether for exploration, description or explanation. We argue that promoting the reuse of virtual experiments (the in silico analogues of wet-lab or field experiments) would vastly improve the usefulness and relevance of computational models, encouraging critical scrutiny of models and serving as a common language between modellers and experimentalists. We review the benefits of reusable virtual experiments: in specifying, assaying, and comparing the behavioural repertoires of models; as prerequisites for reproducible research; to guide model reuse and composition; and for quality assurance in the translational application of models. A key step towards achieving this is that models and experimental protocols should be represented separately, but annotated so as to facilitate the linking of models to experiments and data. Lastly, we outline how the rigorous, streamlined confrontation between experimental datasets and candidate models would enable a "continuous integration" of biological knowledge, transforming our approach to systems biology. Dear Sirs,We enclose a manuscript titled "A call for virtual experiments: accelerating the scientific process" for inclusion within the Special Issue on "Multi-bio and multi-scale systems biology" in Progress in Biophysics and Molecular Biology. In it we propose the concept of "virtual experiments" as the in silico analogues of wet-lab or field experiments, and argue that this concept deserves as much attention as the computational models to which such experiments are applied. Promoting the reuse of virtual experiments would vastly improve the usefulness and relevance of computational models, encouraging critical scrutiny of models and serving as a common language between modellers and experimentalists.Experiments are fundamental to the scientific method, whether for exploration, description or explanation. Experiments should therefore be as central in computational biology as in other sciences, for studying the implications of theories or hypotheses encoded as computational models. In current practice, however, experiments applied to models are usually embedded in opaque computer code, which is often not available for public scrutiny. We discuss how the protocol for a computational experiment should be viewed as an entity in its own right, and cleanly separated from the description of the model itself, and from surrounding code. Annotations can then facilitate the linking of models to experiment descriptions and any corresponding data available.This approach can provide many benefits for scientific progress: in specifying, assaying, and comparing the behavioural repertoires of models; as a prerequisite for reproducible research; to guide model reuse and composition; and for quality assurance in the application of computational biology models. The paper lays out these benefits to motivate wider adoption and tool support for virtual experiments. We also present a "vision of the future" in which virtual experim...

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Section: Box 3: Expanding the Scope Of Virtual Experimentsmentioning

confidence: 99%

A call for virtual experiments: Accelerating the scientific process

Cooper

Vik

Waltemath

2015

Progress in Biophysics and Molecular Biology

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“…This complicated operation normally requires several months or even 1 year of training before the technician is able to reliably record from the cells [9] because of the difficulty in obtaining successful recording. This also significantly increases the cost of the whole patch clamp recording process.…”

Section: Introductionmentioning

confidence: 99%

Cell segmentation and pipette identification for automated patch clamp recording

et al. 2014

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A visual-based approach for identifying living cells and performing the automated patch clamp recording was reported. Based on the image processing and blob detection algorithm, the vision-based method was developed for the detection and identification of biological cells and micropipette. The method was implemented in a micromanipulation system that enabled the identification of the boundary and the center of the target cell and separation from its neighboring cells. The method successfully identified a batch of neuroblastoma cells with the highest yield of 90%. The results demonstrated that the visual-based approach can be integrated to the micromanipulation system to automatically manipulate the patch pipette tip to the center of the target cell, and as a result, the whole-cell recording can be performed precisely and effectively.

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“…52 On the three fronts of modeling, computation, and experiment, there have been a series of important recent advances, and many new avenues of research have emerged. These include: (1) new techniques to record high-density brain activity; [55][56][57] (2) theoretical advances and clinical application of deep brain stimulation methods; 58 (3) sophisticated biophysical models of rhythms and disease; 59,60 (4) novel optogenetic techniques that permit interrogation of neural circuits in vivo; 61 and, (5) increasingly-sophisticated data-analysis techniques. 62 The modeling and computational work-both deterministic and stochastic-have focused in part on reproducing experimental results, and on the investigation of the underlying biophysical and dynamical mechanisms, especially when experiments are not possible or very difficult to perform.…”

Section: Introductionmentioning

confidence: 99%

Introduction to Focus Issue: Rhythms and Dynamic Transitions in Neurological Disease: Modeling, Computation, and Experiment

Kaper

Kramer

Rotstein

2013

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Rhythmic neuronal oscillations across a broad range of frequencies, as well as spatiotemporal phenomena, such as waves and bumps, have been observed in various areas of the brain and proposed as critical to brain function. While there is a long and distinguished history of studying rhythms in nerve cells and neuronal networks in healthy organisms, the association and analysis of rhythms to diseases are more recent developments. Indeed, it is now thought that certain aspects of diseases of the nervous system, such as epilepsy, schizophrenia, Parkinson's, and sleep disorders, are associated with transitions or disruptions of neurological rhythms. This focus issue brings together articles presenting modeling, computational, analytical, and experimental perspectives about rhythms and dynamic transitions between them that are associated to various diseases. To have and to hold-and to oscillate-in sickness and in health. INTRODUCTIONDynamic phenomena, such as rhythmic oscillations and waves, are ubiquitous in the nervous system and have been associated with cognition and motor behavior in both health and disease. [1][2][3][4][5] Oscillations in different frequency ranges have been associated with healthy brain function, including learning, memory, spatial navigation, attention, sleep, and motor behavior. 2,6-14 Wave-like phenomena have been associated with sensory processing 15 and working memory. 16 Rhythmic neuronal activity-and transitions between rhythms-are also central phenomena in neurological disease. 5,17 Perhaps the most famous manifestations occur in epilepsy, which is characterized as a paroxysmal cerebral dysrhythmia (see, e.g., Ref. 18 A broad range of rhythms, and transitions between different rhythmic regimes, have been observed for these diseases. In epilepsy, the rhythms range from extremely fast (e.g., hundreds of Hertz 27-30 ) to slow (e.g., a few Hertz 31,32 ) and are bracketed by abrupt transitions at the onset and termination of seizure events. [33][34][35][36] Schizophrenia-characterized as a failure of cognitive integration-manifests in brain rhythms as altered beta and gamma band (15-70 Hz) synchronization. 22,37 In Alzheimer's disease, brain rhythms shift to power at lower frequencies, and the coherence of fast rhythms decreases. 23,38,39 In Parkinson's disease, pathologically exaggerated beta oscillations characterize the abnormal rhythms. 25,[40][41][42][43][44] Transitions between dynamic regimes have been observed during sleep, and abnormal transitions have been associated with sleep disorders. 26,[45][46][47][48][49][50][51] Finally, the slow waves of cortical spreading depression 52 underlie the reduction of excitability in neuronal tissue associated with migraine, 53,54 in particular, migraine with aura. 52 On the three fronts of modeling, computation, and experiment, there have been a series of important recent advances, and many new avenues of research have emerged. These include: (1) new techniques to record high-density brain activity; 55-57 (2) theoretical advances and clinical a...

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Automated whole-cell patch-clamp electrophysiology of neurons in vivo

Cited by 220 publications

References 13 publications

A call for virtual experiments: Accelerating the scientific process

A call for virtual experiments: Accelerating the scientific process

Cell segmentation and pipette identification for automated patch clamp recording

Introduction to Focus Issue: Rhythms and Dynamic Transitions in Neurological Disease: Modeling, Computation, and Experiment

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