Cell-Based Assays for High-Throughput Screening

An, Wenlin; Tolliday, Nicola

doi:10.1007/s12033-010-9251-z

Cited by 168 publications

(128 citation statements)

References 69 publications

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“…The approach has already been successfully applied to diverse areas of research such as the identification of drug targets in cancer 9,[31][32][33] and embryonic development [34][35][36] and even has potential for application in deciphering the pathways involved in neuropsychiatric disorders 37,38 . However implementation of such a system requires a significant investment of time and effort with process optimization often taking a minimum of 6 months.…”

Section: Discussionmentioning

confidence: 99%

High Content Screening in Neurodegenerative Diseases

Jain¹,

Kesteren²,

Heutink³

2012

JoVE

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The functional annotation of genomes, construction of molecular networks and novel drug target identification, are important challenges that need to be addressed as a matter of great urgency [1][2][3][4] . Multiple complementary 'omics' approaches have provided clues as to the genetic risk factors and pathogenic mechanisms underlying numerous neurodegenerative diseases, but most findings still require functional validation 5. For example, a recent genome wide association study for Parkinson's Disease (PD), identified many new loci as risk factors for the disease, but the underlying causative variant(s) or pathogenic mechanism is not known 6,7 . As each associated region can contain several genes, the functional evaluation of each of the genes on phenotypes associated with the disease, using traditional cell biology techniques would take too long.There is also a need to understand the molecular networks that link genetic mutations to the phenotypes they cause. It is expected that disease phenotypes are the result of multiple interactions that have been disrupted. Reconstruction of these networks using traditional molecular methods would be time consuming. Moreover, network predictions from independent studies of individual components, the reductionism approach, will probably underestimate the network complexity 8 . This underestimation could, in part, explain the low success rate of drug approval due to undesirable or toxic side effects. Gaining a network perspective of disease related pathways using HT/HC cellular screening approaches, and identifying key nodes within these pathways, could lead to the identification of targets that are more suited for therapeutic intervention.High-throughput screening (HTS) is an ideal methodology to address these issues [9][10][11][12] . but traditional methods were one dimensional whole-well cell assays, that used simplistic readouts for complex biological processes. They were unable to simultaneously quantify the many phenotypes observed in neurodegenerative diseases such as axonal transport deficits or alterations in morphology properties 13,14 . This approach could not be used to investigate the dynamic nature of cellular processes or pathogenic events that occur in a subset of cells. To quantify such features one has to move to multi-dimensional phenotypes termed high-content screening (HCS) 4,[15][16][17] . HCS is the cell-based quantification of several processes simultaneously, which provides a more detailed representation of the cellular response to various perturbations compared to HTS. HCS has many advantages over HTS 18,19 , but conducting a high-throughput (HT)-high-content (HC) screen in neuronal models is problematic due to high cost, environmental variation and human error. In order to detect cellular responses on a 'phenomics' scale using HC imaging one has to reduce variation and error, while increasing sensitivity and reproducibility.Herein we describe a method to accurately and reliably conduct shRNA screens using automated cell culturing 20 and H...

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Section: Discussionmentioning

confidence: 99%

High Content Screening in Neurodegenerative Diseases

Jain¹,

Kesteren²,

Heutink³

2012

JoVE

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“…Today we are faced with ultra-HTS daily screen rates of hundreds of thousands, in part thanks to the continued development of technologies such as micro-fluidics (Agresti et al, 2010). As a discovery tool, it traces it roots back over twenty years (An & Tolliday, 2010), however it is the more recent improvements in cell culture technique -with the potential for multivariate output such as gene expression -that brings it into the domain of high content computational biology. With this maturation of cell-based assays we also notice an increased focus on statistical rigour, analytical integration, and the apparent user-driven plateau in miniaturisation (Mayr & Bojanic, 2009).…”

Section: The Marriage Of 'High Throughput' and 'High Content'mentioning

confidence: 99%

High Content and Throughput Drug Discovery

Wills¹

2011

Systems and Computational Biology - Molecular and Cellular Experimental Systems

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“…This is so far the first trial of HTS to a solar energy system design. The HTS method (roughly defined as the screening of the candidates with the best target properties using advanced high-throughput experimental and/or computational techniques) has already been widely used in biological [24][25][26][27][28] and computational [29][30][31] areas. With the basic concept that screening thousands or even millions of possible cases to discover the candidates with the best target functions or performances, HTS helps people dramatically reduce the required regular experiments, saving much economic cost and manpower.…”

Section: Introductionmentioning

confidence: 99%

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Liu

et al. 2017

International Journal of Photoenergy

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Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a highperformance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

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Cell-Based Assays for High-Throughput Screening

Cited by 168 publications

References 69 publications

High Content Screening in Neurodegenerative Diseases

High Content Screening in Neurodegenerative Diseases

High Content and Throughput Drug Discovery

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

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