Semi-high Throughput
Semi-high Throughput - Related Videos
09:57
Visualization of the Charcoal Agar Resazurin Assay for Semi-quantitative, Medium-throughput Enumeration of Mycobacteria
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Cited by 10 •
2016
The charcoal agar resazurin assay (CARA) is a semi-quantitative, medium-throughput method to assess activity of test agents against mycobacteria that are replicating, non-replicating, or both. The CARA permits rapid evaluation of time- and concentration-dependent activity and identifies parameters to pursue by colony forming unit (CFU) assays.
08:12
Rapid Optical Clearing for Semi-High-Throughput Analysis of Tumor Spheroids
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Cited by 2 •
2022
Tumor spheroids are becoming increasingly utilized to assess tumor cell-microenvironment interactions and therapy response. The present protocol describes a robust but simple method for semi-high-throughput imaging of 3D tumor spheroids using rapid optical clearing.
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.
10:28
A Semi-High-Throughput Adaptation of the NADH-Coupled ATPase Assay for Screening Small Molecule Inhibitors
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Cited by 22 •
2019
A nicotinamide adenine dinucleotide (NADH)-coupled ATPase assay has been adapted to semihigh throughput screening of small molecule myosin inhibitors. This kinetic assay is run in a 384-well microplate format with total reaction volumes of only 20 µL per well. The platform should be applicable to virtually any ADP producing enzyme.
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.
FAQs
Miniaturization allows more embryos, cells, or treatment conditions to be handled in parallel, while standardized culture conditions make comparisons between samples more consistent. Together, these features help separate treatment-related developmental effects from differences caused by handling or culture variation. The result is a more controlled way to examine multiple experimental conditions without sacrificing the flexibility needed for developmental biology studies.
Semi-high Throughput occupies a practical middle ground: it expands the number of samples or conditions studied simultaneously while preserving substantial experimental control and data quality. Compared with conventional experiments, it supports broader condition testing; compared with fully high-throughput screening, it retains greater flexibility for adapting assays and examining developmental phenotypes in context. This balance supports efficient hypothesis testing.
Automated or semi-automated imaging captures developmental phenotypes across many samples using a more consistent observation process than entirely manual examination. It can support systematic measurement of visible developmental outcomes and help identify reproducible patterns among treatments or experimental conditions. Those patterns can then guide the selection of conditions that merit more detailed mechanistic investigation.
A typical workflow begins by arranging multiple embryos, cells, or treatments in a miniaturized experimental format. Samples are maintained under standardized culture conditions, handled in parallel, and evaluated through automated or semi-automated imaging. Researchers then compare the resulting developmental phenotypes across conditions, identify reproducible responses, and prioritize selected conditions for follow-up experiments with greater mechanistic detail.
This approach is useful when a study must compare several genes, signaling conditions, treatments, or developmental outcomes but still requires meaningful experimental control. It can accelerate early hypothesis testing and reveal which conditions produce consistent phenotypic patterns. Researchers can use those results to narrow a broader experimental space before investing in focused investigations of developmental mechanisms.
By comparing multiple experimental conditions in parallel, the workflow can reveal developmental phenotypes associated with gene function or signaling pathway activity, as well as changes in morphogenesis. It also supports systematic assessment of developmental toxicity across treatments. In each case, the resulting phenotype patterns help researchers identify informative conditions and select candidates for detailed developmental analysis.