High-throughput Protocol
High-throughput Protocol - Related Videos
11:33
Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography
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Cited by 48 •
2016
We present a protocol on how to utilize high-throughput cryo-electron tomography to determine high resolution in situ structures of molecular machines. The protocol permits large amounts of data to be processed, avoids common bottlenecks and reduces resource downtime, allowing the user to focus on important biological questions.
09:44
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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2015
Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3’ UTR.
11:36
A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
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Cited by 6 •
2016
We present a protocol for the functional assessment of comprehensive single-site saturation mutagenesis libraries of proteins utilizing high-throughput sequencing. Importantly, this approach uses orthogonal primer pairs to multiplex library construction and sequencing. Representative results using TEM-1 β-lactamase selected at a clinically relevant dosage of ampicillin are provided.
06:19
High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
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Cited by 16 •
2023
This protocol details high-throughput crystallization screening, ranging from the 1,536 microassay plate preparation to the end of a 6 week experimental time window. Details are included about the sample setup, the imaging obtained, and how users can perform analyses using an artificial intelligence-enabled graphical user interface to quickly and efficiently identify macromolecular crystallization conditions.
09:37
Optimized Negative Staining: a High-throughput Protocol for Examining Small and Asymmetric Protein Structure by Electron Microscopy
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Cited by 71 •
2014
More than half of proteins are small proteins (molecular mass <200 kDa) that are challenging for both electron microscope imaging and three-dimensional reconstructions. Optimized negative staining is a robust and high-throughput protocol to obtain high contrast and relatively high resolution (~1 nm) images of small asymmetric proteins or complexes under different physiological conditions.
FAQs
Parallelization increases capacity by allowing many samples, conditions, or subjects to be processed within the same overall workflow. Automation helps coordinate repeated operations, while miniaturized assays use smaller experimental formats. Together, these features let behavioral researchers examine more experimental variation in limited time and resources, supporting systematic comparisons across groups.
Standardization keeps data collection consistent across the large number of samples or subjects handled by the workflow. This consistency makes behavioral responses easier to compare and supports reproducibility between experimental conditions. Without a common procedure, differences in handling or measurement could complicate interpretation of genetic, environmental, or pharmacological effects.
Structured analysis pipelines organize and compare the large datasets generated by high-throughput behavioral work. They help researchers evaluate responses across many conditions rather than treating each observation in isolation. This systematic analysis can reveal behavioral patterns that smaller experiments may miss and provides a consistent basis for interpreting variation among samples or subjects.
A smaller experiment generally examines fewer samples, conditions, or subjects, whereas a high-throughput approach expands the number evaluated within limited time and resources. The larger scale enables broader comparisons and increases the chance of detecting patterns across behavioral responses. Its value therefore lies in systematic coverage, not simply in performing the same experiment faster.
Researchers first establish a standardized workflow for the samples, conditions, or subjects under study. They then use parallelized processing, automation, or miniaturized assays to increase capacity and collect behavioral data consistently. Finally, structured analysis pipelines compare responses across the dataset, allowing researchers to evaluate variation and identify broader patterns.
They are useful when researchers need to screen the effects of genetic, environmental, or pharmacological factors across many experimental conditions. The approach supports systematic evaluation of behavioral variation and can help identify responses associated with disease-related phenotypes or potential interventions. Its expanded capacity is particularly valuable when smaller experiments cannot cover the relevant range of variables.
High-throughput behavioral studies can produce comparative response patterns across large datasets, helping researchers detect effects linked to genes, environments, or pharmacological conditions. These results may clarify behavioral variation, highlight disease-related phenotypes, and inform the evaluation of potential interventions. Because many variables can be examined together, the resulting comparisons can strengthen interpretation and reproducibility.