High Throughput Scanning
High Throughput Scanning - Related Videos
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.
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.
02:28
Leaky Scanning
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2020
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...
12:03
Differential Scanning Calorimetry
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2023
Source: Danielle N. Beatty and Taylor D. Sparks, Department of Materials Science and Engineering, The University of Utah, Salt Lake City, UT Differential scanning calorimetry (DSC) is an important measurement to characterize thermal properties of materials. DSC is used primarily to calculate the amount of heat stored in a material as it heats up (heat capacity) as well as the heat absorbed or released during chemical reactions or phase changes. However, measurement of this heat can also lead to...
11:41
Scanning Electron Microscopy (SEM)
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2023
Source: Laboratory of Dr. Andrew J. Steckl — University of Cincinnati A scanning electron microscope, or SEM, is a powerful microscope that uses electrons to form an image. It allows for imaging of conductive samples at magnifications that cannot be achieved using traditional microscopes. Modern light microscopes can achieve a magnification of ~1,000X, while typical SEM can reach magnifications of more than 30,000X. Because the SEM doesn’t use light to create images, the resulting pictures it...
FAQs
Parallelization allows many samples, measurements, images, or environmental locations to be examined within the same overall workflow rather than processed one at a time. This increases measurement speed and expands the number of observations available for comparison. The larger, more consistently collected dataset can reveal differences and patterns that a smaller manual survey might not capture efficiently.
These components divide the work of data collection and analysis. Sensors and imaging systems gather measurements or visual information, robotics can support the handling or examination of many observations, and computational pipelines organize the resulting data to identify signals, patterns, or differences. Together, they connect rapid observation with systematic interpretation across large datasets.
Standardization makes measurements, images, or observations more consistent across samples, locations, and time points. That consistency strengthens comparisons because observed differences are more likely to reflect environmental variation rather than changes in how data were collected or processed. In environmental studies, this supports clearer analysis of pollutants, microbial communities, ecosystem conditions, and emerging changes.
Manual examination may become impractical when researchers need to assess large numbers of samples, measurements, images, or locations. High-throughput scanning addresses that scale challenge by combining automated, parallelized collection with computational analysis. Its main advantage is not simply speed, but the ability to compare many standardized observations efficiently across environmental conditions.
A typical workflow begins by identifying the samples, measurements, images, or environmental locations to examine. Automated equipment or computational systems then collect and organize standardized observations across those targets. Researchers can compare the resulting data to identify patterns, signals, or differences across space or time, linking broad data collection to environmental interpretation.
The approach can support investigations of pollutants, microbial communities, ecosystem conditions, and environmental changes across space or time. By examining many observations, researchers can compare conditions efficiently and look for signals that may indicate emerging changes. The resulting information can contribute to risk assessment, conservation planning, and ecosystem management.