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Neuromuscular disease modeling on a chip.

ABSTRACT: Organs-on-chips are broadly defined as microfabricated surfaces or devices designed to engineer cells into microscale tissues with native-like features and then extract physiologically relevant readouts at scale. Because they are generally compatible with patient-derived cells, these technologies can address many of the human relevance limitations of animal models. As a result, organs-on-chips have emerged as a promising new paradigm for patient-specific disease modeling and drug development. Because neuromuscular diseases span a broad range of rare conditions with diverse etiology and complex pathophysiology, they have been especially challenging to model in animals and thus are well suited for organ-on-chip approaches. In this Review, we first briefly summarize the challenges in neuromuscular disease modeling with animal models. Next, we describe a variety of existing organ-on-chip approaches for neuromuscular tissues, including a survey of cell sources for both muscle and nerve, and two- and three-dimensional neuromuscular tissue-engineering techniques. Although researchers have made tremendous advances in modeling neuromuscular diseases on a chip, the remaining challenges in cell sourcing, cell maturity, tissue assembly and readout capabilities limit their integration into the drug development pipeline today. However, as the field advances, models of healthy and diseased neuromuscular tissues on a chip, coupled with animal models, have vast potential as complementary tools for modeling multiple aspects of neuromuscular diseases and identifying new therapeutic strategies.

SUBMITTER: Santoso JW

PROVIDER: S-EPMC7358135 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Neuromuscular disease modeling on a chip.

Santoso Jeffrey W JW McCain Megan L ML

Disease models & mechanisms 20200707 7

Organs-on-chips are broadly defined as microfabricated surfaces or devices designed to engineer cells into microscale tissues with native-like features and then extract physiologically relevant readouts at scale. Because they are generally compatible with patient-derived cells, these technologies can address many of the human relevance limitations of animal models. As a result, organs-on-chips have emerged as a promising new paradigm for patient-specific disease modeling and drug development. Be ...[more]

PMID: 32817118

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Project description:BackgroundThe ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.ResultsIn this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length.ConclusionThe Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the Bayesian latent method can outperform other methods, especially when the data contain outliers.

Dataset Information

Neuromuscular disease modeling on a chip.

Publications

Neuromuscular disease modeling on a chip.

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